A Microfabricated 3D Tissue Engineered "Liver on a Chip": High
Information Content Assays for in vitro Drug Metabolism Studies
by
Anand Sivaraman
Bachelor of Technology (Hons.), Chemical Engineering
Indian Institute of Technology
Kharagpur, India, 1999
Master of Science in Chemical Engineering Practice
MassachusettsInstitute of Technology
Cambridge, MA, June, 2002
Doctor of Philosophy in Chemical Engineering
MassachusettsInstitute of Technology
Cambridge, MA, August, 2004
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Thesis Supervisor, August 2004
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Chairman, Committee for Graduate Students, August 2004
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A Microfabricated 3D Tissue Engineered "Liver on a Chip": High Information
Content Assays for in vitro Drug Metabolism Studies
by
Anand Sivaraman
Submitted to the Department of Chemical Engineering on
August 9th2004, in partial fulfillment of the requirements for the degree
of Doctor of Philosophy in Chemical Engineering
Technical Summary
Recent reports indicate that it takes nearly $800 million dollars and 10-15 years of
development time to bring a drug to market. The pre-clinical stage of the drug development process
includes a panel of screens with in itromodels followed by comprehensive studies in animals to make
quantitative and qualitative predictions of the main pharmacodynamic, pharmacokinetic, and
toxicological properties of the candidate drug. Nearly 90% of the lead candidates identified by current
in vitro screens fail to become drugs. Among lead compounds that progress to Phase I clinical trials,
more than 50% fail due to unforeseen human liver toxicity and bioavailability issues. Clearly, better
methods are needed to predict human responses to drugs. The liver is the most important site of drug
metabolism and a variety of ex vivoand in vitromodel systems have therefore been developed to mimic
key aspects of the in vivo biotransformation pathways of human liver - a pre-requisite for a good,
predictive pharmacologically relevant screen. Drug metabolism or biotransformation in the liver
involves a set of Phase I (or p450 mediated) and Phase II enzyme reactions that affect the overall
therapeutic and toxic profile of a drug. The liver is also a key site of drug toxicity following
biotransformation, a response that is desirable but difficult to mimic in vitro. A major barrier to
predictive liver metabolism and toxicology is the rapid (hours) loss of liver-specific functions in isolated
hepatocytes when maintained under standard in itrom
cell culture condition. This loss of function may be
especially important in predicting toxicology, where the time scale for toxic response may greatly
exceed the time scale for loss of hepatocyte function in culture. Although a wide variety of approaches
to improving hepatocyte function in culture have been described, not all of the important functions specifically the biotransformation functions of the liver - can as yet be replicated at desired in ivolevels,
especially in culture formats amenable to routine use in drug development.
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The in vivo microenvironment of hepatocytes in the liver capillary bed includes signaling
mechanisms mediated by cell-cell and cell-matrix interactions, soluble factors, and mechanical forces.
This thesis focuses on the design, fabrication, modeling and characterization of a microfabricated
bioreactor system that attempts to mimic the in vivo microenvironment by allowing for the three
dimensional morphogenesis of liver tissue under continuous perfusion conditions. A key feature of the
bioreactor that was designed is the distribution of cells into many tiny (-0.001 cm3) tissue units that are
uniformly perfused with culture medium. The total mass of tissue in the system is readily adjusted for
applications requiring only a few thousand cells to those requiring over a million cells by keeping the
microenvironment the same and scaling the total number of tissue units in the reactor. Using a
computational fluid dynamic model in ADINA® and a species conservation mass transfer model in
FEMLAB® , the design of the bioreactor and the fluidic circuit was optimized to mimic physiological
shear stress rates at the tissue fluid interface, while satisfying the tissue oxygen demand.
Using a broad spectrum of gene expression, protein expression and biochemical activity
metrics, the liver tissue phenotype maintained during culture in the 3D bioreactor was seen to be
substantially closer to that of native liver than that of cells maintained in standard cultures. Global
transcriptional profiling was first used to identify genes that are differentially expressed between 2D
collagen sandwich cultures, a variety of standard static 3D cultures, and the 3D perfused bioreactor at
multiple time points up to 20 days in culture, relative to the expression profiles seen in liver in viv. The
biotransformation genes (P450 and Phase II) of the liver were seen to be maintained at close to in ivo
levels in the 3D perfused bioreactor, while a significant downregulation of these genes was seen in the
other standard 2D and 3D static cultures. In order to validate the microarray data, a specific time point
(Day 7 post isolation) was chosen to compare the basal and drug-induced expression levels of several
important CYP450 and Phase II metabolism genes more quantitatively via RT PCR and biochemical
activity assays.
Classes of genes that were found to be differentially expressed between the tissues formed in
the standard 2D collagen sandwich cultures and 3D perfused bioreactor cultures were found to be
preserved at near in ivo levels in the 3D bioreactor even after seven days of culture following cellisolation from in tviv rat liver. In addition, the rates of biochemical activity in seven day old bioreactor
cultures of the proteins that the biotransformation genes code for, as measured by the hydroxylation of
testosterone, were found to be similar to rates measured in vivoas well as in freshly isolated hepatocytes.
On the other hand, in keeping with the significant downregulation of the same genes in 2D cultures,
the biochemical activities of the proteins in 2D collagen sandwich cultures were also seen to be
significantly downregulated relative to in viwo.The ability of the 2D collagen sandwich cultures to
inducers - 3-Methyl Cholanthrene,
respond to prototypical drugs that are known to act as in imvo
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Pregnelone-16a Carbonitrile, and Clofibric Acid - was seen to be sub-physiological unlike that of the
3D bioreactor cultures. Semi-quantitative RT PCR assays were used to compare basal expression levels
of key hepatic nuclear receptors and transcription factors that regulate the induction of the CYP450
genes and other broad programs of liver-specific genes. The better maintenance of the liver specific
transcription factors and nuclear receptors that regulate broad programs of liver function, in the 3D
Bioreactor, may explain the superior basal and functional phenotype seen in the 3D perfused
bioreactor. Some genes were seen to be significantly down-regulated in both the 2D collagen sandwich
as well as well as the 3D bioreactor cultures, and based on current data available in literature, it is
hypothesized that these genes are likely regulated by soluble factors present in the media.
This study has attempted to show the utility of three dimensional tissue constructs as a more
predictive screen for drug metabolism and xenobiotic induction, by looking at a very broad set of
fundamental tissue phenotypic as well as regulatory markers. The study has also identified key
regulatory mechanisms
biotransformation
that may be responsible
for better
retention of hepatic xenobiotic
phenotype in 3D perfused cultures over 2D static cultures. Thus, it may be
concluded that some important liver-specific functions of hepatocytes can be maintained at nearphysiological levels in vitr when isolated cells are cultured in a format that fosters tissue-like structures
to form and allows continuous perfusion and convective distribution of nutrients over length scales
comparable to those in the liver capillary bed. Because the functional unit in the microreactor
comprises -1000 cells, the system can be readily scaled to meet a variety of needs. This thesis thus
provides a foundation for extension of this culture model to applications where near-physiological
levels of liver-specific expression are needed in long-term culture, including assessment of acute and
chronic liver toxicity arising from exposure to drugs or environmental agents; models of disease such as
viral hepatitis infection and cancer metastasis
Thesis Supervisor: Dr.Linda G.Griffith
Title: Professor of Biological Engineering and Mechanical Engineering
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Acknowledgements
I am greatly indebted to my advisor, Linda Griffith, for having given me the complete
freedom and independence to carry out my thesis work. The idea of designing an in vitro3D
liver tissue screen for metabolism and toxicity as part of my thesis, was one that came out of a
visit to Schering-Plough Pharmaceutical Labs in June 2000 - and Linda was instrumental in
having me visit SP and talk to the researchers there on current unmet needs in the field.
To Steve Tannenbaum, my thesis committee member, I owe my deepest gratitude for
many insightful discussions on the chemistry of metabolism of xenobiotics. I also warmly
thank Roger Kamm and Mohammad Kazempur-Mofrad, for invaluable help and support with
the mathematical modeling of the tissue culture system. I am grateful to Klavs Jensen for
serving on my thesis committee, and providing valuable feedback on the effects of mass
transfer on the intrinsic rates of metabolism of drugs. Ron White from Schering-Plough
provided the much needed framework that helped ensure that the thesis was of appropriate
relevance to the pharmaceutical industry.
I must acknowledge several people who helped provide the technical expertise and
assistance for this work. Dan Bauer and Emily Larson, for figuring the science behind the
voodoo primary hepatocyte isolation protocols, Katy Wack and Donna Stolz for those
wonderful electron microscopy pictures, Karel Domansky for help with the fabrication and
design of the silicon scaffolds and the bioreactor, and finally Mark Powers, Carolyn Baker and
Dena Janigian for their patience in teaching a chemical engineer what sterile techniques in
biological experiments were all about.
I have had the opportunity to work with two remarkable undergraduate researchers
in Maritza Rodriguez and Seth Townsend. To Maritza, I owe many thanks for putting in those
long nights with me in assembling and seeding reactors. To Seth, I possibly owe a very
important part of my thesis - his validation of the 18s gene to normalize gene expression data,
laid the foundation for many of the comparative gene expression studies across different
systems.
My sincere thanks to Tomo, Brad and Rebecca, for leading the effort on the
Affymetrix microarray experiments and letting me include my reactor experiments with
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theirs. I wish to thank all of the Griffith and Lauffenburger lab members, past and present, for
making my 5-year sojourn a wonderful learning experience. In particular, I thank Albert, Joe
M., Katry,Nate, Artemis, Brent, Corey, Ajit, Kathryn, Megan, Emily, Tomo, Brad, Shawdee,
Alexandria, Henrik, Dan, Adam, Jane, Steve, Dena and Joe S. for their friendships and for the
wonderful times spent in their company in the BPEC lab and student office.
Thanks are especially due to Brent Schreiber for having worked with me on reviewing
the literature for the section on Liver Structure and Function (Section 1.1) and The in vivo
microenvrmnment
of the liverrevisited(Section1.2) - sections that the reader will find are common to
both his thesis as well as mine.
Finally, special thanks to Kevin - for being the surrogate advisor, the friend,
philosopher and guide. Without his guidance and help, this thesis would not be in its present
form. I will eternally value his friendship and support.
Anand Sivaraman,
MIT, Camb idge, MA
August2004
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Infond memory of my mother ...
the most wonderful human being I have known ...
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Table of Contents
Abstract .................................................................................................................................................... 2 Technical Summary................................................................................................................................- 3 Acknowledgements ................................................................................................................................- 6 -
List of Figures.........................................................
- 12-
List of Tables .........................................................
18
- Chapter 1..........................................................19 Introduction, Background and Motivation ........................................ ..................19 1.1 The Liver: Structure and Function - an engineering analysis .......................................-20 1.2 The in tevomicroenvironment of the liver revisited ........................................................27 1.3 Metabolism of Xenobiotics in the Liver .........................................................
- 29 1.4 The CYP450 biotransformation enzymes ........................................ ..................32 1.5 The Phase II biotransformation enzymes
..................................36 -
1.6The Drug Development Process.........................................................
- 39 -
1.7 Hepatic Tissue Engineering for drug development assays: current status .................- 43 1.8 Hypotheses and Specific Thesis Objectives .........................................................
- 49 Chapter 2.........................................................
51 Design and Fabrication of the 3D Microfabricated Liver Bioreactor and Fluidics ................- 51 2.1 Key facets of the in tvit microenvironment that need to be recapitulated in an in itro
system .............................................................................................................................................- 51 -
2.2 Design principles: biophysics of tissue morphogenesis .................................................
- 53 2.3 Microscopic design parameters...........................................................................................
55 2.4 Photolithography: fabrication of the silicon scaffolds ....................................................57 2.5 Design and fabrication of the polycarbonate bioreactor housing ................................-59 2.6 Assembly of the bioreactor ........................................................
- 61 2.7 Design of fluidic system .......................................................................................................
- 62 2.8 Maintenance of crossflow in the bioreactor ........................................................
- 63 2.9 Summary of key results and conclusions ........................................ .................68 Chapter 3........................................................
69
- Modeling the Fluid Dynamics and Mass Transfer Effects in the 3D Bioreactor ...................- 69 3.1 Computational fluid flow model in ADINA®......................................................... - 70 3.2 Velocity profiles and shear stress distributions ........................................ ................- 74 3.3 Mass transfer model in FEMLAB ........................................................
- 78 3.4 Tissue disposition of oxygen ...............................................................................................
- 82 3.5 Measurement of the effect of crossflow ........................................ .................90 3.6 Summary of key results and conclusions ........................................ ................
- 93 Chapter 4........................................................
95 Basic Characterization of Liver Tissue Phenotype in the Bioreactor . ..............................- 95 4.1 Isolation of primary rat hepatocytes ..................................................................................
- 96 4.2 Formation of multicellular spheroidal aggregates............................................................97 4.3 Bioreactor assembly and cell-seeding.........................................................98 4.4 Evaluation of hepatic phenotype: albumin and urea secretion.................................- 100 -
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4.5 Ultrastructural evaluation of tissue phenotype ............................................................- 102 4.6 Maintenance of p450 isoforms in the 3D bioreactor culture ......................................-105 4.7 Qualitative spectroscopic measurement of p450-1A activity ......................................-107 4.8 Summary of key results and conclusions ............................................................. - 109 -
Chapter 5............................................................
- 110 -
Measurement of Xenobiotic Metabolism in 2D and 3D Cultures ..........................................
- 110 5.1 Challenges in the measurement of intrinsic reaction rates in a 3D system...............- 110 5.2 Paralyne coating of the fluidic circuit.............................................................111 5.3 Minimizing the dead volume in the bioreactor and fluidics........................................-116 5.3.1. Reservoirless fluidic circuit .............................................................-
116 -
5.3.2. Small reservoir, small bore tubing fluidic circuit .......................................................117 5.4 Measurement of xenobiotic metabolism in 2D collagen sandwich (2DCSW) and 3D
Bioreactor (3DB) cultures.........................................................
- 119 5.5 Theoretical validation of the zeroeth order Michaelis-Menten Kinetics at high substrate
concentrations ............................................................125 5.6 Summary of key results and conclusions ............................................................. - 127 -
Chapter 6............................................................
- 129 -
Comparing Basal Biotransformation Capacity of various in vitroSystems .............................-129 6.1 Global gene expression profiling of tissue phenotype .................................................
- 129 6.2 Quantifying relative gene expression: The two step process: Reverse Transcription Polymerase Chain Reaction (RT-PCR) ..........................................................134 6.3 Identification of a well-conserved housekeeping gene for RT-PCR studies............- 138 6.4 Analysis of RT-PCR data: Use of normalized fold change as a metric to compare gene
expression data.........................................................
- 141 6.5 Comparing liver specific gene expression between in vitri cultures using Affymetrix ®
microarray and RT-PCR studies..........................................................145 6.6 Relative expression of liver transcription factors between 2D and 3D cultures .....- 150 6.7 Measurement of testosterone metabolism in 2DCSW and 3DB cultures ................- 151 6.8 Biochemical regulation of tissue function: effect of soluble factors in the media... - 154 6.9 Summary of key results and conclusions ............................................................. - 156 Chapter 7............................................................
- 158 Inducibility as a Quantitative Functional Response Marker .....................................................158 7.1 The cue-signal-response hypothesis.............................................................159 7.2 Inducibility of 2D and 3D cultures using 3MC and clofibric acid .............................-164 166 7.3 Inducibility of 2D and 3D cultures using PCN ............................................................7.4 Key regulatory mechanisms that affect p450 basal expression and induction .........- 169 7.5 Relative inducibility of 2D and 3D cultures - a summary ...........................................171 7.6 Summary of key results and conclusions ............................................................. - 173 C hapter 8.............................................................................................................................................
- 175 Conclusions and Recommendations ...................................................175 References ..................................................-
180
-
Appendices: Protocols and Experimental Methods ...................................................196 ®
Isolation of primary rat hepatocyte enriched fraction using Percoll ..............................197 Isolation of total RNA from isolated hepatocytes, in vivo tissue, 2D and 3D cultures- 198 -
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Primer design procedures and guidelines ........................................ ...............
- 203 cDNA Preparation and Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR)
protocols ......................................................................................................................................
-205 Hepatocyte isolation and Spheroid Formation ........................................................209 Bioreactor Seeding and Maintenance of Culture........................................................ - 210 Measurement of the total number of viable cells in cultures using the measured amount of
total RNA and RT-PCR against the 18s gene........................................................212 Preparation of 2D collagen gel sandwich cultures ........................................................ - 213 Preparation of 3D spheroid and 3D Matrigel spheroid cultures ......................................-214 Induction experiments on 2D collagen sandwich and 3D microreactor cultures .........- 215 Accession numbers for mRNA and complete cds sequences used .................................-216 Primer sequences used in RT-PCR studies ........................................ ................217 Melting and annealing temperatures of designed primers ..................................................219 Microarray Processing ...............................................................................................................221 Testosterone Metabolism studies ............................................................................................222 The Drug Development Process - a detailed overview .....................................................224 -
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List of Figures
Figure 1-1:The functions of the liver ...................................................-
21 -
23 Figure 1-2: The multiple models of liver architecture...................................................................Figure 1-3: Schematic drawing of the structure of the normal liver .........................................24 Figure 1-4: Schematic of the Liver Sinusoid...................................................-
25 -
Figure 1-5: Comparative state of liver non-parenchymal cells in normal vs damaged states - 27 Figure 1-6: The role of p450's in medicine and biology...............................................................33 Figure 1-7: Catalytic cycle of CYP450's...........................................................................................34 Figure 1-8: Relative abundance of the various p450's in human liver .......................................-35 Figure 1-9: Co-factors involved in Phase II biotransformation reactions ................................-38 Figure 1-10:A schematic of the drug discovery and development process .............................-39 Figure 1-11: The main causes of failure of lead candidates ................................................... - 40 Figure 1-12: The growing expenditure on predictive in vitro and in vivmanimal models of
toxicology
icolo.41................................................................................................................................................-
-
Figure 1-13: In vitroand in vivomodels used in the development of new drugs, ranging from
human to isolated enzymes, in order of in vivoresemblance ................................................... - 43 Figure 1-14:Advantages and disadvantages of the various in vitromodels ...............................-45 Figure 1-15:Advantages and disadvantages of the isolated hepatocytes and hepatocyte cultures
as an in vitromodels ...................................................-
46 -
Figure 1-16: Applicability of current in vitro models in various stages of biotransformation
research ....................................................................................................................................................- 49 -
Figure 2-1(a): Important facets of the in vivomicroenvironment that need to be recapitulated in
an in vitrosystem....................................................-
52 -
Figure 2-1(b): Important facets of the in vivo microenvironment that need to be recapitulated in
an in vitrosystem....................................................-
53 -
Figure 2-2: Cell-Cell homotypic interactions that lead to histotypic re-organization over a
defined length and time scale and adhesion guided morphogenesis of pure and mixed cell
54 populations............................................................................................................................................-
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Figure 2-3: The design parameters used in the fabrication of the cell-holding channels in the
silicon scaffold ......................................................................................................................................57 Figure 2-4: Schematic of the DRIE process (left) used to fabricate the scaffolds and photos of
the channel structure and geometries showing resolution. ..........................................................
- 58 Figure 2-5: Silicon scaffolds of different geometries microfabricated by DRIE .....................- 59 Figure 2-6: Changes made to the design of the MilliF reactor, based on feedback from
experiments on MilliII reactors ............................................................-
60 -
Figure 2-7: An expanded view of the scaffold assembly (left) and a schematic of the various
parts of the bioreactor ..........................................................
- 61 -
Figure 2-8: Photograph of an assembled bioreactor ...........................................................
- 62 -
Figure 2-9: Single pump fluidic system used to run culture media through the bioreactor.. - 63 Figure 2-10: Loss in crossflow seen in 3D bioreactors 24-48 hrs. post seeding......................- 64 Figure 2-11: Schematic showing a two-pump fluidic system where the second crossflow pump
pushes fluid from the top of the reactor in through the channels ...............................................64 Figure 2-12: Schematic of the two-pump fluidic circuit after the addition of the inline filters- 65 Figure 2-13: Cell debris seen in the media in the reservoir 1.5 hrs. after reversal of crossflow- 66 Figure 2-14: Cleaning effect of crossflow ........................................
...................
- 67 -
Figure 2-15: Schematic of the optimized two-pump fluidics used with the bioreactor .........- 67 Figure 3-1: Scanning electron micrographs providing appropriate length scales for the idealized
tissue geom etry .....................................................................................................................................72 Figure 3-2: Idealized tissue-channel geometry with a cylindrical conduit for convective flow- 73 Figure 3-3: Mesh distribution in the ADINA® model used to describe fluid flow in the
bioreactor ............................................................-
73 -
Figure 3-4: Uniform flow seen over the top of the channels, over most part of the bioreactor
after a short entrance length................................................................................................................
74 Figure 3-5: Uniform axial flow is .seen to be maintained over most of the channels .............- 75 Figure 3-6: Velocity profile in a single channel of the bioreactor ..............................................
- 76 Figure 3-7: Shear stress distribution along the depth of a channel ............................................76 -
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Figure 3-8: Uniform shear stress distribution seen over the top of the channels in the
77 -
bioreactor ..............................................................-
Figure 3-9: Maximum shear experienced by the cells in a channel as a function of crossflow
rate ..............................................................-
78 -
- 80 Figure 3-10: Interfaces and boundaries in the mass transfer model ..........................................
Figure 3-11: Appropriate non-dimensional parameters used to characterize the system.......- 81 Figure 3-12. Tissue distribution of oxygen in the limiting case of reactors with zero cross-flow
- Scenario 1 ..............................................................-
85 -
Figure 3-13: Tissue distribution of oxygen in bioreactors with cross-flow - Scenario 1......- 86 Figure 3-14. Tissue distribution of oxygen in the limiting case of reactors with zero cross-flow:
Scenario 2:.............................................................-.................................................................................. 88 -
Figure 3-15: Tissue distribution of oxygen in bioreactors with cross-flow - Scenario 2 .......- 89 Figure 3-16: Minimum tissue oxygen concentration asymptotically falls to zero for large values
of the Damkohler number (Da) ........................................................................................................90 Figure 3-17: Effect of perfusion crossflow rates on tissue expression of CYP3A2 ...............-91 Figure 3-18: Expression of Hif-3-ocmRNA in bioreactor cultures operated at different crossflow rates using RT-PCR .............................................................
- 92 -
Figure 3-19: Induction of Heme Oxygenase - 1 mRNA in bioreactor culture under hypoxia:- 93 Figure 4-1: Schematic of the steps involved between the isolation and seeding of rat liver cells
into the bioreactor .............................................................
95
- -
Figure 4-2 A schematic of enriched rat liver hepatocyte isolation procedure ..........................-96 Figure 4-3: Schematic diagram of the bioreactor fluidic system.
...................................-
98 -
100 Figure 4-4: Cleaning effect of crossflow .......................................................................................101 Figure 4-5: Albumin secretion rates in bioreactor cultures ......................................................102 Figure 4-6: Urea secretion rates in 3D bioreactor cultures........................................................103 Figure 4-7: TEM's of tissue phenotype in the bioreactor .........................................................Figure 4-8: SEM's of tissue structures formed in the micro channels of the bioreactor .....- 104 Figure 4-9: Toluidine blue stained sections of the liver tissue from a bioreactor channel..- 105 -
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Figure 4-10: Maintenance of expression of CYP450 2B protein for at least upto two weeks in
bioreactor culture ............................................................-
106 -
Figure 4-11: Repeatability of p45 0 expression in bioreactor cultures ......................................-107 Figure: 4-12: Spectrometric system used for in situ spectroscopy of the 3D perfused tissue- 107 Figure: 4-13: The EROD Assay to measure the activity of P4501A .......................................-108 Figure 4-14: Detection of CYP activity using a fluorescence detector .................................- 109 Figure 5-1: Loss of hydrophobic drug to adsorption, absorption and non-specific binding in
empty reactors with no cells seeded............................................................-
112 -
Figure 5-2: Parylene N was used to passivate the pump tubings .............................................
- 113 Figure 5-3: Steps in the conformal deposition of Parylene N on CFLEX® tubing .............- 114 Figure 5-4: Minimal loss of hydrophobic drug to Parylene N coated tubing ........................-115 Figure 5-5: Minimal loss of hydrophilic products of metabolism due to ...............................-116 Parylene N coated tubing ...................................................................................................................116 Figure 5-6: Reservoir-less reactor fluidics configuration to minimize dead volume ............- 117 Figure 5-7: Components of the two pump, small reservoir fluidic circuit .............................-118 Figure 5-8: Testosterone biotransformation pathway in the rat liver....................................- 119 Figure 5-9: Steps in quantifying the concentration of hydroxylated products of testosterone
metabolism using HPLC-UV ...........................................................-
121 -
Figure 5-10: Specimen standard curve used to calculate the normalized slope of AUC ratio- 124 Figure 5-11: Specimen absorbance spectrum of a hydroxylated product and recorded retention
time .. .........................................................-.............................................................................................
12 5 Figure 5-12: A schematic analysis of the limiting case of high concentration of drug (substrate)
added to the 3D culture ............................................................-
126 -
Figure 5-13: Results for the tissue distribution of testosterone ................................................127 Figure 6-1: Stable expression (though downregulated in many cases) of many of the CYP genes
is seen in the 2D collagen sandwich cultures after seven days in culture .................................-132 Figure 6-2: Log ratios of all culture systems (2D, 3D) versus intact liver tissue were clustered
for all CYP450 genes as represented on the Affymetrix Rat Genome (U73A) arrays...........- 133 -
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Figure 6-3: The various steps involved in an RT-PCR experiment used to quantify relative
134 gene expression....................................................................................................................................Figure 6-4: The three regions seen in the fluorescence-cycle number amplification curve
obtained following RT-PCR ..........................................................-
135 -
Figure 6-5: Fluorescence following binding of the SYBR Green dye to ds-DNA ...............- 135 Figure 6-6: Linear relationship between threshold cycle number and quantity of gene product
for the 18s gene ...................................................................................................................................137 Figure 6-7: Isolated Hepatocytes vs. Total RNA ..........................................................-
139 -
Figure 6-8: 18s gene per cell is invariant across the different culture systems.......................-140 Figure 6-9: Effect of addition of testosterone on the per cell 18s expression levels in 2D
collagen sandwich cultures ..........................................................-
141 -
Figure 6-10: 18s versus b-actin as a normalization gene .........................................................- 145 Figure 6-11: Relative CYP450 gene expression across cultures - from RatU34A microarray- 147 Figure 6-13: Basal expression of hepatic transcription factors in isolated hepatocytes, 2D
collagen sandwich culture (day 7), and 3D microreactor (day 7) cultures expressed as log2-fold
change relative to liver in vivo.............................................................................................................151 Figure 6-14: Downregulation of Bile and Fatty Acid transporters seen in both 2D as well as in
3D reactors ..........................................................................................................................................155 Figure 6-15: Other genes modulated by specific biochemical components in the media ... - 156 Figure 7-1 : Prototypical inducers and nuclear receptors that mediate the induction of some of
the important CYP450's found in rat liver.....................................................................................162 Figure 7-2: Role of nuclear receptors in CYP gene induction. .................................................163 Figure 7-3: Dose response in 2D collagen sandwich cultures: Case of PCN ........................-163 Figure 7-4: Mechanism of induction of 3MC-responsive genes ..............................................
- 164 Figure 7-5: Mechanism of induction of Clofibric Acid-responsive genes ..............................-165 Figure 7-6: Results of prototypical induction studies: 3MC, CLO studies .............................-166 Figure 7-7: Mechanism of induction of PCN responsive genes
....................................- 167 -
Figure 7-8: Results from the PCN Induction studies .......................................................... - 168 Figure 7-9: Basal expression of ligand-binding nuclear receptors ............................................170 -
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Figure 8-1: A one-pass back mix fluidic design for the measurement of one-pass extraction
rates at non-saturable concentrations of the drug....................................-
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List of Tables
Table 1-1: General pathways of xenobiotic biotransformation and their...................................- 31 31 major sub-cellular location.......................................................................................................................Table 3-1. Average viable cell numbers and non-dimensional parameters for modeling the tissue
distribution of oxygen in bioreactors with and without cross-flow - Scenario 1 .........................-84 Table 3-2. Average viable cell numbers and non-dimensional parameters for modeling the tissue
distribution of oxygen in bioreactors with and without cross-flow - Scenario 2 .........................-87 Table 5-1: Products of testosterone hydroxylation and p450's that mediate their formation. - 123 Table 6-1: Global comparison of gene expression profiles across different in vitrocultures at
various tim e points..................................................................................................................................- 131 -
Table 6-2: Higher basal activity of 2C11, 2B1, 2B2 and 3A1, 3A2 seen in the 3D microreactors
153 over the 2D collagen sandwich cultures .............................................................................................-
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Chapter 1
Introduction, Background and Motivation
Recent reports indicate that it takes nearly $800 million dollars and 10-15 years of
development time to bring a drug to market [1]. In itromodels are used to make quantitative
and qualitative predictions of the main pharmacodynamic, pharmacokinetic, and toxicological
properties of the candidate drug, during the pre-clinical screening stage of the drug
development process [2-4]. Nearly 90% of the lead candidates identified by current in vitro
screens, fail to become drugs [5], resulting in an unmet need for systems that are more
predictive of the in vivo metabolism and toxicity profile of the drug. A more predictive in vitro
system can help fail drugs earlier in the drug development process, much before expensive
clinical trials. Drug biotransformation that involves a set of Phase I (or p450 mediated) and
Phase II enzyme reactions [6, 7], can affect the overall therapeutic and toxic profile of a drug.
It occurs in many tissues, with the liver as the most important organ [7]. Thus, a variety of in
ditr model systems have been developed to mimic the in vivo biotransformation pathway in
the liver - a pre-requisite for a good, predictive screen. These include isolated perfused livers,
liver tissue slices, primary cell culture and suspension culture systems, isolated organelles,
membranes or enzymes, and a variety of recombinant systems [2-4, 8]. Each model has its
advantages and disadvantages, and these have been well documented in literature [2-4, 8-10].
One such in itromodel, the primary isolated hepatocytes, rapidly lose liver specific functions
when maintained under standard in vitro cell culture conditions [8, 11, 12]. A variety of
modifications to conventional culture methods have been developed to foster retention of
hepatocyte function, including culture on or in basement membrane gels [8, 13], co-cultures
with other liver-derived or non-liver cell types [14-17], cultures in collagen gel sandwiches [18],
three dimensional culture in spheroids [19, 20], roller bottles [21, 22], addition of exogenous
compounds to the culture medium [23], culture of cells in a variety of bioreactors [24-27]. Still,
not all of the important functions -specifically the biotransformation functions of the liver can
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as yet be replicated at desired levels, prompting continued development of new culture
methods.
Dr. Jim Darnell who won the Lasker award in the year 2002, discovered that "a liver
cell could remain a liver cell only when it stayed in the liver .... without constant signals from
their normal place in the organ, liver cells lost a crucial molecular component ... that helps give
them their identity" [28]. Thus, in essence, it is important that all the important facets of the in
vivomicroenvironment be recreated in any in tr liver tissue culture system to replicate all the
higher order functions of the organ system.
A broad goal of this thesis is to recreate the in vivo3D microenvironment that enables
cell-cell, cell-matrix, soluble and shear mediated signalling mechanisms in an in vitrosystem, in
an attempt to better preserve the biotransformation functions of the liver in long term
cultures.
Such an in vitro system may help address a number of unmet needs in the
pharmaceutical industry. It may find useful applications as a screen for quantitative studies on
the metabolism and toxicity of new chemical entities (NCE's), as well as a model for studying
the pathology of chronic liver diseases and infection by Hepatitis C [29]
This chapter starts with an introduction to the basic biology of the liver, followed by a
primer on the role of the liver as an organ in the biotransformation of xenobiotics - Le. the
ability to clear 'foreign' substances from the body. Following a discussion on current
challenges that face the Pharmaceutical industry - specifically the the drug development
process, the role of hepatic tissue engineering in developing screens for metabolism and
toxicity is briefly discussed. This chapter concludes with a note on the central hypothesis on
which this thesis is based followed by a discussion on specific thesis objectives.
1.1 The Liver: Structure and Function - an engineering analysis
The liver possesses an extremely sophisticated engineering design. It houses a large,
highly structured reactor bed, an intricate flow manifold, and a separation system that
efficiently delivers metabolic products to the blood stream, while shunting bile salts into the
retrograde bile duct. This system's main functions are to remove toxins and provide metabolic
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activity such as cytochrome P450 activity, glycogen storage, urea production, and release of
proteins, carbohydrates, lipids and cholesterol, and metabolic wastes. In addition the liver
serves as a storage vessel for iron (processed from hemoglobin) and copper, fat-soluble
vitamins (A, E, D, and K), and blood, which can be released during hemorrhage. In all, over
500 functions have been identified in the liver many of which are vital to sustain life. Some of
these functions are schematically depicted in Figure 1-1. These disparate functions arise from
the highly intricate cellular arrangement and structure.
11a"W;
/1{
,
'"I
Figure 1-1: The functions of the liver
There are two main competing views of the structural organization of the fundamental
functional unit of the liver - the lobule and the acinus [30]. Both models posses a hexagonal
tissue structure with the portal triads at the vertices and the central vein at the centroid. In
IKieman's proposed lobule model [31] the blood passes into the periphery from the digestive
tract via the portal triad, traverses the sinusoid, and then exits via the central vein (hepatic vein)
(Figure 1-2A, [32]. The portal triad is comprised of three vessels: the hepatic artery bringing
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oxygenated blood from the heart, the portal vein carrying enriched blood from the intestine,
and the bile duct which drains bile from individual bile ducts (Figure 1-2B). These inputs and
outputs branch into complex tree structures, which supply and drain the entire liver.
Rappaport proposed the acinar model in 1954 based on the observation that as blood
passes through the sinusoids, oxygen content, and dissolved solutes are altered at different
positions in the sinusoid by the hepatocytes which have contacted it. Consequently, the cell
types in the liver represent a heterogeneous population of cells whose function differs relative
to the composition of contacted blood. Therefore, the acinus is subdivided into three zones
graded by the depletion of oxygen and other metabolites in adjacent RBC's as they travel the
length of the sinusoid toward the central vein (Figure 1-2C) [33]. Isolating a single sinusoid
shows the fundamental unit of liver structure: a set of thin hepatocyte plates, called the acinus,
strung between the portal triad and the hepatic veinule (Figure 1-2D) [34-36].
These two models of the liver, though seemingly disparate, provide the foundation for
current models. Further research should result in a more definitive model for the architecture
of the basic functional unit of the liver, but for the purposes of this research the liver will be
viewed as a heterogeneous hexagonal tissue, which is a simplification and an incorporation of
both theories.
Additionally the liver has an equally intricate organization on the cellular level that may
be required to be replicated to achieve proper function. The acinus is organized in a perfused,
spongelike, capillary bed structure, composed primarily of mature hepatocyte plates of a single
cell thickness, known as the parenchyma [30]. These plates have an apical domain which
forms bile canalicular networks involved in the secretion of bile components and metabolites
of xenobiotics, and a basal domain which interacts with ECM and participates in cell signaling.
These hepatic plates are lined by fenestrated endothelial cells, which create a physical and
chemical shield between the sinusoid and the hepatic plate. The region between the
endothelium and the hepatic plate, known as the Space of Disse, is traversed by Stellate cells,
the resident liver fibroblasts. Interspersed in the sinusoid are kupffer cells, a specialized form
of macrophage (Figure 1-3A). Fluid flows through two paths: the bulk travels "down" the
acinus from the portal region to the central vein; hepatocytes also form ducts known as bile
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canaliculi that transport bile retrograde or "up" to the bile duct in the portal triad (Figure 13B). These ducts are separated from the rest of the tissue by tight junctions between
neighboring hepatocytes in a similar fashion to that of the digestive system [34-36].
A.
B.
Wnr
C.
D.
Figure 1-2: The multiple models of liver architecture
(A) Liver microarchitecture features hepatocytes around the central vein (B) Liver lobule showing the
portal triad, hepatic sinusoids and blood flow regions (C) Regions of the classical lobule and acinus. (D)
Schematic of liver operational units: Combination of the classical lobule and the acinus
(ImageA isfr m Jaregui et al. 2000, B and Cjfim Klassenet al. 2001, and D fomJunguiera et al. 1999)
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A.
B.
tery
Centralvein-
-Liverplates
Kupffer
cells-
Bile
canaliculus
Endothelialcellsof sinuso
-Fat-storing
cell
-Sinusoidal
capillary
-Fat-storing
cell
-Hering'scanal
in
Inletarteriole
_Inletvenule
Inletvenule-
-Distributing
vein
Hepaticartery
Portalvein-
Bile duct
Distributing
ve
Figure 1-3: Schematic drawing of the structure of the normal liver.
(A) Sinusoidal flow conduits and cellular positioning
(B) Vascularization of one portal triad unit
Images Taken from Junqueira and Carneiro,Basic Histology, a text and atlas,p. 333, Figure 16-1 1.
The normal hepatic sinusoid is formed or lined by four cell types, each with its specific
phenotypic characteristics, functions, and topography (Figure 1-4). These cells may be
considered to represent a functional unit at the border between the hepatic plates and the
blood. They participate in various liver functions and liver pathologies and our knowledge
about this contribution is growing (emerging as a new focus in liver research). The
heterogeneity of these cell-types and possible cooperation between NPC's and the hepatocytes
may add to the overall understanding of liver function.
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A.
B.
~ ~
j
I
_
.. 1:
_r-
,,,
$1museid
-1-*
__
i0
_
_
,
.
,
~~ _.~'
an~
1~i
c
EDO~
Figure 1-4: Schematic of the Liver Sinusoid
(A) Location of NPC's in reference to a capillary of the sinusoid
(B) Detailed architecture of sinusoidal microarchitecture
(Image (A) isfrom the thesis of Tomo Iida, 2003, (B)from Wake et al. 1997)
This section briefly reviews the main function and phenotypic characteristics of
different cell types that constitute a sinusoid:
Hepatocytes
Hepatocytes are highly differentiated epithelial cells that form the cell plates of the liver lobule
and perform the majority of the previously described functions attributed to the liver. In vivo
liver is composed primarily of hepatocytes (-60-65%) [30], that function in detoxification of
systemic and portal blood; secretion of plasma proteins, growth factors, and bile; metabolism
of proteins, steroids, and fat; and storage of vitamins, iron, and glycogen [4, 8].
Endothelial Cells
Endothelial cells (EC's) constitute the closed lining or wall of the capillary (Figure 1-3) and
make up 18-2 3 % of all hepatic cells.
EC's posses small fenestrations to allow the free
diffusion of substances, like 02, but not of particles like chylomicrons, between the blood and
the hepatocyte surface. This filtering effect regulates the fat uptake by the liver. These cells
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also have a pronounced endocytotic capacity, which makes them an important part of the
reticuloendothelial system [37]. They are also active in the secretion of bioactive factors and
extracellular matrix components of the liver. Zonal heterogeneity of the endothelial lining has
recently been reported with regard to its filtering capacity (fenstration) and binding capacity for
lectins and cells [38].
Kupffer Cells
Kupffer cells (KC's) line the sinusoids of the liver and are attached to the endothelial
cells (Figure 1-4) and represent 8-12% of hepatic cells. They are derived from blood
monocytes and are the largest group of fixed cells macrophages in the body [30]. They are
potent mediators of the inflammatory response by the secretion of a variety of bioactive
factors and play an important role in the immune defense. KC's are have a high endocytotic
capacity and are capable of removing particulate matter from the bloodstream. They
phagocytose old cells, foreign particles, tumor cells, bacteria, yeast, viruses, and parasites
(Valatas et al. 2003). The large sizes of the liver and tremendous numbers of kupffer cells make
the sinusoids a very important location for clearance of particulate matter from the plasma.
About one-third of the hearts cardiac output flows through the liver and makes it a key source
of plasma filtration. Kupffer cells are known to be numerically more prominent in the
periportal region [39].
Stellate Cells
The stellate cells (also called Ito cells or lipocytes) lie in the space of Disse, encircling
the sinusoidal endothelium and represent 5-8% of hepatic cells [40] . These cells are the main
hepatic source of extracellular matrix components and are thought to be the body's main site
of Vitamin-A storage [30]. These cells are the only sinusoidal phenotype capable of forming
junctions with hepatocytes [41]. Stellate cells display two phenotypes in normal in vimotissue.
In the resting or 'quiescent' state, they resemble fibroblasts but their cytoplasm contains
numerous droplets in which Vitamin-A is stored. Upon transdifferentiation to the 'activated'
state stellate cells elongate to resemble myocytes (Figure 1-5) and exhibit contractile function
that plays a role in regulation of sinusoidal tone and resistance.
In chronic liver disease the
stellate cells synthesize and secrete collagen into the Space of Disse, leading to "capillarization"
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(fibrosis) of the sinusoids (Figure 1-5). Stellate cells are more numerous in the periportal
region than in the pericentral region of the hepatic acinus. Periportal cells also store higher
amounts of Vitamin-A.
*
A
NT-'.rml
It;.S..
...
HepattLlc
PralI
B rIR~g
I NcPll?
4yofibroblas
I~irit
fal/mgbra
PortalArhnOZ
ulrTr Cl.l
nil..
ijm
/ .iatfix 'lknpavirkma
wr/
llPiltf~r
Prokriting
Myribroblast
t
Im WMrrbraRd
Far
Figure 1-5: Comparative state of liver non-parenchymal cells in normal vs damaged states
Pit Cells
These are known to represent a liver-associated population of large granular lymphocytes.
They have the capacity to kill tumor cells and probably also play a role in the antiviral defense
of the liver. The have been suggested to have a growth-regulatory function in the liver [30].
Like the kupffer cells, they are known to be more abundant in the periportal region of the
sinusoid [42, 43].
1.2 The in vivomicroenvironment of the liver revisited
A major challenge in toxicology and pharmacology research has been to create an in
vivo like primary cell culture analog that maintains liver-specific function and replicates
biological tissue features such as polarity, architecture, and normal bile canaliculi formation,
while maintaining the biotransformation capacity [8, 12]. A variety of culture systems have
demonstrated the retention of original morphological characteristics and the maintenance of
some hepatic function [8, 12, 14, 16, 18, 24, 27, 44, 45], however, none of these systems have
succeeded in replicating the liver associated environmental cues fully and therefore have not
been successful in stabilizing the hepatic phenotype [24, 45-47].
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Sinusoidal cells change phenotype drastically depending on their environment. This
'transdifferentiation' is pronounced enough that it is not clear whether the gene expression
differences between cell types or differences between phenotypic states of one of the cell types
have a greater dynamic range [48, 49]. Thus, even though a particular sinusoidal cell type has a
restricted set of expressible genes, the specific proteins that are expressed within that subset
can change drastically in response to a given environment. Therefore, it is important to
replicate the sinusoid microenvironmental cues within an in vitroculture system.
1) Cell-MatrixInteractons
Hepatic stability and polarity have been shown to be influenced by variations in
composition and topology of extracellular matrix (ECM) [8, 12, 38, 50]. ECM interacts with
cells via binding of intergrins, and other cell surface adhesion receptors and activate a number
of intercellular signaling mechanisms, which enhance homeostasis of cell phenotype [51].
Culture of cells with collagen type I or Matrigel® (biologically derived, basal lamina like
compound) have induced cells to maintain phenotype specific morphology and express liverspecific function longer than previous hepatocyte monolayer cell culture
In vivo,changes in
the microenviroment of the Space of Disse result in activation of stellate cells, deposition of
fibronectin and production of cytokine activating agents by endothelial cells, and increased
cytokine secretion and proliferation of Kupffer cells [52]. These results demonstrate that the
presence of cell-matrix interactions is essential to the homeostasis of an in itroliver analog.
2) SolubleSinals
Hepatic cells communicate via soluble signaling mechanisms following injury to induce
the activation and termination of tissue regeneration [39]. Thus it is apparent that any in vitro
system that aims for spontaneous regeneration or reconfiguration of sinusoidal structure must
contain the necessary soluble signals. Though the exact mechanisms of these interactions has
not been established, strong evidence exists that non-parenchymal cells play a critical role in
the release and regulation of these soluble signals[53]. Because the composition and time
dependence of cell signaling proteins during liver regeneration are unknown, supplementing
media with the proteins seems more like a fishing expedition than a science, and a more
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natural approach is simply addition of physiological ratios of NPC's to in vitroco-cultures.
However, for this to be done it is important that the various cell types from the liver by
separable into their individual types, and fixed ratios (reminiscent
of thoseseenin viv) of the various
cell types then be added back into culture. A possible approach to do this is discussed
elsewhere [54].
3) Cell-Cell Interactions
Cell-cell interactions, both homotypic and heterotypic have been shown to improve
viability and function of in vitro hepatic cultures [43]. These cell-cell interactions consist of
gap junctions, desmosomes, E-cadherins and tight junctions, who trigger a number of
intercellular signaling mechanisms, which enhance homeostasis of cell phenotype [51].
Heterotypic interactions in hepatocyte-nonparenchymal cell co-cultures are thought to present
a highly conserved
signal that greatly
augments
liver specific functions
[55].
Specific
mechanisms that stabilize hepatocyte function have not yet been elucidated at the molecular
level but homotypic and heterotypic interactions are thought to be imperative to the function
and stabilization of the 'normal' liver phenotype [43, 45, 56].
1.3 Metabolism of Xenobiotics in the Liver
All organisms are exposed constantly and unavoidably to foreign chemicals, or
xenobiotics, which include both man-made as well as natural chemicals such as drugs,
industrial chemicals, pesticides, pollutants, toxins produced by molds, pyrolisis products in
cooked food etc. The lipophilicity of the xenobiotics that enables them to be absorbed
through the skin, lungs or gastrointestinal tract, is an obstacle to their elimination as lipophilic
compounds can be easily re-absorbed. Consequently, the elimination of xenobiotics often
depends
on
their conversion
to water-soluble chemicals by a process known
as
biotransformation, which is catalyzed by enzymes present in the liver and other tissues.
Biotransformation helps convert lipophilic compounds to a more hydrophilic form that
enables their removal through feces or urine. Biotransformation helps speed up the clearance
of toxic lipophilic substrates from the body. For e.g. the biotransformed form of hexobarabital
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is known to have a half life of 5-6 hours, while its lipophilic parent drug has a half life of nearly
2-5 months [57]
Xenobiotics exert a variety of influences on biological systems, depending on theIR
physicochemical property. This is of particular relevance to both pharmacology and
toxicology. For e.g., in some cases, chemical modifications caused by the biotransformation of
a drug may be responsible for the pharmacodynamic effect (or lack thereof) of the chemically
modified drug, relative to the parent drug. In other cases, the modification may be responsible
for the production of intermediates or final products that may or may not have a characteristic
toxic effect or tumorigenesis effect that is possibly unseen in the parent drug. In most cases,
however, biotransformation terminates the pharmacological effects of a drug and lessens the
toxicity of xenobiotics. Enzymes catalyzing biotransformation reactions often determine the
intensity and duration of action of drugs and play a key role in chemical toxicity and chemical
tumorigenesis [57]. Xenobiotic biotransformation can thus be thought of as the principal
mechanism for maintaining homeostasis during exposure of molecules to small foreign
molecules, such as drugs.
The terms biotransformation
and metabolism are used almost synonymously,
particularly in the context of drugs, though metabolism includes absorption, distribution,
biotransformation
and
clearance -
and
is hence
much
broader
in scope
than
biotransformation. The reactions characterized by biotransformation enzymes are generally
divided into two groups, called Phase I and Phase II, as shown in Table 1-1 (From [571)
Phase I reactions involve hydrolysis, reduction and oxidation. These reactions expose
or introduce a functional group (-OH, -NH 2, -SH, or -COOH), and usually result only in a
small increase in hydrophilicity. Phase II reactions involve acetylation, glucoronidation,
sulfation, methylation, conjugation with glutathione and amino acids. The co-factor for these
reactions with functional groups that are either present on the xenobiotic or are introduced/
exposed during Phase I biotransformation. Most Phase II reactions result in a large increase in
hydrophilicity. Phase I and Phase reactions may occur independently or sequentially one after
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the other. Xenobioiic biotransformation enzymes are widely distributed throughut the body,
and are present in several subcellular compartments. In vertebrates, liver is the richest source
of such enzymes.
REACTION
ENZYME
LOCATION
Phase I
Hyvdrolysis
Carboxylesterase Pep tidase
Microsomnes,cvtosol, blood, lvsosomes
Epoxide Hydroxylase
M/Ncrosomes,cytososl
Azo-and nitro-reduct ion
Reducion
(:arboml reduction
Cvtosol
Disulfide resuction
Cvtosol
Sulfoxide reduction
Cvrosol
Quinonc reduction
Cvtosol: mcrosomes
Reductive dehalogen:ation
IMicrosomes
AMcoholdehydrogena ase
Aldehvde
O.xidadtion
l.icroflora, microsomes, cvtosol
dehydrogei
Cytosol
nase
Mkitochondia, cytosol
Aldehyde oxidase
Cytosol
Xanthine oxidase
Cvtosol
Monoamine oxidase
Cvtosol
Diainne oxidase
Cvtosol
I-Hsynflthase
lficrosomes
Flavi-mniono-oxygcnases
Ihlcrosomes
Prostaglandin
Cytochrome P450
Mficrosomes
Phase II
Glucoronide conjuga .tion
Microsomes
Sulfate conjugation
Cvtoso
Glutathione conjugal ion
Cytosol, mnicrosomes
Aninmo acid conjugat ion
hfitichondria, microsomes
Acvladon
Mitochondria, cvtosol
Methd4ation
Cvtosol
Table 1-1: General pathways of xenobiotic biotransformation and their
major sub-cellular location
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Within the liver, they are primarily located in the endoplasmic reticululm (microsomes)
or the soluble fractions of the cytoplasm (cytosol) of the hepatocytes, with lesser amounts in
the mitichondria, nuclei and lsosomes. (Table 1-1)
The presence of the biotransformation enzymes in the endoplasmic reticulum (ER) in
the liver is possibly due to presence of the lipid bilayer in the ER. There is huge difference in
the ability of different tissue to biotransfrom xenobiotics. This has huge toxicological
implications, in terms of tissue-specific chemical injury. Several xenobiotics, including
acetaminophen and carbon tetrachloride, are hepatotoxic due to their activation to reactive
metabolites in the liver. [58]. Cells within the liver - depending on their location in the lobule
also show considerable differences in their biotransformation of xenobiotics. For e.g. the cells
in the centilobilar region are the ones that are responsible for the transformation of carbon
tetrachlotride to their toxic intermediates. There are considerable differences in the distribution
of the various biotransformation enzymes even with the same organ, across different species.
[59].
The focus of this thesis will be on the biotransformation pathways mediated by the
enzymes in the liver - specifically the Phase I CYP450's and some of the Phase II enzymes, as
these have been known to be one of the most difficult to maintain in standard cultures in itro.
1.4 The CYP450 biotransformation enzymes
Among the Phase I biotransformation enzymes, the CYP450 system ranks first in
terms of catalytic versatility and the sheer number of xenobiotics it detoxifies or activates to
reactive intermediates [60, 61]. The highest concentration of p4 50 enzymes is present in the
endoplasmic reticulum in the liver, though they are also present in many other tissues. The
p450's play a key role in regulating the intensity and duration of action of drugs, and they are
also responsible for the detoxification of xenobiotics. They, play a key role in the biosynthesis
or catabolism of steroid hormones, bile acids, fat soluble vitamins, fatty acids, and eicosanoids,
and this underscores the catalytic versatility of the p45 0 's. Figure 1-6 details the key role of
p450's in biology and medicine.
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All p450's are heme-containing proteins. The heme iron is usually in the Ferric (Fe 3)
state. When reduced to the Ferrous state (Fe?2),the p450 can bind ligands such as oxygen and
carbon monoxide. The resulting complex that forms absorbs light maximally at 450 nm.
poiymorphisms
drug
desig
antibiotics
mutagen activation
therapy design
drug interactions
promoters chemotherapeutics
CARCINOGENESIS
REACTIVE OXYGEN
PHARMACOLOGY
endocrine disruptors
susceptibility
growth actors
V
ENVIRONMENTAL
pesticides
TOXICOLOGY -
~
-
P450
retrnoids
-
BIOTECHNOLOGY
biomarkers
OLOGY
selective biocides
\
ENDOCRINOLOGY
phytosteroids
adrenal seroids
OLO
HOMEOSTASIS
androgens
/ | ecdysons\
estrogens
REGULATION
ENZYMES
pollutants
natural products
neurosteroids
juvenile hormone
GENE
neurostroids
Yflower
-- \ENDOCAIN
\ \ \N
faty
vitaminD\
progesterones
ketones
color
bile
acids
acids
/
eicosancids
Figure 1-6: The role of p450's in medicine and biology
The basic reaction catalyzed by the p450's is mono-oxgenation
in which one atom of
oxygen is incorporated into a substrate, designated RH, and the other is reduced to water, with
reducing equivalents derived from NADPH as shown in Figure 1-7.
Although CP450
functions as a mono-oxvgenase, the products are not limited to
alcohols and phenols due to re-arrangement reactions [62]. During catalysis, CP450
binds
directly to the substrate and molecular oxygen, but it does not directly interact with NADH or
NADPH. The mechanism by with the p 4 50 receives electrons from the ND(P)H depends on
the cellular location of the p450. In the endoplasmic reticulum in the liver, where most of the
drug biotransformation p450's are located, the electrons are relaved from NADPH to the p4 5 0
via a flavoprotein
(Figure
1-7) called NADPH-cvtochrome
P450 reductase.
flavoprotein electrons are transferred from the NADPH to the CP450
Initt fCehoov3
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Massachsetts Insitute of'Iechnolop,·
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itehooy-EgneigCnc
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In the mitochondria, that house the p450's involved in steroid hormone metabolism
biosynthesis and vitamin D metabolism, electrons are transferred via twvoproteins - an iron:
sulfur protein called ferrodoxin and and an FMN containing flavoprotein called ferrodoxin
reductase. Phospholipids
b5 also play an important
and cytochrome
role in the
biotransformation function of CYP450 [57]
Druq
Oxdzed
NHADP'
H0O
NADPH
02
Figure 1-7: Catalytic cycle of CYP450's
The catalytic action of p450's (Figure 1-7) involves, in the first part, the activation of
oxygen, and in the final part, substrate oxidation - i.e. the abstraction of a hydrogen atom or
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an electron from the substrate followed by oxygen rebound (also called radical recombination).
Following the binding of the substrate to the p450 enzyme, the heme iron is reduced from the
ferric to the ferrous form, by the addition of a single electron from the NADPH-cytochrome
p 4 50 reductase. Oxygen binds to the p450 in its ferrous state and the FE 2 O, complex is
converted to the Fe2-OOH complex by the addition of a proton (H) and a second electron
from the reductase or the cvtochrome b5. This cleaves the ferrous complex to produce water
and an (FeO)'3 complex, which transfers its oxygen atom to the substrate. Release of the
oxidized substrate returns the CYP450 back to its initial state. CYP450 catalyzes many tpes of
oxidation reactions including hvdroxylation of an aliphatic or aromatic carbon, epoxidation of
a double bond, heteroatom (S-, N-, [-) oxygenation and N-hydroxylation, heteroatom (O-, S-,
N-, Si-) Dealkylation, oxidative group transfer, cleavage of esters and dehvdrogenation.
Detailed reviews on the chemistry of these reactions are available in literature [57, 58, 60, 62,
631.
The relative abundance of the various CYP450's in the human liver (there are more
than 400 different p450's that have been identified, and more than 59 pseudogenes), is shown
in Figure 1-8.
Figure 1-8: Relative abundance of the various p450's in human liver
Studies on the biotransformation of new chemical entities by p450's leading to their
activation/ inactivation, are one of a series of studies undertaken by the Pharmaceutical
-gncigCne
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industry during the drug development process [1, 64, 651. In addition, information on which
p450 metabolizes a drug can help predict or explain drug interactions [66]. For example, when
administered with azole antifungals (e.g. ketaconazole) or macrolide antibiotics (e.g.
ervtlhromycin), the antihistamine terfenadine (Seldane) can cause torsadesde pointes,which in
some individuals has apparently led to lethal ventricular arrythmias [66]. This drug interaction
can be rationalized on the basis that terfenadine is normally converted by intestinal and liver
CYP3A4 in humans to a tertiary-butvl alcohol, which is further oxidized to a carboxylic acid
metabolite. This latter metabolite does not cross the blood-brain barrier and terfenadine thus is
a non-sedating antihistamine. When formation of the carboxylic acid is prevented by
ketaconazole etc (CY'1P3A4inhibitors), the plasma levels of the parent drug terfenadine
become sufficiently elevated to block cardiac potassium channels, leading to arrvthmias [66].
Thus competitive and non-competitive inhibition of CYP450's is an important screen for new
chemical entities in the drug development process.
In contrast to inhibitors, inducers of p 4 50 increase the rate of xenobiotic
biotransformation [67-70]. Some of the p450's in the liver microsomes are inducible. Clinically
important consequences of p45 0 induction include the enhanced biotransformation of
cyclosporine, warfarin and contraceptive steroids by the inducers of CkT2C and CYP3A
enzymes and enhanced activation of acetaminophen to its hepatocyte metabolite N-acetelvlbenzoquinoneimine, by the CYP2E1 and 3A enzyme inducers. Thus, in addition to CYP450
inhibition, CYP1450induction of new chemical entities is also screened for during the drug
development process.
In addition, mechanistic studies that determine possible drug-drug interactions, drug
induced toxicity etc., involving a new chemical entity, is an essential part of the drug discovery
process.
1.5 The Phase II biotransformation enzymes
Phase II biotransformation reactions include glucoronidation, sulfation, acetylation,
methylation, conjugation with glutathione (mercapturic acid synthesis), and conjugation with
36-36
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... ...
amino acids such as glycine, taurine and glutamic acid [71]. The co-factors for these reactions
are shown in Figure 1-9. They react with functional groups that are either present on the
xenobiotic or are introduced/ exposed during Phase I biotransformation. With the exception
of methylation and acetylation, Phase II biotransformation reactions result in a large increase
in the hydrophilicity of the parent compound. Glucornidation, sulfation, acetylation and
methylation involve reactions with activated or high energy co-factors, whereas conjugation
with amino acids or glutathione involves reactions with activated xenobiotics. Most Phase II
enzymed are located in the cytosol, a notable exception is the UDP-glucuronosyltransferases,
which are microsomal enzymes (Table 1-1). Phase II reactions usually proceed much faster
than Phase I reactions, such as those catalyzed by CYP450 [57]. Therefore the rate of
elimination of xenobiotics whose excretion depends on the rate of excretion depends on both
Phase I as well as Phase II biotransformation reactions, is generally determined by the first
one.
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[ I.I
...
z
...
L.L~
011
O
IlO
I
^-.u -------
I O·-I-
_ I
ounauo n (ouununj
"
0-0-P-O-CH
II
1
/
o
3 ;Phosphoadenohne5'-phomphoulaW (PAPS)
UrWdntw5'-dphoepbO-D-gIuJcronicdcid(UOP-GA)
I~~r...
Meoyllaton
1
r.
OH
H2N
WOC",H
I
O
CH-(CH,--S-C,
H2
V
1OO
~~~~
Acerylcoem2yme A
S-AdenoeylmethlonlneSAM)
Amino Acid Conjugaion I
Glutathlone Conjugation I
I
CH
CG co
SH,
% V
rglulImC aci
. %
cyMYlm
*
gIycwE
r,lv3
I
Glulthlos
Taurine
Glutmhee
Figure 1-9: Co-factors involved in Phase II biotransformation reactions
(fim [57])
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1.6 The Drug Development Process
Discovering and bringing one new drug to the market tpically costs a pharmaceutical
or biotechnology
company nearly $800 million and takes an average of 10 to 15 years [1, 72].
Figure 1-10 is a schematic of the various steps involved in the drug discovery and development
process.
4·
,$,;
"...
-IIllJII
If
u'.r
Id,
,,
Figure 1-10: A schematic of the drug discovery and development process
New drugs begin in the laboratory with chemists, scientists, and pharmacologists who
identify cellular and genetic factors that play a role in specific diseases. This leads to a search
for chemical and biological substances that target these biological markers and are likely to
have drug-like effects. It is estimated that out of every 5,()00 new compounds identified during
the discovery process only five are considered safe for testing in human volunteers after
preclinical evaluations [1, 72]. After 3 to 6 years of further clinical testing in patients, only one
of these compounds is ultimately approved as a marketed drug for treatment. There is thus a
high attrition rate of compounds as they step through each step of he drug discovery and
development process, beginning with the demonstration of efficacy in experimental cell and
animal models and concluding with the demonstration of safety and efficacy in humans.
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Drugs can fail at any point during the process - and failure at later times is inevitably
more costly. Nearly 90% of the lead candidates identified by current in vitro screens fail to
become drugs [5] ('igure 1-11). Many of the lead candidates fail due to unacceptable toxicity in
one or more animal species. Among lead compounds that progress to Phase 1 clinical trials, a
significant number fail due to unforeseen
human liver toxicity [73] (Figure 1-11). This high
rate of failure is inspite of an investment of more than 130 million dollars on predictive
ADMET (absorption, distribution, metabolism, elimination and toxicity) screens, per $800
million dollars spent on developing a new drug. The projected annual five year growth rate in
O-15°0o(rotre. PhRibLA) (Figure 1-12)
expenditure on toxicology screens and assays is nearly 10%
Why Lead Candidates Fail
Market/Business
Lack of Effica
oxic ity
31%
22%
Poor ADME
41%
90% of Lead candidates fail to become drugs
Figure 1-11:The main causes of failure of lead candidates
Nearlv 90% of the lead candidates entering Phase 1, fail due to liver toxicity [5]
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In-vi
$1.30
ADME
Total Toxicology:
$1.5 bn
$1.50 bn
In-vitro
In-vitro
$0.20 bn
Surces: PhRNlABCC
2000/2001
$3.0 billion
ir Growth
Total
10% - 15%
In-vitro >25%
Figure 1-12: The growing expenditure on predictive in itro and in r'oanimalmodels of
toxicology
Thus, it is clear that there is an unmet need for a more predictive, cost effective screen
of drug metabolism and toxicity in the liver. Indeed, there is considerable savings that can be
had by failing chemical entities earlier in the drug discovery process - also called the fil early,
/ail cheap
paradigm.In addition, minimizing false negatives results in a significant opportunity'
cost for drugs that may have otherwise not gone to market.
By contributing to the understanding of a toxicity and providing perspective,
investigational toxicology programs can enhance the discovery and deveklopment process.
Investigational toxicology efforts can provide information regarding toxic mechanisms of a
drug, and models for avoiding repeated toxicity problems. For toxicity revealed during preclinical development, determining the mechanism and providing an investigational model can
contribute to the discovery or design of a less toxic analog. Alternatively, mechanistic studies
can place the observed toxicity in perspective with regard to human risk. In mechanistic
problem solvhing,it is important to select appropriate investigational models. While in i2wo
models are needed to investigate drug effects in the context of toxicokinetics and systemic
influences, in itr) models can be efficient and cost effective tools for investigating specific
mechanisms in a precisely controlled environment. This is particularly so in the pre-clinical trial
phase of the drug discovery and development process when there are far too many
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compounds to be able to do animal testing, for pharmacology and toxicity assessment. The
development of in itro model systems to evaluate the toxicity of chemicals and drugs has thus
become increasingly important. These in itro models may also enhance our understanding of
the mechanisms of drug and chemical induced toxicity, because in vivomodels are complicated
by the presence of structural and functional heterogeneity and do not allow for mechanisms to
be clearly defined or reproducibly examined. Additionally, in itro systems enjoy increasing
popularity from the public because their application in toxicity testing may allow a decrease in
the number of animals used in biological testing.
In vitro models are usually target organ based and the most frequently encountered
target organ toxicity is the liver. There are a number of in itro liver models such as perfused
livers, primary isolated hepatocytes, liver slices, supersomes, microsomes, and transgenic cell
lines - currently being used as predictive screens as well as mechanistic models of drug
metabolism and toxicity (i.e as ADMET screens). However, its is pretty clear from the
numbers in Figures 1-11 and 1-12, there has been little success in striking a balance between
making the system easy to use and ensuring that the system recapitulates the most important
functions of the in ri'o liver. Not all of the important functions can as yet be replicated at
desired levels in in itro models, especially in culture formats amenable to routine use in drug
development.
In summary, there is need for an in litro liver model that is 'sufficiently' complex as to
be able to replicate the liver pathology and biotransformation functions and thus fail toxic
compounds early, while being amenable to use in a high throughput format. At the same time,
the system must be precisely controllable and reproducible, to allow for its use in
investigational toxicology, toxicokinetics and mechanistic xenobiotic biotransformation
experiments.
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1.7 Hepatic Tissue Engineering for drug development assays: current status
The type of in-vitromodel used to perform drug metabolism studies is a compromise
between convenience and relevance [2, 4, 8, 65]. Figure 1-13, charts the various in vitroand in
viv models used in the development of new drugs.
Figure 1-13: In vitroand in vivomodels used in the development of new drugs, ranging from
human to isolated enzymes, in order of in vim resemblance
Taken from reference[3]
Drug metabolism has been investigated previously in increasingly purified liver
fractions culminating in single enzyme systems. In addition perfusion of the whole liver in-situ
has been employed. The former reductionist approach suffers from lack of relevance to the invivosituation and the latter, while as close as possible to the liver in-vimo,suffers from complex
methodology and limited multiple usage. Thus isolated hepatocytes and cultured hepatocytes
may provide a convenient link between sub-cellular fractions and the complex architecture of
the intact organ, while encouraging multiple experiments on the same batch of cells [8, 12, 65].
Thus in order to study liver function in a controlled environment, isolation and maintenance
of viable hepatocytes is desirable. The use of an inherently simple system while maintaining the
intact cellular architecture allows for accurate control of, for example, endobiotic/xenobiotic
interactions, drug-dosing regimens, and the role of competing pathways in drug metabolism.
Furthermore, a critical assessment of cellular function and dysfunction can be monitored in
the presence of drug, inhibitor and co-factors. The models currently in use for human drug
metabolism studies include [3]
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1. Supersomes
2. Microsomes and cytosol
3. S9 fraction
4. transgenic cell lines
5. Primary hepatocytes and cultured primary hepatocytes
6. Liver slices
7. Perfused liver
8. In vivo animal model
9. Human
Figure 1-14 summarizes the advantages and disadvantages of the various culture
models. A detailed review of the advantages, disadvantages and future applications for each of
the models listed above is available in literature [2, 3].
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-
In it,, tcimique
H-nms CYP ad- U
Advaupa
I sit-
Disadva
upS_
-
Osn isoyi
Difflclt
jurpaset
Mxnpolam to HL2Mand m vivo
Difflnmt goyple
Highenzym activitis
Humantivr macrtoms
Unsuitable fr quantitative mersum
Study
of
individual.
gander-, and species-
s
Only CYP and UGTenzyes
specfc bionnfomtiaa
Human li
cytosol
NAT. Sr, and 0ST activ- Only NAT,ST; ad GSr
ity depends on coafcts
High
Study
zyuM activies
gd'er-,
of
i'diduml
and
speci-
spcific biotmnsfamtion
Human li
S9 faction
Huma liv all
a li
Thnspnic ca lines
Bol pas
Low esqassima levels
tIomnplre repmsntatia ef in vivo situation
Only a lfw is eym
expamu
Study of om isaym
or a
af CYRP
Well etablibhed and cha-
Isdationcm be conplicatd andtm coming
Study f mdiaton ad c-
Only plSelocted cells can be studied
zymc indatsn possible
Ding
tanspopmn
still
pmsan and opetionel
hntac cellulr
tractiom
Moqtolgica
studies pos-
Liwrslics
Cll duUs du gisdOimoD
Indequate pnettima
Damaed calls on de edges
sibk
Imendiv
iduall
viafim
itmitd viable period
canbe studied
cExpesiveapquip
]solates perusd
liver
t
Bie formatom
Thro.didnsioal
Limited viable peiod
Po paduibility
No human lier availabe
Figure 1-14: Advantages and disadvantages of the various in itromodels
Takenfirm refrences[2, 3]
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and cytosl
Relatively stable enzym
expression levels
CYPs itducible
Easy to culbs
Hiihsr
ssioD levels
cambin
Primy heptocylm
Lwr amzyme activit than in misaoms
I and I
Study
of
individal
gader-, and specksspecificbiotbmnsfction
Easy to culum
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Thus from Figures 1-13 and 1-14, its is apparent that primary hepatocytes may serve to
be a useful model for a number of specific biotransformation studies, provided the hepatic
function, specifically the biotransformation functions of the isolated hepatocytes or hepatocyte
cultures are preserved in itro.Figure 1-15 summarizes the advantages and disadvantages of the
use of primary hepatocytes or hepatocyte cultures are in vitr models.
Advntages
Wel established
Well chmnclrized
Virity fiir up to 4 weeks
Study of madins andenzyme induces possible
Disadvanulh
Isolation ca be complicald andtimec...
Vible cellenichmnt possible
Cryopeaesvatii possible
Drmu tnrorers
still
pesent andoprational
Cellular nuctims
Only pmselctd
cels can be studied
Call damage during isolation
more difficult to study
Figure 1-15:Advantages and disadvantages of the isolated hepatocytes and hepatocyte
cultures as an in vitromodels
Taken from references[2, 3]
Although primary hepatocytes maintained under conventional culture conditions have
been used broadly for short-term studies on drug metabolism and hepatotoxicity, the rapid
loss of many liver specific functions, the failure to re-establish normal cell polarity and
architecture, including bile canaliculi and the deterioration of cell viability within several days
have limited their application for long term studies [74, 75]. Historically, three major
complications have confounded the use of cultured hepatocytes for the study of drug
transport and metabolism, as well as expression of liver specific genes. First, there is variable
attachment and rapid functional deterioration of hepatocyte cultures maintained on plastic
culture dishes. The second problem involves supplying the culture with adequate nutrients to
carry out the large number and variety of cellular functions conducted by hepatocytes in-situ
(e.g. synthesis of serum proteins). The third problem stems from an incomplete understanding
of the hormonal requirements of the cultured hepatocytes. Whether due to one or a number
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of these factors, primary cultures of rat hepatocytes inevitably show a complete loss of greatly
diminished activity in liver specific protein synthesis [76, 77], xenobiotic-metabolizing activity
[78], cytochrome p450 induction [79] and Na+ linked bile acid transport [8] within the first 2448 hours when maintained under conventional culture conditions. Further, the failure of the
cultured hepatocytes to re-establish normal bile canalicular networks (cell polarity) has also
limited their application for examining the regulation of biliary secretion and mechanisms of
choleostasis [8]. These restrictions have been a major obstacle for performing studies
pertaining to the metabolism of drugs in as much as cytochrome p450 mediated metabolism
frequently determines the toxicity and mutagenicity of many natural and synthetic chemicals.
In addition the loss of cytochrome p450 and other drug metabolism enzymes limits the kinds
of studies that can be performed with the primary hepatocyte cell culture models, and raises
uncertainties about the relevance of findings in cultured cells to the intact liver.
Initial attempts at culturing rat hepatocytes were done using simple or conventional
culture conditions: seeding individual cells in static medium in dishes coated with extracellular
matrix proteins such as collagen I or matrigel. Some methods involved re-suspending cells in
physiological medium containing serum and growth factors and plating them onto rigid
substratum such as plastic or protein coated dishes. Under these conditions, hepatocytes
spread extensively, assume a flattened morphology and eventually form a confluent monolayer.
Rubin et al [8] demonstrated that hepatocytes attach more readily to a substratum of gelled
collagen I than to a rigid substratum of the same material. Many other studies have been
performed on different culture systems including the immensely researched 2D sandwich
configuration wherein hepatocytes are cultured in-between two successive layers of ECM [18,
24, 50, 80]. Lecluyse & co-workers have extensively researched the possibility of growing
hepatocytes in matrigel [80] while Wu and co-workers have used suspension cultures to form
spheroids [20]. These cultures have been shown to maintain hepatic function (serum protein
secretion and p450 induction) for extended culture periods. However, spheroidal cultures
differ structurally from what is seen in-vio. The lack of perfused flow through the spheroidal
aggregates prevents nutrient transport to the cells in the interior of the aggregate, especially in
case of spheroids whose sizes exceed diffusion length scales. A detailed review of the various
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in vitro primary cultured hepatocyte models and their drawback are discussed in detail in
literature [4, 8, 65]. In general, none of the current in vitrocell culture models have been able to
replicate all the pharmaceutically relevant functions of the liver [80].
In any culture system it is necessary that an environment be provided which ensures
long-term maintenance of hepatic function and cell viability. Adequate nutrient and oxygen
transport is critical in the maintenance of cell viability over extended days in culture. At the
same time, it is important that many of the facets of the in ivomicroenvironmental cues - cellcell homotypic and heterotypic interactions, soluble and shear mediated signalling, and
adhesion guided cell-matrix signalling, be provided to the primary hepatocytes in culture to
replicate in ivo like function.
At the same time, it needs to be borne in mind that all of the in vitromodels discussed
in Figure 1-14, may have very specific uses for a defined set of biotransformation experiments.
An excellent review by Brandon et al.[3], summarizes possible uses for some of the standard in
ito models. This is reproduced in Figure 1-16. Indeed, as suggested in the figure, stable, iin im
like cultured primary hepatocyte models may be poised to be a very useful screen for testing
the toxicity of a drug and its metabolites.
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Drugrsecb
Inviu modl
Axgumcuts
Drmgbiransoumiaion
(1) Pooled microsoms ad
(1) Combination ezyrns in one model. CYPs and
UGTs in micrusana and soluble phase 1 enzymes in
cytosol
(enr-speific
fiaicuos can be used)
(2) Supmomes
and NAT
(4) (rmgargic) cell li
-rimary
hepatocyes
(5) lver sties
Isolation
mtabolis
ptsme
(3) Combiation of phase I and I ezymes
(4) intact ci to study
(3) Human liver S9 frcia
and
(2) One spcifi isozy
(6) Ntfmed anim liver
(1) CYP or UGT supera NA cytosol
or
sa
(2) Milrosomes or cytoal
(5) Intact ivhNrstu
(6) Only for bile cmretion
(1) Mctabolim of a specific isoyme
ld)
(hiher
(2) Only whe isoeazynt is not psent
as isodaed en-
zym
Drug-drug intracticm
IIbi.cntfntia
(3) Only wbho m
(3) S9 fIacti
(1) Microsomes or cytsol
C-) CYP or UG
super-
aetbdiis canbintim
(1) Combinaftionof
(2)
of pse
I and
zynms in ono modl
at aom specific isoyme
diton
sam ar NAT cytod
Influen
of
polyme-
(3) PRimay hbetocyles
(3) ntact cell
(1) upsnoms
(1) CYP p
(2) Micnuoines or cyteac
(2) Prom one paent that shows that polymor-
(3) Pimary hqepatocys
(1) Pimayhepaocyes
(3) Fn om patient that shows that polynmphism
(1) htact cell and cytetacicity is nmasurable
C) Clhmannic) Ci
(2) Influe
phims
odpbism in supenomes available
phism
Toxicity of drug and its
mtabbdhtes
cytotaxi
of on or combinaion f enzymes
can be studied
(3) inact liw suctme
(1) Dimr
model
to1ol
(3) Superslms
(2) Rat CYP supears
(3) S9 fatio
(4) Phnuly
t animal modls ae aailable
mpxrocytes
a- available
(3) Diffr
nimalmods an available
(4) Iutactcell and differnmt aimal modelhs-
available
Figure 1-16: Applicability of current in itromodels in various stages of biotransformation
research
1.8 Hypotheses and Specific Thesis Objectives
This thesis will be centered on the hypothesis that the higher order functions of the
liver, including its biotransformational capacity can be best recreated and preserved in an in
vitroculture, if the critical facets of the in ivo microenvironment namely - cell-cell signalling
through homotypic and heterotypic interactions, adhesion guided cell-matrix signalling and
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soluble and shear mediated signalling in a three dimensional environment - are recapitulated in
the in vitroanalog.
The specific objectives of this thesis are:
1. To determine appropriate design parameters to create a three dimensional "in vim" like "in
vitro" system - specifically, to maintain crossflow through the tissue and to optimize the
crossflow rate to replicate in im shear rates, while satisfying tissue oxygen demand.
2. To assess the ability of the system to maintain liver function - with emphasis on the
biotransformational capacity of the system - using broad metrics of gene and protein
expression and biochemical activity.
3. To use this in vitro system to extract quantitative rates of metabolism of a candidate
xenobiotic.
4. To compare the biotransformational capacity of the designed system with that achieved by
standard cell culture models used in the pharmaceutical industry, again using metrics of gene
and protein expression and biochemical activity.
5. To model the disposition of any chemical species (including a xenobiotic) in the system
taking into account appropriate mass transfer considerations.
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Chapter 2
Design and Fabrication of the 3D
Microfabricated Liver Bioreactor and Fluidics
As described in detail in Chapter
, there is an unmet need in the pharmaceutical
industry for a predictive in tro hepatic tissue culture screen that may be used in pre-clinical
hepatic drug metabolism and toxicity studies. As described for the specific case of liver tissue
models in Section 1.3, in itro models of heterotypic cell interactions are essential tools in
dissecting dynamic physiological and pathophysiological processes. Such models fulfill an
important connection between well-defined cell cultures and the complexity of the whole
animal. They also provide experimental models of human tissue responses, where in 'il'o
models are usually unavailable. Most primary liver cell culture systems currently available have
shown rapid loss of liver-specific function and failure of the cultured hepatoqctes to reestablish normal bile canaliculi, cell polaritr, and cell architecture
[4, 8, 11, 65]. One specific
goal of this thesis was to develop a facile method for recreating a perfused capillan, bed
structure of liver tissue in titr as a tool for studying liver physiology of particular relevance to
the Pharmaceutical industry.
2.1 Key facets of the in vivo microenvironment that need to be recapitulated in
an in vitro system
Most tissues comprise a hierarchical arrangement of cells penneated by capillary blood
vessels. Tissue homeostasis is maintained in part by communication between the different cell
types in a tissue.
Each cell receives signals from its neighboring
cells via direct cell-cell
interactions, cell-matrix interactions, and via soluble signaling molecules (cytokines and growth
factors).
In addition, mechanical forces -- such as shear stress on the endothelium from
flowing blood -- are converted to chemical signals that are necessary for normal tissue function
[81-841.
Techno1ov
Insdture
Massachusetts
NfassachustettS
Institureof
offlechnolop,
51 -51 -
Biotechnology Process Enneering Center
iotechnolgy Prorcess Rngineering Center
The in
ivo microenvironment
(Figure 2-10 of hepatocytes includes signaling
mechanisms mediated by cell-cell and cell-matrix interactions, soluble factors, and mechanical
forces. Primary liver cell isolates contain hepatocytes and non-parenchymal
cells (stellate cells,
Kupffer cells, endothelial cells) and can be induced to recreate features of liver tissue structure
in itro [17, 22, 27, 55, 85, 86].
What is a 3') Perfused Liver Analoq - Minmicin vivo 30 Tissue
CON.
vein
artery
1
,ll
macrophag6e
.vlI'.
(Kupffer cell)
* enable physiological 3D cell-cell relationships of NPC and hepatocytes
Figure 2-1(a): Important facets of the in tivo microenvironment that need to be recapitulated
in an in iitm system
Need to capture the histotypical interactions between the various liver cell types.
Although a rich array of two- and three-dimensional co-culture models have been
developed, the available models lack one crucial feature of most tissues -- a perfused
microvasculature - Figure 2-1 (b). Unlike larger vessels, which can be attached to a flow system,
Instute of
Massachusetts Istiute
Mlassachusetts
of Technology
Technolog-
a-
-
-
52 -52 -
Biotechnology Process Tngineering Center
Biotechnolog Process Engi~eering Center
the small size of capillary blood vessels make them inherently difficult to perfuse in itro in the
context of host tissue.. As a step towards creating physiological mimics of human and animal
tissues that recapitulate the features of a capillar bed, an in itro sstem
that facilitates
perfusion of 3D heterotypic co-cultures at the length scale of the capillary bed in an
arrangement that also allows in situ analysis of cell behavior via microscopy and spectroscopy,
is needed.
What is a 3)
e
Liver Analog - Mimic in vivo fluid/cs
.-;'
.IV
d; as
;,le
!%,ud
mi
I-
c-'harmc
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A
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v
4
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artery
.
enable controlled perfusion through tissue:
interplay of biology and fluid mechanics shear-induced signaling by endothelium I
U
woidg
central vein
Figure 2-1(b): Important facets of the in it'o microenvironment that need to be recapitulated
in an in ritro system
Enable controlled perfusion through the hepatic tissue
2.2 Design principles:biophysics of tissue morphogenesis
A mixture of isolated cells of many different types has an intrinsic propensity to
reorganize in a histotypic fashion (where cells of the same type come together) into a
finctional tissue, if provided an appropriate length scale and time scale to re-organize [55, 85].
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This was well illustrated in a series of landmark experiments by Steinberg and co-workers [85]
and more recently by Michalopoulous and co-workers [21, 22]. In addition, the differential
adhesion of cells to matrix substrata, has been shown to be an important consideration in the
morphogenesis of different cell types into a tissue [55, 85, 87]. The biophysics of cell-cell
cohesion mediated through cadherins (calcium-dependent transmembrane proteins that
interact homotypically with their counterparts on neighboring cells), and that of cell-substrate
adhesion through integrins (
heterodimeric transmembrane proteins that recognize and bind
mainly to components of the extracellular matrix) have been shown to be important
determinants of ultimate morphology, cytoarchitecture, and organization achieved by a
mixture of different cell types in itm [55, 85, 87, 88].
Principles of In VitroMorphogenesis
cell-cell
,'~~0
P~i "~adhesion
;m
receptor
mix
relativestrengths
of adhesion
t
Cell-Cell Forces > Cell-Surface Forces
igh_ _ _ a~w
uffiihx
nffiritu
day
Intermediate (examples)
high
b _ la
;
-ffinitf
affinity
Cell-Surface Forces > Cell-Cell Forces
high_ _ ola
bigP_ _ _I;
ffiriv
ISteinberg,
a-liritv
:
ity. .ff.n.t....................
..!fjn. ,,;
M. (1963)
Figure 2-2: Cell-Cell homotypic interactions that lead to histotypic re-organization over a
defined length and time scale and adhesion guided morphogenesis of pure and mixed cell
populations.
The differential substrate adhesion strength of the cells A and B is shown
to be an important determinant of tissue morphology.
54 -54
of Tcchnologv
Institute of
Massachusetts Institute
Massachusetts
T'cchnolop,
-
""--
-
Biotechnology Process Enneering Center
.Biotechnolog Process En~ineering Center
"`-
Thus the design of an appropriate in itro system will involve the creation a 3D
environment that will facilitate tissue morphogenesis in a controlled fashion - i.e. by
engineering a system that affords a control over the homotypic associations between similar
cell types, over the heterotypic associations of different cell-types and over the adhesion
guided biophysical interactions between the cells and their matrix substratum. The key
principles in tissue morphogenesis are summarized in Figure 2-2. The scope of this thesis was
limited to engendering the homotypic interactions between the hepatocytes and the cell-matrix
interactions between hepatocytes and collagen I in a 3D environment and studying the
biotransformational capacity of the tissue so formed. Two important components of the in ivo
microenvironment - the replication of the heterotypic interactions between the hepatocytes
and the other cell types in the sinusoid (the endothelial cells, stellate cells and Isuppfer cells) by
including physiological ratios of the various cell types, and the control over soluble signalling
mechanisms - were not addressed as part of this thesis. While the non-parenchymal cells are
possibly present in the cultures prepared as part of this thesis, they are possibly present at subphysiological ratios with respect to the parenchymal cells. However, the bioreactor design is
sufficiently general in being able to incorporate these aspects of the microenvironment in
future experiments.
2.3 Microscopic design parameters
Key issues that affect the microscopic design parameters of the in vitrosystem include
constraining the size of cell aggregates (controlling the length scale to allow for histotypical reorganization and to prevent nutrient depletion), controlling the uniformity of aggregates (to
ensure repeatable cultures), and obtaining a physiological perfusion rate through 3D tissue
structures (to simulate physiologically relevant mechanical stresses in the system). As a first
step in this direction, a microfabricated bioreactor that comprised an array of 40-100 capillary
bed-sized (-0.23mm x 0.3 mm x 0.3 mm) channels in a silicon chip and a reactor housing that
enables a stable cross-flow perfusion of cells and cell aggregates seeded into the channels, was
developed. The reactor was designed to enable long-term 3D culture and to allow in situ
optical imaging (via 2-photon microscopy) and spectroscopy of the three dimensional cultures
to assess structure and function. The heart of the reactor is the scaffold, comprising a thin (t -
Inttt ofTehooy-5
Mascsts
Massachustts Institute of Technology
-55 -
itcnlgyPoesEgneigCne
Biotechnology Process Egneering
Center
230 mrn)
silicon sheet permeated from top to bottom by a regular array of channels and seated
above a micro porous filter, which in turn is mechanically supported by a second silicon
scaffold. The channels in the upper scaffold hold the liver cells and influence morphogenesis
via the nature of cell adhesion to the channel walls that is coated with extra cellular matrix
(thus influencing cell-substrate adhesion), and via the physical dimensions and geometry of the
channel cross section (thus influencing cell-cell homotypic and heterotypic interactions).
Perfusion rates are to be optimized taking into account both oxygen mass transfer and shear
stress considerations. A square cross section of 0.30 mm was chosen to allow for a sufficient
length scale for tissue re-organization [55, 85, 87, 88]. Figure 2-3 details the microscopic design
parameters of the bioreactor. The width of the channels in the bioreactor was set to mimic the
appropriate length scale needed for histotypic cell sorting. The channel depth mimics the
length of the hepatic acinus. The low hydraulic permeability of the filter allows for a large
pressure drop across the filter enabling a uniform crossflow through every channel of the
bioreactor independent of the tissue distribution and cell numbers in each channel of the
bioreactor. The axial flow rate was set to a high value of 0.5 ml/min to minimize oxygen
concentration gradients along the length of the bioreactor (Pe - 100 >>1).
This thesis focused on the maintenance and optimization of the perfusion or crossflow
through the tissue in the channel of the bioreactor. The crossflow rate (and hence the values of
the Peclet and Reynolds numbers for the perfusion flow rate through the tissue) was
optimized taking into account the twin considerations of simulating physiologically relevant
shear stresses while also satisfying the tissue oxygen demand.
Massachusetts Institute of Technology
-56 -
Biotechnology Process Engineering Center
.
p... F L
L:
Microscopic Reactor Design Parameters
channel dimensions and crossflow rates
. '
.b. .
I I .1 ~- -
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;
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.
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APfilter
hit f-hannels
length scale of cell sorting and self-organization
= 300 urnm
(Powers, 1997: Steinberg, 1963)
t -
30 um (mimic length scale of lobular repeat unit)
Qxflow ~ to be optimized based on simultaneous considerations of tissue
oxygen demand and the need to simulate physiologically relevant
shear stress
Qin
- 0.5 ml/min, so that Pe >> 1, negligible axial oxygen concentration
drop along the length of the reactor
Figure 2-3: The design parameters used in the fabrication of the cell-holding channels in the
silicon scaffold.
The tissue perfusion and bioreactor axial flow rates are set taking into account the need to
simulate physiological shear stresses in itro while satisfying the tissue oxygen demand
2.4 Photolithography: fabrication of the silicon scaffolds
All scaffolds used in this thesis were fabricated in house bv Dr.lKarel Domansky at the
MilcrosvstemsTechnology Laboratory at MIT. Scaffolds were fabricated out of silicon. Silicon
allows facile prototping
of a variety of channel cross-sectional geometries and dimensions.
The silicon scaffolds were fabricated by deep reactive ion etching (DRIE) of double-side
polished 230
m thick <100> boron-doped silicon wafers. Because the DRIE processes
exposes the patterned wafers to SF6 etch followed by passivation in C4F8, the sidewalls of the
microchannels are covered with a thin laver of a fluoropolymer. In contrast, the top surface of
the silicon wafer is devoid of the fluoropolhmer due to the protection by photoresist during
the DRIE process (igure 2-4).
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Figure 2-4: Schematic of the DRIE process (left) used to fabricate the scaffolds and photos of
the channel structure and geometries showing resolution.
Upper photos show a light micrograph as well as an SEM (Scanning Electron Micrograph) of
the scaffold structure (adapted from figure in [891)
Because the system is designed for the formation of microtissue units within the
channels, it is essential that cells can attach to the channel walls while remaining non-adherent
to the top oxidized silicon surface. Previous results from experiments conducted in the lab,
suggest that the cells adhere more strongly to the collagen coated photopolymer surface than
to the collagen coated silicon dioxide surface [89]1.Silicon scaffolds with channels and slits of
varied geometry are showed in Figure 2-5.
Technology
of Technollop,
nstitute of
'Massachusetts
assachustts Institute
-
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-58
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Figure 2-5: Silicon scaffolds of different geometries microfabricated by DRIE
(Photqgraphby Dr.Karel Domanwskj)
2.5 Design and fabrication of the polycarbonate bioreactor housing
The bioreactor housing features top and bottom polycarbonate compartments, each
with flow inlets and outlets. The scaffold assemblv fits into the 12 x 7 x 2.4-mm oblong pocket
in the top compartment (Figure 2-7). The bottom compartment functions as a lid enclosing
the bioreactor. A Buna O-ring with a 316-stainless steel retaining ring provides the seal. Both
compartments
are approximately
fabricated b
micromechanical milling and thermal diffusion bonding. The bonding
technology,
24.5 x 16 x 3.1 mm in size. The top compartment
originally developed for PILMA [90), 91], was modified b
was
Dr.Karel Domanskv
and co-workers for polycarbonate [92], tested on a batch of prototqypes, and transferred to
Eastern Plastics Inc. (Bristol, CT) for fabrication. The reason for using a bonding technique
instead of an adhesive was to avoid the possibility of leaching chemicals harmful to the cells
into the cell culture medium. Prior to bonding, features in the compartment
were
micromachined on both sides. The 800 x 400-xrn surface channels were closed (forming Dshaped channels) by the thermal diffusion bonding of a thick sheet of polycarbonate to the
rnicromachined part and lapping it to 125 ,urn. The dimensions as well as the curvature of the
[)-shaped channels were set based on experimental results obtained from tissue cultures rumn
on a version of the reactor called MilliII. The poor tissue phenotqpe in the MilliII reactors
resulted in modifications to the design of the inlet chamber to minimize the possibility of cell
stress/ shear when multiple spheroids of 3001Lmdiameter entered the inlet port simultaneously
(Figure 2-6). In this way, a thin, optically clear window for microscopic observation of the cells
Institute of
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Nfasiachusettt
of Technology
Tetchnology~
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was formed. The size of the oblong fluidic chamber above the cell-holding scaffold was 8 x 3 x
0.725 mmn.The bottom bioreactor compartment was fabricated by micromechanical milling.
The threaded fluidic connectors were custom-machined from polvetheretherketone (PEEK)
and sealed with Viton O-rings. The internal volume of the reactor (including connectors) is
102
l.
Lessons learned from Milli HIexperiments - Valuable
inputsfor design of next prototype
Necessary Changes to Design:
nf
c-hin fnr length
occRnvx
withnut
. MP-rhnnirmn
tn icnlatp
ollc in entrance
for
a fullyIncdeveloped
flow
- lMarginalincrease
· Marginal increase in entrance length for a fully developed flow
t
· Increase in inlet port size - from 300 .tm to 800 gm
· Chip geometry - round vs. oval - minimize misalignment, "hands
free" assembly of reactor
* A gradual change in flow direction into and out of the reactor
chamber - minimize cell shear
Figure 2-6: Changes made to the design of the MilliF reactor, based on feedback from
experiments on Mill reactors
Technology
Institute of
MassachusetrsInstitute
ofecnology~
1M~assachusetts
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Silicone gasket
Silicon scaffold
:affold
Microporous filter
ssembly
¢,...
Silicon scaffold
. .
.-. '~..
Silicone gasket
Q-----/
0-ring with
9
,
bonded by thermal diffusion
retaininginsert
Figure 2-7: An expanded view of the scaffold assembly left) and a schematic of the various
parts of the bioreactor
(Schematicand photograpbs1?,Dr.Kirel Domansk)
2.6 Assembly of the bioreactor
Polvcarbonate reactor compartments and polycarbonate scaffolds were sterilized in
70% ethanol. All other reactor parts, tubing, connectors, and reservoir were sterilized by
autoclaving. Cell scaffolds were coated in 30
g/mL Vitrogen (type I collagen) (Cohesion
Technologies, Palo Alto, CA) for 30 min and rinsed in phosphate-buffered saline (PBS), pH
7.4 (Life Technologies,
Rockville, MD).
Hydrophilic
Durapore
M
T
filters (fillipore
Corp.,
Bedford, NL) with a pore-size of five microns were punched with a custom-made oblong
in PBS.
puncher and coated with 1o bovine serum albumin (BSA) (Sigma, St. Louis, MO10)
Components of the scaffold assembly were individually inserted into the bioreactor
pocket as shown in Figure 2-7 and secured by attaching the bottom reactor compartment
using three screws. Four threaded holes in the corners of the reactor were used for screwing
Tnrtt ofTcnlg
\issch-et
MLassachustts nsitute of Technology
2-BoehooyPoesEgneigCne
Biotechnology Process Enineering
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C:enter
the reactor to a plastic mounting bracket. A photograph of an assembled reactor is shown in
Figure 2-8.
.!i~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
s
N
II.
~
.v. 11W~~~~~~
.4
.:~
Figure 2-8: Photograph of an assembled bioreactor
(Photographby
3 Dr.Karel Domansky)
2.7 Design of fluidic system
A single pump fluidic system was originally designed to go with the bioreactor. The
bioreactor was connected to a 40-ml polyvpropylene reservoir with silicone (Silastic(®,Dow
Coming, NJ) tubing. The reservoir featured a customized lid with fluidic feedthrough
connectors.
A mini peristaltic pumps (Instech Laboratories, Inc, Plymouth Meeting, PA)
equipped with C-flex® pump tubing of different internal diameters for selected flow rate
ranges were used to circulate the culture medium as shown in the schematic in Figure 2-9. The
crossflow rate was determined exclusively by the pressure drop across the filter, sandwiched
between the two silicon scaffolds. Thus, a portion of the axial flow was pulled through the
channels of the silicon scaffold in the bioreactor and eventually went into the reservoir. The
system was operated in complete recycle.
'1'cchnology
Intiwre of
N1assachusett Tnititue
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of'lechnology
62
-(62
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_____
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Biotechnology Process Ennecring
Single Pump Fluidic System
-.I
~.
---I
imaging & spectroscopy
.
#
reservoir
Figure 2-9: Single pump fluidic system used to run culture media through the bioreactor
2.8 Maintenance of crossflow in the bioreactor
During the initial runs of the bioreactor, the perfusion flow through the silicon chip
was observed to dnr up within 24-48 hrs. after seeding the reactors (Figure 2-10).
It was hypothesized that this occurred either due to:
A: Increased impedance in the cross flow line due to bubbles in some channels of the
bioreactor.
B: Clogging of the Durapore
membrane filter in the scaffold sandwich by cell debris and
spheroids.
Masachuserts
MassachusetsIntirntc
Intitte of
of 'Iechno]ogx'
T~'chnolqgy
- 633 -
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-
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Biotechnolopr Process Engineering Cnter
Crossflow measurement in Hepatocyte Culture
0.08
0.06]
3
o 0.04
',
,.
I
0.02 -
Ca
0
-n
70
10
20
30
40
50
60
Cells seeded
Time (hrs) post seeding
Figure 2-10: Loss in crossflow seen in 3D bioreactors 24-48 hrs. post seeding
In order to test hypothesis A, a second peristaltic pump was used in the cross flow line to
maintain a constant as shown Figure 2-11 below. It was reasoned that the introduction of the
second peristaltic pump (that maintains a constant flow independent of head or impedance, so
long as the impedance is below the rated head of the pump) in the crossflow line would help
maintain a constant flow in the line.
Axial Flow
500 ld/min
Reservoir
To I
Reactor
From Reservoir
...................
Reversed
flow
75 pl/m in
Figure 2-11: Schematic showing a two-pump fluidic system where the second crossflow pump
pushes fluid from the top of the reactor in through the channels
6
-64-
MasahuetsnsittcofTehnloy
Massachusetts Institute of T'echnolog
-
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I
BorcholgyPrces
ngnerig
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Biotechnology Process Engineering Center
However, the cross flow was found to dry up even in this case. This suggested
that hypothesis B, maybe the most likely reason for the clogging of the filter. Another
related set of experiments conducted simultaneously, wherein the flow in the cross flow
line was reversed, showed that the reactors started leaking in the crossflow line within
24-48 hrs of reversal of flow, possibly due to the clogging of the filter in the reverse
direction, in the absence of any filter in the external flow circuit to collect and screen the
cell debris. The next set of experiments involved the use of two filters (marked F) in the
external flow circuit as shown in Figure 2-12 below:
Axial Flow To Reservoir
500 gl/min
Reactor
F1
-
From Reservoir
..
,*·······
v,_,/
75 gdl/min
(A) Reversed flow
...................(B) Normal flow
Figure 2-12: Schematic of the two-pump fluidic circuit after the addition of the inline filters
Filter F
(0.2 p.m) is added to the axial line of the reactor during seeding and is
removed within two hours after seeding. The purpose of this filter is to remove most of the
excess spheroids that did not go into the channels. This helps eliminate any noise in the
albumin data due to secretion from suspended spheroids / spheroids in the flowing medium.
Immediatelv after seeding, the pump in the cross flow line can either be connected in normal
flow mode (in the direction of arrow B, as shown). After 24 hrs, when the spheroids are well
spread and have formed structures, a filter is added to the crossflow line (F2, 0.8/0.2 pm
Supor* double filter from Pall Gelman), and the direction of cross flow is reversed. In
reversing the direction of crossflow, adequate precautions need to be taken to:
1. The crossflow line as well as the bottom of the reactor must be primed to minimize air
bubbles that can. jostle the cells out of the channels. The fourth port (not used in normal
reactor operation) may be used to prime the CF line using the crossflow pump i.e. keep the
fourth port open till the crossflow line is primed. Use of smaller diameter tubing in the
Masacustt
Manssachusetts Insdrutc of Tchnolop,
I~uut
6 -Bitehnloy
o Tchnloy
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ioechnolopr
PocssEninerngCete
PI-rocessE~ngireerig
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crossflow line helps minimize the time needed for priming the line. Moreover, use of a syringe
to prime the line helps minimize the time needed for priming.
2. The filter used in the crossflow line must be passivated with the culture medium to
minimize nutrient depletion in the flow circuit.
.
, .
Figure 2-13: Cell debris seen in the media in the reservoir 1.5 hrs. after reversal of crossflow
Figure 2-13 above is a picture of the cell debris and unattached spheroids collected in
the medium (taken 1.5 hrs after flow reversal). The presence of filter F2 prevents this debris
from entering the crossflow line and clogging the DuraporeR' filter placed between the silicon
scaffolds. It is a good practice to change the medium in the crossflow line, 2-3 hrs. after flow
reversal. Moreover, if possible the filter F2 in the CF line should be changed whenever the
media is changed - once evetr three days to prevent clogging of the inline filter leading to a
loss in crossflow.
The crossflow is maintained at a constant flow rate for the entire duration of the
culture, by using the scheme described above. Figure 2-14 below shows cell debris being
removed from the filter as a result of the reversal of crossflow. The tissue structures however
remain intact in the channels of the bioreactor.
-
-
-~~~~~~_
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- 6-
nsttut o Tehnoog
Masachsets
Nla,,;sachsetts
Institute of'l'cchnol(V-
-
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_
_
-Bioecnolgy
Biotechnology P'rocess Enaincring Center
~
~~~~~~~~~~~~~~~~~~
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-
-__
(a)
(h)
Figure 2-14: Cleaning effect of crossflow
(a) Before reversal of crossflow (b) 15 minutes after reversal of crossflow
Thus, a modified design of the fluidic circuit optimized for use with the bioreactor,
that ensured the maintenance of a constant perfusion rate (crossflow) through the tissue, is
shown in Figure 2-15.
imaging & spectroscopy
vent filt
Figure 2-15: Schematic of the optimized two-pump fluidics used with the bioreactor
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2.9 Summary of key results and conclusions
A microfabricated bioreactor system that allows for three dimensional morphogenesis
of liver tissue under continuous perfusion conditions was developed keeping in mind
important facets in titr microenvironment that had to be recreated in the in
itro
system. A key
feature of the bioreactor is the distribution of cells into many tiny, (-0.001 cm3) tissue units
(capillary bed-sized (-0.23mm
in a silicon chip) that are
x 0.3 mm x 0.3 mm) channels
uniformly perfused with culture medium. The total mass of tissue in the system is readily
adjusted for applications requiring only a few thousand cells to those requiring over a million
cells by keeping the microenvironment the same and scaling the total number of tissue units.
The reactor was designed to enable long-term 3D culture and to allow in sitt optical
imaging (via 2-photon microscopy) and spectroscopy of the three dimensional cultures to
assess structure and function. The channels in the upper scaffold hold the liver cells and
influence morphogenesis via the nature of cell adhesion to the channel walls and via the
physical dimensions and geometry of the channel cross section. Perfusion rates are optimized
taking into account both oxygen mass transfer and shear stress considerations.
The bioreactor fluidics was optimized to include a two-pump system, together with an
inline filter that enables the maintenance of a constant rate of perfusion through the tissue
formed in the channels of the silicon scaffold in the bioreactor.
'lechnology
Institute of
NLsachusetts Institute
,%assachusetts
of'lechnolog
_··_
"
-E
_
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U-
.-
68 -68
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Biotechnology Process Engineering center
Biotechnology Process Engineering Center
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Chapter 3
Modeling the Fluid Dynamics and Mass
Transfer Effects in the 3D Bioreactor
This chapter details the fluid dynamical modeling of flow in the microfabricated bioreactor in which primary rat liver tissue is maintained in 3D channels in the silicon scaffold, by
continuous perfusion of culture medium. The fluid dynamical modeling of the bioreactor
system was done with considerable support from Mohammad Kaazempur-Mofrad, a research
scientist with the Kamm Lab. Figures in Sections 3.1 and 3.2 have been taken from already
published modeling results [89]. In addition, to better understand the tissue disposition of
xenobiotics, nutrients as well as oxygen, a comprehensive mass transfer model was also
developed. Perfusion through the channels causes the cells to be constantly under the
influence of fluid flow. The rate of flow can influence morphogenesis and cellular function via
mechanical and chemical means [81, 84]. Fluid shear can activate signaling pathways and
influence differentiated function of hepatocytes and endothelial cells [82, 83]. An accurate
determination of the shear stress experienced by the tissue is important in determining several
design parameters such as flow rate and geometry to ensure that the cells in the bio-reactor are
subjected to the same kind of environment as they would inside the liver capillaries. Fluid flow
inside the bio-reactor was analyzed using a computational model in ADINA ® to estimate the
velocity profiles, pressures and fluid shear stresses present within the system. The reactor
geometry is sufficiently complicated that simple analytical calculations had to be supplemented
by a full computational model.
In addition to shear stress, the fluid flow through the channels influences the
metabolite and nutrient concentration distribution both within the channel as well as in the
three-dimensional tissue. These two concentration gradients are coupled and necessitated the
use of a Finite Element Model in FEMLAB, to solve for the gradients.
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The computational fluid dynamical model in ADINA®, together with the mass transfer
model in FEMLAB®, allow for the determination of tissue distribution of any metabolite,
given an appropriate intrinsiccellular uptake/ release rate for the metabolite. Alternatively, the
mass transfer model helps estimate the intrinsicrate of metabolismof any metabolite, given the
average outlet concentration of the metabolite in a single-pass of the metabolite through the
reactor at steady-state. For a reactor operated in complete recycle mode, the model may still be
used to predict the intrinsicrate of metabolism,given the average outlet concentration of the
metabolite as a function of time, and ensuring that the dead volume in the reactor fluidics is
very small to ensure an average residence time much smaller than the time between
consecutive media samples taken from the reactor. This can ensure quick equilibration of local
concentration changes within the reactor volume.
3.1 Computational fluid flow model in ADINA
The main results reported in sections 3.1 and 3.2 (upto Figure 3.8), was work done by
Dr.Mohammad IKaazempur Mofrad and Dr.Arpita Upadhyaya of the Kamm lab, published
previously [89]1.The fluid dynamics within the reactor was modeled using the complete form
of the Navier-Stokes equations in three dimensions, without any initial relaxations or
approximations. The basic governing equations for viscous incompressible fluid flow are
(using index notation and usual summation convention):
Continuity:
Viji=
(3-1)
4 i+ vjvi,j
Momentum:
= Zj
(3-2)
where p is the mass density of the fluid, ,i is a component of the fluid velocity and ,yis the ij1
component of the stress tensor.
Zr,=-pg
Inttt
Masahset
ofTeholg
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(3-3)
tcnlg
:
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.... . -
e
where
p is the fluid pressure, ,. is the
V. +v.i
2
2
(3-4)
ronecker delta, and ,U the coefficient of viscositv.
The finite element method was used to solve the above equations incrementally in
time using the commercially available finite element analysis software ADINA, version 7.3.
The main model developed for the Millill reactor and published previously [89] was slightly
modified with the help of Dr.IKaazempur-Mofrad and used for the MilliF reactor. The reactor
geometry in the model was made identical to the geometry of the bioreactor used in the
experiment. The reactor parts included in the model were - the upper chamber with its inlet
and outlet channels as well as the silicon scaffold with the tissue filled micro-channels. The
entire volume under consideration was meshed using four-node tetrahedral fluid elements. A
linear interpolation function for the velocity and pressure were used with the NewtonRaphson iteration scheme. For the whole-reactor studies, a mesh size of 0.1 mm was used and
provided sufficient accuracy. The mesh size was decided as an optimal value based on
computational resources and accuracy of the solution. The material properties were assumed
to be those of water: density = I g/cm 3 and viscosity = 0.01 Poise. Further, the assumption of
laminar flow is justified due to the low Revnolds number of the sstem: Re = 1 in the interior
of the upper chamber and Re = 0.1 within the scaffold for finite cross-flow.
The value of the experimental input flow rate from the peristaltic pump was used to
calculate and specify the input velocity (in the y-direction) at the channel inlet. For simplicity,
the average velocity was calculated using v = Q/A, where Q is the pump flow rate and A is the
cross-sectional area of the inlet. A flat profile was assumed at the inlet. It was verified that the
fluid attains a parabolic velocity profile within a distance of approximately one diameter from
the inlet as expected for low Re flows. A zero traction boundary condition was specified at the
outlet from the upper chamber of the reactor to denote a free surface. No-slip boundary
conditions were specified at the walls of the upper chamber and the silicon scaffold. The
outlets from the scaffold micro-channels were either specified with no slip boundanr
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conditions to denote zero cross flow or given a specified velocity in the z-direction (as
calculated from experimental values of cross-flow rate).
The presence of tissue in the micro-channels was modeled by redefining the channel
geometry. Within a day after seeding, the hepatocyte spheroids, which are firmly adherent to
the collagen coated channel walls, spread out to form a layverof tissue inside the channels,
leaving a roughly cylindrical conduit for fluid flow. The geometry is evident from the SEM
images (Figure 3-l).
Isometric view
"I.
0
El
Is
300
1
=.
_
I
I
Figure 3-1: Scanning electron micrographs providing appropriate length scales for the
idealized tissue geometry.
The isometric view shows the idealized tissue geometry-an annular tissue area with a
cylindrical conduit in the center of the channel.
Through the obsenrvationof several images (at least 15) of tissue structure in scaffolds,
the model geometry of the flow path was defined to be a cylinder of 100 im diameter. The
surface was assumed to be rigid with no slip between the fluid and tissue. This assumption is
justified considering that the small fluid velocities in the reactor are not sufficient to cause flow
induced deformation of the tissue. Two regions can be specifically identified in the idealized
geometry in Figure 3-2 - the conduit that allows cross-flow through the channel and the tissue.
As a first pass, the tissue was assumed non-porous ind convective transport through the tissue
was neglected. A detailed investigation of the stresses inside a single micro-channel required
the use of a finer mesh than that used globally. Due to computational limitations, a smaller
mesh size of 0.01 mm for only one channel at several scaffold locations was used, while using
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the original mesh of 0.1 mm for the rest of the reactor. Figure 3-3 shows the mesh for an
entire model.
Isometric view
Trj~f
"ALILRu
Fluid flow
E
E
o0
M
Ti
Tirr_
Fluid flow path
Figure 3-2: Idealized tissue-channel geometrn with a cylindrical conduit for convective flow
A non-porous tissue, with species diffusion and enzymatic reaction as the only contributing
factors to mass conservation within the tissue, was assumed.
p rfuson cross-fow
mlcrochannels
Figure 3-3: Mesh distribution in the ADINA ® model used to describe fluid flow in the
bioreactor
(from [891)
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3.2 Velocity profiles and shear stress distributions
This section details the results from the fluid dynamic modeling of flow in the
bioreactor. The input flow rate from the pump was set to be 0.5 ml/min and the rate of crossflow was specified to be 40 .d/min (or 1 l/min per channel) based on experimental data.
Figure 3-3 shows the y-velocity (along the long axis of the chamber) at the vertical center plane
(z = 0.0). No apparent non-uniformity of flow is seen along the length of the reactor.
Figure 3-4 shows the x component of the velocity at the center z-plane. The xvelocities are high near the inlet and outlet due to the sudden widening and narrowing of the
flow path, but reduce to zero beyond at most one row of channels. Therefore an axial flow is
maintained over most of the channels. The z-velocities are negligibly small at this plane.
Z
X
Y
Y-VELOCITY
TIME1.000
- 1E.80
)W
- 15.60
- 1440
- 1a20
'
N.,
- 10.80
.1~
-
%
-~:.,
-- 1
..>
-
9.60
8.40
7.20
6.D0
4.80
3.60
2.40
1.20
00
Figure 3-4: Uniform flow seen over the top of the channels, over most part of the bioreactor
after a short entrance length
(from [89])
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5~~~~~~~~~~~~~~~~~~~~_.
A-
b
z
Y-VELOCITY
TIME1.000
L[~~I-
i'Ag~~
O
W
14.40
13.20
-12.0U
-10.80
9.60
8.40
720
6.00
4.80
3.60
2.40
- 1.20
0.00
Figure 3-5: Uniform axial flow is seen to be maintained over most of the channels
(from [891)
It is seen that the flow is fully developed away from the inlet and outlet. Next, the
effects of fluid flow inside single tissue-filled microchannels within the silicon scaffold were
investigated. The main effect of shear stress will be on the outermost cell layer in contact with
the fluid. The cells lining the cross-flow path are directly under shear and therefore susceptible
to shear induced mechanical and chemical signals. The experimentally obtained value of
roughly 1 l/min per channel (experimentally determined to be in the optimal range wherein
CYP450 expression is seen not be affected by flow rate) was used to specify the z-velocity at
the channel outlet. Figure 3-5 shows the z-velocity inside a single channel of 100 Jim diameter
at the center of the scaffold. The flow becomes fully developed within 50 Sim of entering the
channel. Figure 3-6 plots the wall shear stresses at different z-positions along the tissue filled
channel. The shear stresses at the walls are about 0.4 dyne/cm 2 near the top of the channel
and approximately 0.7 dyne/cm 2 halfway through the channel vertically. To verify these results,
the wall shear stress in the channels was calculated assuming Poisseulle flow. Using the
relation: ;' = 8luQ,/2 3, z' = 0.7 dyne/cm 2 , similar to the value obtained from the numerical
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model. Figure 3-7, shows a pretty uniform shear stress distribution over the top of all the
channels in the bioreactor.
L
z
x. Lv
VELOCITY
Z-VELOCITY
TIME 1 000
TIn 1.000
3500
-
-
-00
O
'
2.750
--.000
-1.250
-1 W
-1.7500
-2.?..
- 2.000
-22W
-1.000
2.750
-0.750
0.500
-2.750
a'
:.
-3.250
-3.500
i
0.250
0.000
Figure 3-6: Velocity profile in a single channel of the bioreactor
(from
[891)
The presence of the filter causes the profile to flatten at the end of the channel.
Figure 3-7: Shear stress distribution along the depth of a channel
('s' denotes distance from the top of a channel). The maximum shear stress experienced by the
cells is on the order of 0.07 Pa; well within the range of physiological shear stress values (from
[89]).
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Perfusion flow: 40 ul/min i
' Axial flow: 500 ul/min
ss'~
0.030
0020
_
0.015
0.010
o.
n"
)0O
8x3 MilliReactor with open cross-flow channels
Figure 3-8: Uniform shear stress distribution seen over the top of the channels in the
bioreactor
(from [89])
In order to determine the appropriate range of crossflow rates that simulate
physiologically relevant shear stress rates to enable the histotypical reorganization of the 3D
tissue, the maximum shear stress experienced by the tissue in a channel was plotted as a
function of the crossflow rate. This has been reproduced in Figure 3-9. The Poiseullar nature
of the flow predicted by previous modeling studies [89], was thus confirmed using the full
blown ADINA
model developed for the MilliF bioreactor by Dr.Mofrad. Based on the range
of flow rates seen in in vivohepatic sinusoids[81, 83, 84, 93], the perfusion crossflow rate may
be set to a value between 40 l/min and 600 l/min, to mimic shear rates seen in the in ivo
microenvironment.
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II
0.25
W0.20
c0.15
co
Physiological
Value - lower
bound for
)O
perfusion cross-flow rate, uL/min
Figure 3-9: Maximum shear experienced by the cells in a channel as a function of crossflow
rate.
Based on reported in vivovalues of shear experienced by cells in the hepatic sinusoids, the
bioreactor may be operated in a range of flow rates between 40 ul/min and 0.6 ml/mnin.
3.3 Mass transfer model in FEMLAB®
An idealized tissue-channel geometry with a cylindrical conduit for convective flow, as
seen in Figure 3-2 formed the basis for the mass-transfer model. A non-porous tissue, with
species diffusion and enzymatic reaction as the only contributing factors to mass conservation
within the tissue, was assumed. The shaded area in Figure 3-2 represents the tissue. The basic
species conservation equations were used to solve for the coupled tissue-conduit species
concentration profile. These equations in their most generalized form are (assuming constant
density and diffusivity):
DC = DV 2 Ci + R
Dt
(3-5)
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+,
Where:
a
a
D
--
v
+--
a
+v
(3-
a
-
and D i is diffusivity of the i species, C i is the concentration of the i species in the tissue,
R vi
, is the cellular uptake or release rate of the ih species, r, 0 and z represent the cylindrical
coordinates used to describe the system. Since the tissue is assumed solid and non-porous, the
species conservation equation at steady state applicable to the tissue (shaded area in Figure 32), reduces to:
DiV2Citi,ssie + R,, = 0
(3-7)
Where Citissue represents the i h metabolite concentration in the tissue. The appropriate
boundary conditions needed to solve for the concentration profile in the tissue include the noflux boundary condition at the walls of the chip, flux-continuity at the tissue-fluid interface,
and appropriate concentrations on the top and bottom plane surfaces of the scaffold (see
Figure 3-7).
- D.VC, isse
-
D.VCi,tiue
Mwall
=0
(3-8)
D.VCi,fluid fluid-tissue
tissue-fluid=
(3-9)
where CO, and Cz=r are set to known inlet substrate concentrations Co.
The species conservation
equation in the channel with the Poiseuille flow
approximation (which follows from the fluid dynamic modeling described in the previous
section) reduces at steady state to:
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-v~pi = D.V2Cibflid
z
(3-10)
z
The appropriate boundary conditions needed to simulate the metabolite distribution in
the channel include the continuity of fluxes at the fluid-tissue interface and a valid inlet
concentration of the metabolite.
- D.VCi,fluid
fluid-tissue=
Cfluidz=T
D.VCissue
(3-11)
tissue-fluid
(3-12)
= Co
The species conservation equations were non-dimensionalized using an appropriate
length scale (a fixed value of 100 m, which is in the range of the diameter of the cylindrical
flow conduit seen, L), an appropriate concentration scale (inlet metabolite concentration C),
and an appropriate velocity scale (U=vzinet is the velocity entering the channel at the bottom
of the scaffold). This leads to the description of the system using the following nondimensional parameters: The PNcletnumber (Pe),and the Damkohler number (Da) or Thiele
modulus ().
Convectlon-dominated
flow on
top of the scaffold
- Longitudinalaxis
of symmetry
-z
Z=O
~
'L
:
Tissue - fluid Interface
.i
.
*.
Z=T
--
flowthroughchannel
Convectlon-dominated
(P6- 100 for a cross-flow
rateof 1 plVmln/channel)
L
Figure 3-10: Interfaces and boundaries in the mass transfer model
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The appropriate experimental values of inlet velocity U=vi,,le, , inlet substrate
concentration C,, and the characteristic bioreactor channel dimensions (square cross section of
300
munand a depth of 230 Etm, characteristic length scale L = 100 pm) were used to
determine the values of the non-dimensional parameters C*, Pe, and Da defined in Figure 3-8.
Time scalefor convectivetransport
*Pclet
·
Number:
Timescalefor diffusive transport
UL
D
Time scalefor reaction
·Thiele Modulus/ Damkohlcr Number:
R.L2
Time scale for diffusive
DC
transport through the tissue
Cross-section
P ~ 1(3
Fluid -tissue
*= 1
cell
-
interface
P varies with
cross-flowrate
Figure 3-11: Appropriate non-dimensional parameters used to characterize the system
The average viable cell number in the tissue in a channel was determined from total
RNA measurements, described in the Appendix. The planes of symmetry seen allowed the
reduction of the FEMLAB® simulation to the modeling of a quadrant of the tissue-channel
geometry. The cell uptake or release rate of a metabolite was assumed to follow MichaelisMenten kinetics. It is defined as:
RV =Vmax C
C+Km
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where V,
and
K, are the appropriate Michaelis-Menten parameters, representing the
maximal enzyme activity and the substrate concentration at which the activity is half the
maximal, respectively. Tetrahedral elements were used to set the mesh distribution in the
geometry. Mesh sensitivity analysis was performed at the end of every simulation to check for
the effect of mesh density of the predicted concentration profiles. Non-dimensionalization of
the governing species conservation equations yields two main non-dimensional parameters RL2 , which represents the ratio of the time scale for species
the Damkohler number (~'
DC,
reaction to the time scale for species diffusion to the site of reaction in the tissue), and the
UL
which represents the time scale for diffusive to convective transport of
Pclet number (-,
species through the cylindrical fluid conduit). In the definitions above, L is the characteristic
length scale (taken to be 100 pm), D is the species tissue diffusivity (m2 /sec), U is the average
Poissueller velocity (m/sec.), C,, is the initial species concentration at time t = 0, and R,.is the
volumetric species consumption or production rate (also called the volumetric reaction rate,
moles/m
3
sec.).
3.4 Tissue disposition of oxygen
The model was used to simulate the tissue and channel distribution of oxygen using
parametric values reported in literature. A maximal, conservative (i.e. the highest value
reported in literature) cellular oxygen consumption rate of V,/ = 0.4 nmoles/ 106 cells/ sec.
[94], an inlet oxygen saturation concentration of 0.19 mol/m 3 (corresponding to a volumetric
ratio of 19.2% 02, 72.3% N2 and 8.5% CO), a KM of 7
M [94], and an oxygen tissue
diffusion coefficient of 1.9 x 10-9 m2 /sec. [95, 96] were used as parameters in the model. The
per cell consumption rate expressed in units of nmoles/10 6 cells/sec. was converted into a
volumetric rate, expressed in units of nmoles/ m3 of tissue/ sec. using a calculated value of
tissue volumetric density expressed in units of cells/m3 (Table 3-1).
Based on the underlying assumption made, two different scenarios exist for the tissue
disposition of oxygen:
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1. The cells are rigid and each cell occupies a finite constant volume in a tissue, so that the
radius of the cylindrical conduit in Figure 3-2, changes with the average cell number per
channel.
2. The cells are flexible and can be packed in a finite fixed tissue volume - i.e. the mechanical
and biophysical forces force a cylindrical conduit of a fixed radius in each channel independent
of the cell number.
Transmission electron micrgraphs such as those in Figure 4-7, indicate that in some
cases the cells are non-spherical and compacted in a tissue, while in some other cases they
maintain their rounded morphology. Thus both scenarios are analyzed for the tissue
distribution of oxygen.
For the first scenario, the tissue volumetric density (cells/m3 ) was calculated using the
theoretical maximum number of spherical cells of 25 rmndiameter that may be packed in a
total channel cuboidal volume of 300 upmx 300 pm x 230 Pm) and is equal to 1/VC =
1 /(4/3..r3),
where 'r' is the radius of a single spherical cell (12.5 Vm).The average cell number
per channel is calculated using the value of total cell number obtained by measuring the total
RNA (see Appendix) in the scaffold in the bioreactor. It should be borne in mind that though
there may be variations in the total cell number per channel across different bioreactors - this
does not affect the cell density (cells/m3), but does affect the effective diameter of the porous
conduit available for perfusion flow. Hence while the Pe number varies as a result of the
differences in cell number (causing variations in the effective cylindrical conduit radius
available for flow), the Damkohler number remains the same. Table 3-1 describes this in
greater detail. In order to study the effect of various perfusion or crossflow rates on the tissue
distribution of oxygen, experiments were performed with a set of eight reactors set at different
flow rates - 0, 40, 100, 230 ul/min (two reactors at each setting). Day 3 spheroids were seeded
into the reactors and cultured upto seven days in culture post cell-isolation. Table 3-1 gives the
cell numbers measured in the bioreactors and the calculated values of the Pe and Da numbers.
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Table 3-1. Average viable cell numbers and non-dimensional parameters for modeling the
tissue distribution of oxygen in bioreactors with and without cross-flow - Scenario 1
Bioreactor
Avg. viable cell
Avg. viable cell
Calculated
Effective
cross-flow
(ul/min)
number based on
total RNA
number per channel
Tissue
Volume, using
Tissue length
scale 't'
(prm)
measurements
0
27275 +/- 6909
682
a cell diameter
of 25 !zm [m 3]
5.582 x 10-12
40
38956 +/- 9677
974
7.972 x 10-12
18
100
25111 +/- 5620
628
5.140 x 10-12
3.5
230
32527 +/- 3250
813
6.654 x 10- 12
11
Diameter of
cylindrical
crossflow
Perfusion velocity
per channel (m/s)
Characteristic Length
Scale used for nondimensionalization
Peclet
Number (Pe)
Damkohler
number (Da)
100
0.00
1.35
(n=4)
conduit (300
- 2*t)
290
5
(Pm)
0
264
1.9022E-05
100
1.00
1.35
293
3.86073E-05
100
2.03
1.35
278
9.86376E-05
100
5.19
1.35
Figure 3-12 shows the predicted tissue distribution of oxygen for the case where there
is no crossflow (using parameters from Table 3-1). A non-dimensionalized concentration of
1.0 corresponds to saturated concentration of oxygen in the media in equilibrium with air
containing 19.2% of 02, 79.3% N2, and 8.5% CO 2. The oxygen concentrations drop almost to
values comparable to IM at the bottom (230 Gm) of the channel However, tissue hypoxic
responses at the gene expression level can occur at low but non-zero oxygen concentrations
(7% 02 or 0.36 in the scale where 1.0 corresponds to dissolved oxygen concentration in the
medium in equilibrium with 19.2% 02, 72.3% N2 , and 8.5% CO) [97]. Such concentrations
are reached at a depth of 130 pm from the top of the channel or 100 plmfrom the bottom of
the channel. Thus, a large volume of tissue (nearly 43%) in the bioreactors with zero crossflow is exposed to hypoxic conditions. In contrast, in bioreactors with a cross-flow rates of
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even 40 pl/min (i.e lpl/min per individual channel), the minimum oxygen concentration in the
tissue (0.57) is significantly higher (Figure 3-13) than concentrations at which tissue hypoxia
effects have been reported in literature (0.36).
Max 1
1
0.9
250
0.8
200
0.7
150
0.6
100
0.5
50
0.4
0.3
0
0.2
-50
0.1
I
150
(a)
150
(b)
u
Mi 0.0473
0
Figure 3-12. Tissue distribution of oxygen in the limiting case of reactors with zero cross-flow
- Scenario 1
The oxygen concentrations are predicted to drop to a value lower than 0.36 at a depth of 130
pumfrom the top of the channel especially in the cells located right at next to the walls of the
channel (a) A large volume of tissue (nearly 43%) in the zero cross-flow reactors is exposed to
hypoxic conditions Figure 3-12(b) shows the oxygen distribution in the cylindrical porous
conduit. (Note: Symmetry reduces the problem to solving the concentration profile in onequarter of the channel and tissue occupying volume of 150 x 150 x 230 pn).
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Max:1
250
0.95
200
0.9
150
0.85
250
200
150
0.8
100
100
0.75
50
0.7
50
0
0.65
0
-50
-50
0.6
15
150
150
150
u
Mio.565
0
Figure 3-13: Tissue distribution of oxygen in bioreactors with cross-flow - Scenario 1
In reactors with a crossflow rate as low as 40 pl/min, the minimum oxygen concentration in
the tissue, even at the walls of the channel farthest from the flow eft side figure) is predicted
to be significantly higher (0.57) than concentrations at which tissue hypoxia effects have been
reported in literature (0.36). The figure on the right hand side shows minimal drop in
concentration in the cylindrical conduit (Note: Symmetry reduces the problem to solving the
concentration profile in one-quarter of the channel and tissue)
For the second scenario, the tissue volume was assumed constant independent of the
cell number (i.e. the radius of the flow conduit, was assumed constant and equal to 100 im).
The volumetric density (cells/m3 ) was calculated using the experimentally measured number of
cells. The average cell number per channel is calculated using the value of total cell number
obtained by measuring the total RNA (see Appendix) in the scaffold in the bioreactor. It
should be borne in mind that though there may be variations in the total cell number per
channel across different bioreactors - this now affects the cell density (cells/m3), but does not
affect the effective diameter of the porous conduit available for perfusion flow. Hence while
the Pe number remains constant even with differences in cell number, for the same crossflow
flow rate, the Damkohler number changes with changes in tissue volumetric density. A
characteristic length scale equal to the assumed tissue thickness (or of the cylindrical pore
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conduit diameter) of 100ptm, is used in this scenario. Table 3-2 describes this in greater detail.
Table 3-2 gives the cell numbers measured in the bioreactors and the calculated values of the
Pe and Da numbers for Scenario 2.
Table 3-2. Average viable cell numbers and non-dimensional parameters for modeling the
tissue distribution of oxygen in bioreactors with and without cross-flow - Scenario 2
Bioreactor
Avg. viable cell
(A)
(B)
Tissue
Effective
cross-flow
(ul/min)
number based
on total RNA
Avg. viable cell
number per
Fixed Tissue
Volume [m3]
Volumetric
Density
Tissue
length
[cells/m3] =
scale 't'
A/B
(gm)
measurements
channel
(n=4)
0
27275 +/- 6909
682
1.889 x 10-11
1
3.610 x 1013
100
40
38956 +/- 9677
974
1.889 x 10-"
5.156 x 1013
100
100
25111 +/- 5620
628
1.889 x 10-1
3.325 x 1013
100
230
32527 +/- 3250
813
1.889 x 10-11
4.304 x 1013
100
Diameter of
cylindrical
crossflow
conduit
(300 - 2*t)
Perfusion
velocity per
channel (m/s)
Characteristic Length Scale
used for nondimensionalization (pmr)
Peclet
Number
(Pe)
Damkohler
number
(Da)
100
0
100
2.121 x 10-
100
100
100
0.00
0.400
3
100
112
0.571
3
100
280
0.368
100
644
0.477
5.303 x 10-
12.197 x 10-
3
Note: In this scenario, the tissue volume in the bioreactor was assumed constant independent of the
number of cells. i.e. the volume corresponded to the space between the cuboidal channel volume (230
}m x 300 ptm x 300 tm) and the cylindrical annulus seen in Figure 3-2 (of diameter 100 u[m).Thus, it
was assumed that the cell-packing density changed to accommodate more cells within a given volume..
The conclusions drawn from 3-14 and 3-15 are exactly the same as the conclusions
drawn from the predicted tissue distribution of oxygen in Figures 3-12 and 3-13. Thus, both
scenarios lead us to the same conclusion - in the absence of crossflow, a large part of the
tissue is exposed to hypoxic conditions, while any crossflow rate greater than 40 ul/min
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through the forty channels of the bioreactor, helps satisfy the tissue oxygen demand and helps
maintain normoxic conditions in the tissue.
.. . It
n
0.0
E
100.
a.
..'
50
"'i
_j
0
1
.
.. 0
150 ..
200,
0.9
Z
0.8
'
"
0
:
.:"
250
L,
,...
0/ 100 50
9',- so
.
' ...
.
..
.:::
,..~.O
..
..
.
.... ··':':' -""" 150
O
0.2
0.1
:.
100
Rol
0
50
0.5
0.3
'
150
0.6
L..0.4
200
250,
150
z
0.7
w
o 150.
z
Max:1
.
100
. ....
50, .:
Z
: _01i" -.
.z-
u50,.
150
LOO
lt
V~
f4
Figure 3-14. Tissue distribution of oxygen in the limiting case of reactors with zero crossflow: Scenario 2:
The oxygen concentrations drop zero at a depth of 130 pm, as can be seen in the plane cross
sections of the channel, in the first figure. A large volume of tissue (nearly 43%) in the zero
cross-flow reactors is exposed to hypoxic conditions (Note: Symmetry reduces the problem to
solving the concentration profile in one-quarter of the channel and tissue occupying volume of
150 x 150 x 230 pm).
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m.I
a:
UI
9
u.U
F1 0
50
Cr
A
A
- 1200
150
. oz
0
0.9
MI
0.0
z
~95
1 50
0
81
0.95
uJ
200
0
< 075
250
:
O
07
0.75
O 0.6089
u1515
0 ' 50
0
MA0.7M
150-150
0
50
a) Cross-Flow Rate: 401uUmin
50
b) Cross-Flow Rate: 230 l/min
Figure 3-15: Tissue distribution of oxygen in bioreactors with cross-flow - Scenario 2.
In reactors with cross-flow rates in excess of 40 A/min,the minimum oxygen concentration in
the tissue (0.69-0.74) is significantly higher than concentrations at which tissue hypoxia effects
have been reported in literature (0.36). (Note: Symmetry reduces the problem to solving the
concentration profile in one-quarter of the channel and tissue).
High Pclet
numbers, seen for Scenario 2, result in convection-dominated profile
through the porous non-tissue conduit, which in turn translates to a negligibly small
concentration boundary at the tissue-fluid interface. Thus, the minimum tissue concentration
of oxygen becomes independent of Pclet numbers at values of Pe > 100. This results in all
the minimum concentration-Damkohler number curves in Figure 5-18 to collapse into a single
curve, at Pe>100. Similarly, at very low P6clet numbers (<0.1), the minimum tissue oxygen
concentration once again becomes independent of Pdclet number, as can be seen in Figure 316.
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Minimum Tissue Oxygen Concentration as a
function of the Damkohler Number (Thiele Modulus)
an
1.u
0.9
_
0.8
=
* '
0.7
0.6
'
C 0.5
E
C 0.4
0.3
a
0.2
0.1
0.0
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Damkohler Number (Da)
Figure 3-16: Minimum tissue oxygen concentration asymptotically falls to zero for large values
of the Damkohler number (Da).
Hypoxia-like conditions set in only at values of Da > 1.4 in the bioreactors with cross-flows
greater than 40 l.d/min(Pe= 112).
3.5 Measurement of the effect of crossflow
In order to validate the model mass transfer predictions within a tissue containing
channel, expression of hypoxia regulated genes Hif-3ot and Heme Oxygenase -1 were studied
as a function of the cross-flow rate (see Fig. 3-12 and 3-13, respectively). Both Hif-3a and
Heme Oxygenase-1 have been well studied in literature as hypoxia-responsive genes [97, 98].
The object of the gene expression analysis was to determine an appropriate range of crossflow rates at which tissue oxygen concentration profiles are well above those that induce tissue
hypoxic responses. The range of perfusion or cross-flow rates at which the shear stress
experienced by the cells in the tissue are physiologically relevant, while not being too high as to
adversely affect the tissue phenotype (determined by looking at the expression of CYP3A as a
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function of the crossflow rate - see Figure 3-17), were used to study the effect of perfusion
crossflow rate on oxygen distribution in the tissue.
I
I
22
40
40
75
105
230
105
230
Flow rates in ul/min
Figure 3-17: Effect of perfusion crossflow rates on tissue expression of CYP3A2.
Perfusion crossflow rates upto 230 ul/min are seen not to affect the expression of p450's.
All lanes normalized to total protein
Relative to the expression in isolated hepatocytes and the bioreactors with cross-flow,
the expression of Hif-3-c mRNA in the bioreactors without cross-flow is significantly higher
(Students t-test, p<0.05, Figure 3-18). The mass transfer model had shown the presence of
hypoxia-like conditions in a significant volume of tissue in the bioreactor with zero cross-flow,
relative to tissue structures in bioreactors with cross-flow (Figures 3-12 to 3-15). We believe
this may be responsible for the induction of Hif-3c in reactors without cross-flow (Figure 316). Induction of Hif-3 ocin response to mild hypoxia has been reported previously [97],
although the study had concluded that the induction of Hif-3c in in ivo experiments on rat
liver was not very significant. However, the duration of exposure to mild hypoxia in the
studies (up to two hours) was significantly shorter than in our bioreactor experiments (nearly
72 hrs.). Heme Oxygenase -1, which has been shown to be induced in response to mild
hypoxia [98], was induced nearly three-fold in the bioreactors without cross-flow relative to
those in non-zero cross-flow reactors (Figure 3-19). These results confirm the model
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prediction of hypoxia-like conditions in the bioreactors without cross-flow. This study did not
find Hif-la to be very responsive to hypoxia (datanotshown)at the mRNA level.
T
-I1
5.00
4.00
U'
C
2.
2.00
2.00
freshly isolated
T
C
IL
1.00
@
.00
· .2 0.00
@5
-1.00
1.
-2.00
I
I..................................................................................
Perfusion cross-flow rates through 40 channels
rul/minl
Figure 3-18: Expression of Hif-3-c mRNA in bioreactor cultures operated at different crossflow rates using RT-PCR
Induction of Hif-3-alpha expression relative to basal levels can be observed in reactors without
cross-flow (marked by *). The induction is statistically significant (at the 95% confidence level)
in zero cross-flow bioreactors, compared to the expression levels of Hif-3-ocin the bioreactors
with non-zero cross-flow rates. Results are from N=2 experiments with n = 2 bioreactors at
each flow setting in each experiment.
In the bioreactors with cross-flow in excess of 40
l1/min(Pe = 112, for Scenario 2),
the model predicts -onset of hypoxia-like conditions (defined by oxygen concentrations below
0.36) at Damkohler numbers greater than 1.5. However, in all experiments in bioreactors with
cross-flow, Damkohler numbers were in the range of 0.40-0.62 for Scenario 2- values much
lower than 1.5. Tissue oxygen concentration asymptotically falls to zero for large values of the
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Damkohler number (Da). (Figure 3-16). The intent of the RT-PCR studies was to identify
suitable range of cross-flow rates at which no hypoxia was observed in the channels of the
scaffolds. This was seen to be the case for all cross-flow rates in excess of 40 pl./min.
Nonetheless, die presence of non-parenchymal cells, such as the activated Kupffer cells, may
lead to an increase in oxygen uptake rate [99, 100] and hypoxia-like conditions. In these
experiments, Percoll centrifugation during cell isolation minimizes the concentration of nonparenchymal cells in culture.
_
,
I
.
4.U
0
3.00
o
ca
la
2.00
(3
1.00
co
O
o,..
0.00
-1.00
-00
-P
fl
Figure 3-19: Induction of Heme Oxygenase - 1 mRNA in bioreactor culture under hypoxia:
Heme Oxygenase - 1 is induced in reactors without cross-flow. This induction is statistically
significant (at the 95% confidence level, p < 0.04), compared to the expression levels of Heme
Oxygenase - 1 in the bioreactors with non-zero cross-flow rates. Data in (A) is from RT-PCR
studies. Results are from N =2 experiments from 2 different rats and n = 2 replicate reactors
at each flow rate in each experiment. Expressions in all bioreactors run with cross-flow at 40,
100 and 230 pl/min were comparable (data not shown). Data in (B) is from RatU34A
microarray expression data (seeChapter 5for details on basalgene expressionstudies)
3.6 Summary of key results and conclusions
The computational fluid mechanical model in ADINA
®
was used to describe the
velocity and shear stress distribution within the bioreactor. The model helped identify a set of
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crossflow (perfusion) flow rates that simulate physiological shear stress conditions at the tissue
surface within a channel of the bioreactor. The mass transfer model in FEMLAB® was used to
describe the tissue distribution of metabolite concentration gradients. The mass transfer model
helped identify appropriate bioreactor operating conditions (Peclet and Damkohler numbers)
under which the tissue oxygen demand was theoretically and experimentally validated to be
satisfied. The mass transfer model may also be used to determine the tissue disposition of any
metabolite, so long as the requisite model parameters - the Peclet Number and the Thiele
Modulus or the Damkohler number are known.
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Chapter 4
Basic Characterizationof Liver Tissue
Phenotype in the Bioreactor
Enriched rat liver hepatocytes were isolated from male Fischer F344 rats (Charles
River, Cambridge, MA), pre-aggregated into spheroids in spinner flask cultures and then
seeded into bioreactors. This chapter details the steps involved in the isolation, preaggregation, and culture of the primary rat hepatocytes. A schematic of the steps involved is
presented in Figure 4-1. Separate sub-sections in this chapter; give a detailed analysis of each
individual step.
Cell Culture Methods : Isolation-Seeding
Static cultures
,
.....
l
Iaay uj)r
(day 3)
Spinner
flask A
A
(control)
Bioreactor seedina
ds)
(
Morphogenesis
Within 1-2 days.
Hepatic functions
Isolation: Hepatocyte fraction
contains
-5% non-parenchymalcells
monitored
Albumin, Urea,
CYP450
expression and activity
In vivo liver
section
Figure 4-1: Schematic of the steps involved between the isolation and seeding of rat liver cells
into the bioreactor
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4.1 Isolation of primary rat hepatocytes
A schematic of the process of isolation of enriched rat liver hepatocytes is presented in
Figure 4-2. Hepatocytes were isolated from 150-230 g male Fischer rats with a modification of
Seglen's two-step collagenase perfusion procedure [101] as described previously.
2-step collagenase
I
Perfusion (Seglen et al)
Cell isolatic)n
l-
t
NPC's
&W
1. Intact Liver Tissue
2. Perfused Liver Tissue
3. Non-Parenchymal Cells
NPC's) + Hepatocytes soup
r
l
Add percol 140% (v/v)
5-10 million cells/ mL
Centr ifuge
3 times cP50g, 3 min.
Dead cells,
NPC's
Centrifuge, 50g 10 min.
Nearly
99% pure
hepatocyte
fraction
I
3. Non-Parenchymal
Cells (NPC's)
Hepatocytes. soup
4. Hepatocyte enriched
fraction (95% hepatocytes,
J
5% NPC's, Seglen etal)
Centrifuge
3 times @509
I
5.Hepatocyte enriched
fraction (99% hepatocytes)
Figure 4-2 A schematic of enriched rat liver hepatocyte isolation procedure
The resulting cell suspension was centrifuged three consecutive times at 50g (2 min
each). Next, 21.6 ml of Percoll (Sigma-Aldrich, St. Louis, MO), was mixed with 2.4 ml of
Hanks Balanced Salt Solution (HBSS, Sigma, MO), and added to 25 ml of the centrifuged cell
suspension at a cell density of 5-7 million cells per milliliter of solution, as described in the
literature [102]. The solution was further centrifuged at 50g for 10 minutes. Percoll isogradient
centrifugation resulted in the separation of dead cells as well as a significant portion of the
non-parenchymal cells in a floating top layer that was discarded. The sedimented hepatocytes
were then re-suspended in hepatocyte growth medium (HGM) [103], but without hepatocyte
growth factor (HGF). An error was reported in the original HGM formulation reported by
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Block et al. [103]. The concentration of Niacinamide, and those of heavy metal salts were
incorrectly reported in the original reference. Thus, the media formulation used for all
experiments in Chapters 5, 7, and 7 of this thesis used the corrected media formulation. The
concentration of nicotinamide or niacinamide was corrected to 0.305 g/L from the original
concentration of 0.61 g/L ZnC2 concentration in the final formulation was reduced by tenfold to 0.0544 mg/L. Similarly ZnSO 4*7H 2 0 concentration was reduced ten-fold to 0.075
mg/L.
CUSO4*5H2O concentration
was corrected to 0.02 mg/L
from the original
concentration of 0.20 mg/L. Glutamine concentration was reduced to 1 mM from an original
concentration of 5 mM. Gentamycin and HEPES were not used in the media, while Penicilli
and Streptomycin were used as bacterial antibiotics in the media. Other components in the
media were the same as those reported in [47, 89]. The final cell viability, as determined by
trypan blue exclusion, was approximately 90-95% at the end of the isolation procedure.
4.2 Formation of multicellular spheroidal aggregates
Thirty million percolled primary rat hepatocytes were added to 100 mL of HGM in a
500 mL spinner flask (BellCo, NJ, USA) on a spinner table set at 85 rpm [20]. The cells
aggregated to form spheroids. Flasks were taken down either on Day 2, Day 3 or Day 4 after
cell seeding, for seeding the spheroids into the bioreactors. Spheroids of the desired size range
(100 - 300 pun) [47], were separated using appropriately sized filter meshes (Sefar America,
Kansas City, MO) and re-suspended in 25 mL of rinse medium. The rinse solution comprised
phenol red-free Dulbecco's modified Eagle's medium (DMEM) with sodium pyruvate (110
mg/mL) and glucose (1 g/L) (Life Technologies, Rockville, MD) supplemented with bovine
serum albumin (2 g/L, Sigma, St. Louis, MO) and penicillin-streptomycin (100 U/mL). The
size-separated spheroids were centrifuged at 40g for 3 min. The floating debris was then
removed and the spheroid pellet was re-suspended in 30 mL of HGM. Previous work in the
lab has indicated minimal differences in tissue phenotype, assessed by measurement of
albumin and urea secretion rates in reactors seeded with Day 2, Day 3, or Day 4 spheroid
seeded reactors 1471].
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4.3 Bioreactor assembly and cell-seeding
imaging & spectroscopy
vent flilt
Figure 4-3: Schematic diagram of the bioreactor fluidic system
The design and assembly of the microfabricated bioreactors have been described in
detail in Chapter 2, as well as in earlier published literature [47, 89, 104]. The bioreactor system
was primed with rinse solution (comprising phenol red-free Dulbecco's modified Eagle's
medium (DMEM) with sodium pyruvate - 110 mg/mL and glucose - 1 g/L (Life
Technologies, Rockville, MD) supplemented with bovine serum albumin - 2 g/L, (Sigma, St.
Louis, MO) and penicillin-streptomycin at 100 U/mL) to passivate the bioreactor, connector
and tubing surfaces, and to remove air bubbles from the flow paths. Prior to seeding, small
volume conical glass reservoirs were aspirated and refilled with 2 mL of HGM. For
hydrodynamic seeding, a syringe filled with 1 ml cell-spheroid suspension was placed at the
inlet of the upper chamber (port #1) and a second syringe containing -0.5 ml of PBS was
placed at the bioreactor upper outlet (port #2) (see Figure 4-1). The bottom bioreactor outlet
(port #3) was unclamped by removing the tubing from the peristaltic pump rotor and the
bioreactor was tilted approximately 450 with the bioreactor outlet (port #2) higher than the
inlet (port #1). On the third day, spheroids of the desired size range (100 - 300 Lm)were
separated from the spinner flask cultures, using appropriately sized filter meshes (Sefar
America, Kansas City, MO) and re-suspended in 25 mL of rinse medium. We have previously
shown better functional tissue phenotype in reactors seeded with spheroids rather than with
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individual cells [47]. The cell-spheroid suspension was manually injected into the upper
chamber at the flow rate of -0.5 ml/min. The spheroids entered into the scaffold channels
through a combination of settling and flow of cell culture medium from the upper chamber
into the lower chamber aided by the resistance of the syringe on the bioreactor upper outlet
(port #2). Following seeding, cell culture medium was pumped through the upper bioreactor
chamber at 0.5 ml/min (with pump A) and collected in a waste container for one minute to
clear cells from the top surface of the scaffold.
The upper recycle tubing was then
reconnected to the bioreactor (port #2). At this time, the bottom tubing was inserted into the
peristaltic pump B and the perfusion flow rate (cross-flow) down through the seeded channels
was set to a desired flow rate. The selection of flow rates was based mainly on being able to
simulate physiological shear stress conditions, while satisfying tissue oxygen demand, in the
tissue units in the scaffold channels. The choice of these flow rates has been described in detail
in Chapter 3. After one hour, cell culture medium in the reservoir was replaced with 2 ml of
fresh medium to reduce residual cells/debris trapped in the circulation loop.
Spheroids seeded into the channels are initially held in place by the membrane or filter,
and after initial attachment and reorganization (-1 day), by adhesion to the collagen-coated
channel walls. During the "top down" perfusion, the residual cell debris is likely to gradually
clog the micro porous filter. To maintain a constant perfusion rate and keep the filter in the
bioreactor free of debris, 24 hours after the seeding we placed an inline filter between pump B
and the reservoir, and reversed the direction of the cross-flow while maintaining the same
value of the perfusion or crossflow rate. We kept this direction of the cross-flow for the
duration of the experiment. For five-micron pore-size DURAPORE filter (Millipore Corp.,
Bedford, MA) in the bioreactor, we used a 0.8/0.2-pm pore-size double layer inline syringe
filter (Pall Corporation, Ann Arbor, Ml). In this way, cell debris capable of clogging the filter
in the reactor was captured in the inline filter. By replacing the inline filter every 72 hours, we
managed to maintain a constant "bottom up" cross-flow through the channels with cells.
This helps clean the filter out of debris and maintains the crossflow or perfusion
through the tissue. Figure 4-4 shows the removal of debris from the bioreactors as a result of
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the reversal of crossflow, 24 hours after cell-seeding into the channels of the reactor. In the
absence of such a flow reversal, the crossflow through the tissue (the pull-down crossflow) is
seen to fall to zero quickly past 24 hours, possibly due to the clogging of the Durapore
membrane filter placed between the two silicon scaffolds.
Cleaning Effect of Crossflow
(a)
(b)
-
45 min. post flow reversal
Figure 4-4: Cleaning effect of crossflow
Removal of cell debris from the membrane filter sandwiched between the silicon scaffolds, as
a result of reversal of crossflow 24 hrs. after cell seeding (a) before reversal and
(b) after reversal of crossflow
4.4 Evaluation of hepatic phenotype: albumin and urea secretion
In order to assess the basic hepatic phenotype of the tissues formed in the MilliF
reactor, albumin (Figure 4-5) and urea secretion rates (Figure 4-6) were measured in the
bioreactor cultures. Bioreactors were assembled and seeded with Day 3 spheroids and cultured
upto Day 14 (post cell isolation).
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ALBUMIN
35
2
30
H_=
R2 -0.997
-_ _
·I:· ..
25
20
15
;91·
bi
7.82
t 0.51
n:
3
10
5
0
0
nged
2
4
6
8
r.4 I_, :,
'A
- Z
10
,
~
12
14
"I,'
Figure 4-5: Albumin secretion rates in bioreactor cultures
Serum albumin concentrations of medium samples were determined by a sandwich
enzyme-linked immunosorbent assay (ELISA) [105]. Polvclonal sheep and-rat albumin IgG
(ICN, Costa Mecsa, CA) and horseradish peroxidase-conjugated goat anti-rat albumin lgG
(Accurate Chemical, Westbury, NiY) were used in these assays. Absorbance values were
measured with a SpectraNAX 250 microplate specrrophotometer (Molecular Devices). Total
albumin content in bioreactors or static cultures was determined by multiplying the measured
albumin concentration
by the total medium volume at the time of sampling. Urea
concentration was assessed bv Berthelot determination methods (urea nitrogen kit from Sigma,
procedure 640). Both the urea as well as the albumin data was normalized using measured
values of total DNA from the reactors (See Appendix for protocol).
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Biote~chnologyProcess Enineering Centerr
, 40'--~-
<
-1
u
o 120 -:
i
o
:3.
.100
C
i
I
'0o 80
60
40
-)
t o40
-
E
0 O------0
2
;
4
6
8
10
=
12
14
Days in Culture
Figure 4-6: Urea secretion rates in 3D bioreactor cultures
Both albumin as well as urea secretion rates measured in the MilliF bioreactor were
comparable to those seen in the macroreactors (Figures 4-5 and 4-5). In addition, on the basis
of urea and albumin secretion rates, it may be cautiously concluded that the tissue phenotype
in the MilliIiFreactors are comparable with those seen in the macroreactors.
4.5 Ultrastructural evaluation of tissue phenotype
Transmission Electron Micrographs of the tissue sections were prepared by standard
protocol. The electron microscopy was done in collaboration with Dr.Donna Stolz of the
University of Pittsburgh Medical Center. Scaffolds were fxed in 2.5/o glutaraldehyde in PBS
for 24 hr., washed three times with PBS, and post-fixesd for 1 hr with aqueous 1% osmium
tetroxide. After three PBS washes, scaffolds were dehydrated through a graded ethanol series,
and then further dehydrated in four 15-min changes of 100% ethanol. Scaffolds were
subjected to two 10-min incubations in propylene oxide, and then preembedded with a 1:1 mix
of propylene oxide: Polybed 812 epoxy resin (Polysciences, Warrington, PA) for 1 h. Scaffolds
were then incubated in 100% Polybed overnight at 40 (,. The following day, resin was changed
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four times before embedding chips in a thin layer of resin, just enough to fill the channels, and
curing at 370 C overnight and then at 650 C for an additional 2 days. Cells growing within the
channels were removed from embedded chips by rapid, alternating treatments in liquid
nitrogen and boiling water. This treatment shattered the scaffolding and allows for embedded
cells and channels to be removed intact. Blocks of cells were re-embedded in rubber molds
and cross-sectioned perpendicular to channel flow. Thin sections (60 nm) were collected on
copper grids and stained with 4% uranyl acetate in 50% methanol for 7 min and with 1%'olead
citrate for 10 min. Cells were viewed with a JEOL (Tokyo, Japan) JELI 1210 transmission
electron microscope (TE'M at 80 kV. Figure 4-7 shows a representative TENMof bioreactor
tissue thin sections.
1 micron
I micron
'(IhalltCEge
BC
5
Cell -Cell
microns
J
Ulctil.
ER.
.
ER .:
I
ECM
_0
I....,
N
'
N
, 1r
.:tk.$,
_;L--
~s-.
_
Figure 4-7: TEMI's of tissue phenotype in the bioreactor.
Presence of a large number of mitochondria (NI), rounded nuclei (N), bile canaliculi (BC),
endoplasmic reticulum (ER), extra cellular matrix material (ECM) and cell-cell junctions are
indicative of a healthy tissue phenotype.
Scanning Electron Mlicrographs of the tissue structures formed in the reactors are
presented in Figure 4-8. The presence of a large number of microvilli (M in Figure 4-8, (e)),
are indicative of the presence of metabolically active, functional hepatocytes. The presence of
some endothelial cells at the interface between the hepatocytes and the fluid flow conduit in
the center of the channel (Figure 4-8, (a), (b)) is suggestive of heterotypical re-organization. In
some cases, a layered-rearrangement of different cell-types is seen in the tissue, reminiscent of
~\ lassahusetts
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the in ivo tissue phenotype (See Figure 4-8, (c), (d), (e)). However, these results are not
typically seen in all cultures and detailed quantitative histological and microscopic studies will
be needed to characterize the localization of the various cell types present in the hepatic tissue
formed in the 3D bioreactors, once a technique to identify and characterize the various cells
that constitute the tissue phenotype is developed.
Figure 4-8: SEM's of tissue structures formed in the micro channels of the bioreactor
Bioreactor cultures were prepared at Cambridge, KN[and the scaffolds with the tissue
structures were fixed and shipped to Pittsburgh. For the SEIM's on the bioreactor, scaffolds
were removed from the bioreactor and fixed in 2.5%ioglutaraldehyde solution for I hour, and
washed three times with PBS, each wash lasting for 15 min. The scaffold was then incubated
with 1
Osmium Tetroxide for 1 hour, followed by 3X PBS washes (15 min each). The
scaffold was then fixed in Thiocarbohydrazide (TCH) for 30 min, followed by three successive
washes (15min each) in PBS. The scaffold is fixed in Osmium tetroxide for
hour. This is
followed by another set of PBS washes (15 min, 3X). The scaffold is once again fixed in TCH
for 30 min, followed b
the PBS washes as before. This is followed by a final Osmiun
Tetroxide fixing step (1 hour) followed by the 3X PBS washes as before. Serial ethanol
Biotechnology Process Engineering Center
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dehydration steps (30°'o, 500%/,70%, 95°'o Ethanol for 15 min each) followed by four
successive incubation steps of the scaffold in Hexamethylenedisilazane (HMDS)-ethanol
solution (250o, 50%, 75% and 100% HNMDSsolution in ethanol, 15 min each). Most of the
HMNDSis aspirated out of the dish till a very small laver of solution barely covering the
scaffold is left behind. The scaffold is stored in a desiccator till completely dry.
Histological sections stained with toluidine blue are shown in Figure 4-9. Viable cells
take up the toluidine blue stain and are colored dark blue. Serial sections taken from many
different channels of bioreactors from different experiments confirm the presence of mostly
healthy and viable cells in the tissue.
OUS
Ithy
ue:
jidine
e staining
j.
50 -mi
t. ,~~~~~~~~~~I
Figure 4-9: Toluidine blue stained sections of the liver tissue from a bioreactor channel
4.6 Maintenance of p450 isoforms in the 3D bioreactor culture
As described in Chapter 1, the maintenance of the basal expression levels of the
CYP450 gene and protein expression is an important pre-requisite of any in vitroscreen used
for predictive drug metabolism and toxicology. The maintenance of the basal gene and protein
expression of a number of CYP450's of Pharmaceutical relevance was assessed in the MilliF
1echnologv
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bioreactor. While the eventual goal is to be able to measure and quantify the actual rate of
metabolism of drug-like compounds in the bioreactor, preliminary experiments focused on
assessing the ability of the bioreactor to maintain the expression of the Phase I drugmetabolizing enzymes (CYP450's) in the bioreactor culture (Figure 4-10), and the ability of the
culture to induce CYP450's in response to the addition of prototypical inducers by Western
Immunoblotting (see Appendix for detailed protocol).
In addition the repeatability of the
cultures system was tested across different experiments as well as across bioreactor cultures
I 'I-l~~
from the same experiment.
Cells after
Reactors
isolation
CYP2B1,
'
CYP2B
Day3 Day7
Spheroids
10
13
Figure 4-10: Maintenance of expression of CYP450 2B protein for at least upto two weeks in
bioreactor culture.
(All lanes have beennormalied for the same amount of totalprotein)
The expression of both Phase I and Phase 11enzymes were seen to be very repeatable
across different bioreactor cultures (bioreactors from both the same experiment as well as
from multiple experiments showed comparable expression of CYP proteins as well as Phase II
proteins).
Figure 4-11 shows the repeatability both across experiments as well as across different
bioreactor cultures, of the expression of an important Phase I protein, CYP4503A2 and a
Phase II enzyme UGT1 A (UDP glucuronosyltransferase-lA).
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Expt.1, Expt. 2
Rxtr 1, Rxtr 2
n ,"
:7
Day 7
M'm
7
- av
- Y
Dhoeo
rI IILO
11
I11
enzyme:
UGT 1A
CYP3A2
Figure 4-11: Repeatability of p45 0 expression in bioreactor cultures
The bioreactor cultures are seen to be pretty repeatable in their expression of kev Phase I and
Phase II ezvme proteins (Westerns), both across reactor cultures as well as across
experiments.
4.7 Qualitative spectroscopic measurement of p450-lA activity
A specutrometric system, designed in hourseby Dr. Karel Domansk 3 , (See Figutre 4-12),
was used to measure in situ, the maintenance of cytochrome P450 1A (CYP4501A) in
bioreactor-cultured rat hepatocytes.
thermalshield
lightsourcerEX
filter
metallcwavegude
aluminumheal sink
ng bases&slandoffs
Figure: 4-12: Spectrometric system used for in .rsitu
spectroscopyr of the 3D perfused tissue
(Photographly Dr:Karte/Domansk-y)
nsiut o ichokg:
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EROD assay for P4501A
i
o
EROD
A
Assay
n
'
-1
Phase II Conjugates
Resorufin
Ethoxyresorufin
* 120 min. exposure to 8.3 FM 7-ethoxyresorufin,
10 p[Mdicumarol
* 48 hr. induction, 5 tM 3-methylcholanthrene
Figure: 4-13: The EROD Assay to measure the activity of P4501A
Ethoxvresorufm has low fluorescence but it is transformed into a highly fluorescent
form, resorufin, in the presence of CYP4501A. This is the standard Ethoxyresorufin-ODealkylation (EROD) assay shown in Figure 4-13. The absorption and emission peaks of
resorufin as well as the optical filters we used in the assay are shown in Figure 4-14. First, the
signal from a primed bioreactor with only the albumin-free cell culture media and without the
cells, was measured (Figure 4-14 b, black line). This is essentially the bleed through of the
optical filters. Then, the signal from a bioreactor culture with the tissue structure but without
any Ethoxy Resorufin added was measured (blue line). The small increase in the intensity level
can be attributed, for example, to autofluorescence of the cells. Then, ethoxyresorufminwas
added to the cell culture medium at a high substrate concentration (10 IM)and the fluorescent
product resorufin (red line) one hour after adding the substrate. In the control experiment
(Fig. 4-14c), a bioreactor without cells was used and ethoxyresorufin was added to it prior to
the measurement of signal.
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__··___
b)
Intensity (counts)
Spheroids Day 3
500
40( Hepatocyte
growth
300 medium
a)
_______a)
ethoxyresorifin
5
I1A
IA··-
200--(no cells)
200
resorftrill
100
I
ii
500
I
ell,
,
I
550
600
Wavelength
C) Intensity (counts)
* ~~~C)
00
400
650
(nm)
700
Control
measurement
without cells
I
II
300
Hepatocyte
growth
200 medium
100 (no cells)
i
i
0
I
500
550
600
650
700
wavelengtn nim)
WAVELENGHT[nm]
Figure 4-14: Detection of CYP activity using a fluorescence detector
(a) The emission and excitation spectra of Resorufin, with peaks at 571 nm and 585 nm
respectively. (b) Signal due to the formation of resorufin in the bioreactor as a result of the
addition of the C(\T1YAsubstrate -Ethoxvresorufin. (c) Control reactor with cells but without
the addition of Ethonvresorufin.(Experiment
dlonewith ResearchScientist Karel Dovnansky)
4.8 Summary of key results and conclusions
Bioreactors were cultured with primary rat hepatocytes upto two weeks in culture.
Albumin and urea secretion rates were seen to be comparable with rates previously reported in
the macroreactors. Preliminary microscopy data and electron micrographs reveal the presence
of a viable, healthy hepatic tissue phenotype in the channels of the bioreactor. The tissue
constructs formed in the bioreactor are seen to maintain the basal expression of many of the
important, pharmaceutically relevant CYP450's upto two weeks in culture. The bioreactor
fluidics was also optimized to maintain a constant perfusion crossflow rate through the
channels of the bioreactor. The activitv of one of the (C-1Y450'swas qualitatively measured
using a spectrophotometer and a non-fluorescent substrate Ethoxy Resorufin that is converted
to a fluorescent product Resorufin.
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Chapter 5
Measurement of Xenobiotic Metabolism in 2D
and 3D Cultures
5.1 Challenges in the measurement of intrinsic reaction rates in a 3D system
The basic design of the microfabricated bioreactor scaffold-sandwich that houses the
tissue constructs was explained in detail in Chapters 2 and 3. While the three-dimensional
nature of the tissue helps recapitulate aspects of the in zito tissue morphology, it may also lead
to the creation of concentration gradients within the tissue of specific metabolites, media
components, products of metabolism, and xenobiotics, due to the presence of a duffusional
mass transfer resistance in the 3D tissue. Thus the experimentally measured apparent rates of
metabolism of xenobiotics will be different from die intrinsicrates of metabolism within a
single cell. The mass transfer model described in detail in Chapter 3 may be used to predict the
tissue concentration distribution of metabolites, using appropriate parameter values for the
Thiele modules (or the Damkohler Number) and the Peclet number. The same mass transfer
model may also be used to predict the itrinsic rates of metabolism, given the apparent rates of
metabolism calculated experimentally. For the purposes of this thesis, all experiments were
performed using high substrate concentrations of drug compounds (xenobiotics). Thus the
theoretical value of the Damkohler numbers (thiele modulus) were sufficiently low, so that the
enzymatic reaction rate in a cell in the tissue was the rate determining step at steady state (i.e.
the after characteristic time scale for diffusion of the drug to the enzyme site was reached,
typically on the order of a few minutes for a tissue length scale as large as 200 tm). This is
explained in detail with specific model predictions in Section 5.6.
In addition, while measuring the specific hydroxylation rates of hydrophobic drugs, a
significant problem is posed by the drug binding to albumin in the media and to various parts
of the reactor and fluidic circuit. Thus, it is imperative that albumin-free media be used for all
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drug-metabolism measurement studies. In addition, paralyne coating of the reactor fluidic
circuit helps minimize the adsorption of testosterone to the reactor and fluidics.
Also, it is imperative that the total volume of the reactor and fluidics be minimized to
ensure that the system is well-mixed during the course of the drug metabolism studies, when
multiple samples at different time points may be needed to validate the linearity of the
Michaelis-Menten enzyme kinetics. A well-mixed system allows for small sample volumes to
be taken at multiple time points from any port/ location of the reactor and its fluidics. A wellmixed system with a small average residence time of a metabolite, allows for systemic
equilibration of local concentration changes over a time scale much smaller than the time
between two consecutive sample points. On the other hand, once the linearity of the kinetics
has been established, a single sample maybe taken at the end of the experiment for any new
experiments. If the entire reactor fluidic volume is collected, there may not be a need to ensure
that the reactor is indeed well-mixed - as the collection of the entire reactor volume would
ensure that all of the products of metabolism are completely collected and quantified
cumulatively at a single time point, so long as the Michaelis-Menten rate of product formation
is linear upto that time point. Minimizing the reactor and fluidics dead volume also helps
reduces the loss of products of metabolism to the unusable non-organic portion of the media
through downstream vacuum drying and other sample processing techniques prior to its
injection into an HPLC-UV separation and detection system. The quantity of the un-usable
non-organic portion of the media solution has been known to scale with the quantity of media.
Thus, minimizing the volume of the fluidic circuit can help increase sensitivity of the assay by
minimizing loss of products of metabolism to the non-organic phase during solvent extraction.
5.2 Paralyne coating of the fluidic circuit
A large fraction of the hydrophobic drugs such as testosterone and diazepam that were
used as substrates for the CYP450's in the bioreactor, adsorbed to Silicone® and CFLEX®
tubings used in the reactor fluidic circuit. In addition, the drugs adsorbed to the inline filter
and a negligibly small fraction adsorbed to the Durapore® filter used in the scaffold sandwich.
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More than 70% of the hydrophobic drug, was lost through a combination of non-specific
binding and adsorption in empty control reactors with no cells seeded in them (Figure 5-1).
A number of different tubing materials were evaluated - however, the tubings that
were seen to be inert to the hydrophobic drugs and thus did not bind/ adsorb/ absorb them,
were inflexible and hard - properties that made them unsuitable for use in peristaltic pumps.
The fluidic circuit in the bioreactor set up consists of CFLEX® peristaltic pump tubing and
Silicone® tubing. The Silicone tubing was replaced by an inert Teflon® HPLC tubing, to
eliminate loss of drug to these tubings. The peristaltic CFLEX® pump tubing was passivated
by a layer of paralyne that was coated by a vapor deposition process at Advanced Coating, Inc.
(Cucamonga, CA).
Drug/ Substrate adsorption to silicone and CFLEX tubing
Adsorption of diazepam and its metabolites to silicone tubing
a
SII
II
0
iiL
C
C
0
0Ca,U
0
20
40
60
80
100
120
140
160
180
200
Time after addition of drug
Figure 5-1: Loss of hydrophobic drug to adsorption, absorption and non-specific binding in
empty reactors with no cells seeded
Parylene is a common generic name for a unique series of polymers based on
paraxylene. The three most common types of parylene are referred to as: Parylene N, Parylene
C, and Parylene D. The parylenes are formed by the pyrolysis of a di-p-xylene (dimer) in a
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vacuum environment which is then deposited on a cooler (i.e. room temperature) substrate
under continuous vacuum. Vapor phase deposition of the parylene polymer allows it to be
formed as a structurally continuous film which is truly conformal to the design and structure
of the substrate upon which it is being deposited. Paralyne N, with a chemical structure as
described in Figure 5-2, was identified as a suitable passivating agent as it affords the greatest
surface protection.
Figure 5-2: Parylene N was used to passivate the pump tubings.
The parylene N polymers is deposited from the vapor phase by a process which in
some respects resembles vacuum metalizing. Unlike vacuum metalization, however, which is
conducted at pressures of 10-5 torr or below, the parylenes are formed at around 0.1 torr.
Under these conditions the mean free path of the gas molecules in the deposition chamber is
in the order of 0.1 cm. Therefore, unlike vacuum metalizing, the deposition is not line-of-sight
and all sides of an object to be encapsulated are uniformly impinged by the gaseous monomer.
This is responsible for the truly conformal nature of the coating. The passivation process
consists of three main phases - the vaporization, pyrolysis and deposition processes as
described in Figure 5-3.
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OAIFour
_Or~A4nM W
VkLq4-%Mvxfi
IDIOMq
YVAPMOE
VACUU Pim
Figure 5-3: Steps in the conformal deposition of Parylene N on CFLEX ® tubing
Parylene thickness is a function of the amount of vaporized dimer and chamber dwell
time and can be controlled accurately to within +/- 5% of targeted thickness for most typical
applications. A coating thickness of at least 100 im was used during the deposition process.
Due to the nature of the deposition process, the thickness of paralyne was seen to taper
towards the center of the tubing. Thus, short tubing lengths of not more than 20 mm were
used to ensure a uniform pin-hole free coating. In addition pump tubings less than 0.03" could
not be passivated uniformly - hence a crossflow pump tubing of 0.03" ID was used.
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Paralyne coatin
of tubino to minimize/ prevent
adsorption
Adsorption of testosterone on paralyne coated tubing
1.2
0
byI
|
. ·
-
--
-
--
E
i
----
II
C
0.8
IC
0.6
0.4
O
C
0
0
0.2
CO)
0
0
100
200
300
400
500
600
Time (min)
Figure 5-4: Minimal loss of hydrophobic drug to Parylene N coated tubing
An empty reactor without cells now assembled with the Parylene coated pump-tubings
and the Teflon" tubings were seen to absorb/ adsorb/ bind to not more than 2% of the
hydrophobic drug as well as its products of metabolism (Figures 5-4 and 5-5).
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Paralyne coating of tubing to minimize/ prevent adsorption
Adsorption of products of testosterone metabolism on
paralyne coated tubing
1.2
;i
C
_
__-----------___
'U
2 0.8
Cr
C 0.6
O
'IC)
'U 0.4
0.2
0
0
100
300
200
400
500
600
Time (mln)
Figure 5-5: Minimal loss of hydrophilic products of metabolism due to
Parylene N coated tubing
5.3 Minimizing the dead volume in the bioreactorand fluidics
Three concurrent strategies were employed to minimize the dead volume in the
bioreactor. The advantages and disadvantages of each of these strategies are discussed in detail
below:
5.3.1. Reservoirless fluidic circuit
The original design of the bioreactor fluidic circuit included a reservoir with a dead
volume of 15 ml and a tubing network with a dead volume of nearly 2 ml. A re-designed
fluidic circuit that did away with the reservoir, so that the dead volume in the tubing acted as
the "reservoir" instead, was used in an attempt to minimize the total dead volume of the
fluidic system. Figure 5-6 shows the re-designed fluidic circuit. Additionally, smaller bore
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Teflon® (0.007", Alltech, PA) tubings were used in a very large portion of the tubing network
instead of the 0.062" silicone tubing.
Minimization of dead volume in Reactor and Tubing
Original Configuration - Reactor and Fluidics
Reactor + Tubing dead volume: 17.5 ml
Reservoir less Configuration - Reactor and Fluidics
Reactor + Tubing dead volume: 360 ul
Figure 5-6: Reservoir-less reactor fluidics configuration to minimize dead volume
5.3.2. Small reservoir, small bore tubing fluidic circuit
While the reservoir-less reactor configuration helped minimize the total reactor and
fluidics dead volume, it posed serious oxygen equilibration and de-gassing issues. Exchange of
oxygen between the ambient air and the media in the tubing, was minimal as the Paralyne
coating minimized the transport of oxygen through the tubing. Additionally Teflon® also has
low oxygen permeability. Thus, in the absence of the reservoir, there was a possibility that the
media solution used in the reactor fluidics was not saturated with oxygen, as it entered the
bioreactor ports. In the earlier fluidics design, oxygen exchange was made possibly by the
presence of both oxygen permeable Silastic®tubing as well as a 15 mL reservoir.
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To address this issue, the fluidic circuit was re-designed to now incorporate a conical
glass (oxygen permeable and with a re-sealable Teflon septum) reservoir that housed 2 mL of
media saturated with oxygen following equilibration with the ambient air, while continuing to
use the low bore size Teflon ® tubing. The use of very small diameter tubing in the reactor
fluidics necessitated the use of medium pressure rated HPLC fittings (Alltech, PA)
Figure 5-7: Components of the two pump, small reservoir fluidic circuit
(a) The Paralyne coated pump tubing and small bore Teflon® tubing circuit (b) A view of the
microreactor in the fluidic circuit (c) The medium pressure water union HPLC fitting and
inline debris collection 0.8 [tm/0.2 itm filter and (d) the small-volume conical glass reservoir
with a re-sealable Teflon® septum on top.
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The reduction in the total dead volume of the reactor and fluidics helped improve the
sensitivity of detection of the system, especially for metabolites that were produced in very
small amounts, in response to the addition of specific drugs to the system.
5.4 Measurement of xenobiotic metabolism in 2D collagen sandwich (2DCSW)
and 3D Bioreactor (3DB) cultures
Saturation concentration of a typical xenobiotic (i.e. a concentration at which the
Michaelis-Menten rate is zeroeth order with respect to substrate concentration) were used in
the measurement of specific p450 enzyme activity in the 2D collagen sandwich cultures as well
as in the 3D bioreactors. Testosterone was chosen as the candidate xenobiotic, as it is
metabolized into many different hydroxylated products by a large number of CYP450's [106].
Quantitative analysis of metabolism in in-vitro cultures
Measurement of Enzyme activity using candidate drugs/ substrates
OH
Metabolism of Testosterone
1l-OH-testosterone
H
i\--
.8I
%.e
2a/p-OH-testosterone
l'
- 6P-OH-testosterone
1%-~
_
.
.
OH
4,1/.Z
1 6-0O H-testosterone
15D-O H-testosterone
Not a major metabolite in
isher rats (Caldwell et al,
androstenedione
1996)
Measurement of Steroid hydroxylation, Methods in Enzymology, VoL206,Sonderfan, Waxman et al : Sprague-Dawley
Figure 5-8: Testosterone biotransformation pathway in the rat liver
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In addition, testosterone metabolism has been well characterized and understood in
literature in a number of different in vitrocultures [86, 106-120]. The important components of
the biotransformation pathway of testosterone are reproduced in Figure 5-8.
Two dimensional collagen sandwich cultures were prepared similar to previous
methods suggested by Dunn and co-workers and later reviewed extensively in literature [4, 8,
18, 24, 44, 121, 122]. 35 mm tissue culture treated six-well plates (Falcon, USA), were coated
with 600 1l of a collagen mixture (4.7 ml sterile water, 1.3 ml of modified PBS solution [lg
glucose, 1.8 5g sodium bicarbonate, in 50 mL PBS solution], 6.9 ml of a 3mg/ml collagen (type
1) solution (Vitrogen, USA)), and with pH of solution adjusted to - 7.4. Plates were coated
overnight to allow the collagen to gel in an 8.5% C02, 37'C environment. Percolled primary
rat hepatocytes were seeded at a density of 50,000 cells/ cm2 , and evenly distributed on the
plate, in lmL of Hepatocyte Growth Medium (H-GM). Following an overnight incubation at
37°C and 8.5%C02, that allowed the cells to attach to the substratum, the media was aspirated
from the plate and 300 ul of the collagen mixture was added to form the second layer of the
sandwich. The collagen was allowed to gel over a 45 -60 min period, following which 1 mL of
HGM was added to the well. The cultures were incubated in a controlled 37°C, 8.5%CO2
environment. Media were changed everyday.
3D Bioreactors were assembled and seeded as described in Chapter 4, Section 4.3. On
the day when xenobiotic metabolism experiments are to be performed on the 2D culture and
the 3D bioreactor, fresh albumin-free Hepatocyte Growth Media (HGM) was added to both
the 2DCSW as well as the 3DB cultures. The 2DCSW cultures were shaken gently on a shaker
kept inside an incubator that provided a controlled 37°C, 8.5%C02 environment. Media was
changed twice in the 2DCSW at 30 minute intervals to help wash out the albumin present in
the collagen. This can help minimize the loss of drug or xenobiotic due to equilibrium binding
with albumin. Media were changed twice, again at 30 min intervals, in the 3DB to dean out the
rat albumin present in the system. 250 VM concentration of testosterone, that corresponds to a
concentration that saturates the p450's involved in the metabolism of testosterone [117, 118,
120] was made up in fresh albumin-free media solution, and was added to the 2DCSW
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cultures as well as the 3DB. Products of metabolism of testosterone were collected 60 min.
after the addition of the substrate to both the 3DB and the 2DCSW cultures. The use of the
shaker with the 2DCSW helped eliminate concentration gradients in the media. Linearity of
the Michaelis-Menten kinetics was verified over this time-span. To the samples from the
2DCSW and 3DB cultures, a known concentration
of an internal standard (Methyl
Testosterone) was added. The samples were them vacuum dried (SpeedVac, Thermo Electron
Corporation, hA) and testosterone and the hydroxy-products of testosterone were extracted
from the dry powder using methanol as solvent. A schematic of the sample processing steps is
detailed in Figure 5-9.
HPLC-UV detection and quantitation of hydroxylated products
Quantifying intrinsic rates of metabolismof testosterone
in 3
Bioreactors - Recycle Mode, and in 20 sandwich cultures
AM >> Km
c/reeunadsorbed
= 250
testosteronem
..
..
11
HPLC- UV
Detection
Samplef
of
reactor or ZD
cultures
internal std.
Concentrate scample under
vacuum - dry 1to a powder
meTQDoIIsm Una ilTernal
std using 50 -100 ul
methanol
2
5
6IP-OH
Internal
Androstenedione standard
**
a
chosen: Methyl
16a-OH
Measure concentration of products as
-Testosterone
a function of time.
16P-OH
id min. 35 min. 40 min.
Time point: 60 min after addition of
substrate, verified to be within the
linearity of M-M kinetics
46 min.
Figure 5-9: Steps in quantifying the concentration of hydroxylated products of testosterone
metabolism using HPLC-UV.
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Quantification of the concentrations of testosterone and the products of metabolism
of testosterone was done by HPLC analysis on an Agilent 1100 series (Agilent Technologies,
Waldbronn, Germany) which consisted of a 9725 Rheodyne injector (Rheodyne, Rohnert
Park, CA) and a G1314A variable wavelength UV detector at 240 nm (Agilent Technolgies).
An aliquiot of the extracted sample (5uL) from each incubation was injected on a Capcell Pak
C18 column type UG120 size 2.0mmID X 150mm, 5um particle size; Shiseido Fine
Chemicals, Tokyo, Japan and eluted at a flow rate of 200ul/minute by the gradient with the
mobile phase, which consisted of solvent A (20 mM ammonium acetate in 10% methanol)
and solvent B (90% methanol). The solvent gradient (solvent B) used for eluting testosterone
and its metabolites was as follows: 0 min, 10%; 0-10min, 10%10-20min, 30%; 20-30min, 55%;
30-38 min, 55%; 38-45 min, 100%; 45-50 100%. The gradient was then returned to the initial
condition (10% B) and held for 8 minutes before analysis of the next sample. A standard curve
of the ratio of AUC units of testosterone (or products of its metabolism) to that of the internal
standard, was plotted against known concentrations of the standards. Standards were all
purchased from Steraloids, RI.
Table 5-1 lists the slopes of the standard curves generated using known
concentrations of the hydroxylated testosterone standards as well as the testosterone standard.
A specimen standard curve is reproduced in Figure 5-10. The slopes in Table 5-1 were
generated using standard curves such as that reproduced in Figure 5-10. A known
concentration of internal standard - 32
M of Methyl testosterone - was added to all the
standards to normalize the data and to remove any technical errors, associated with the sample
preparation steps. Methyl testosterone was chosen as the internal standard as its physical
properties are very similar to those of testosterone and its hydroxylated products. At the same
time, Methyl testosterone is stable and inert and does not normally react with testosterone or
the hydroxylated products.
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6-alpha OH testosterone
2A1
32.208
0.706
15-alpha OH testosterone
2A1, 2C12, 2C13
33.850
0.742
6-beta OH testosterone
IAl, 2A1, 2B2, 2C11,
2C13, 3A1, 3A2
34.535
16.141
0.757
7-alpha OH testosterone
2A1
35.721
16-alpha OH testosterone
2B1, 2Cll, 2B2, 2C7,
2C13
35.904
11-alpha OH testosterone
-N/A-
36.634
16-beta OH testosterone
2B1, 2B2, 3A1, 3A2
36.725
2-alpha OH testosterone
3A2
38.002
0.833
2-beta OH testosterone
3A1, 3A2, 1A1, 1A2
38.550
0.845
15-beta OH testosterone
3A1, 2A1
Androstenedione
2A1, 2B1, 2B2, 2C11,
3A1, 3A2
0.783
26.848
0.787
0.803
28.917
0.805
40.283
8.169
0.883
Testosterone
42.975
42.000
0.942
Methyl Testosterone (IS)
45.621
1.000
Table 5-1: Products of testosterone hydroxylation and p450's that mediate their formation.
* Relative retention time is defined as the ratio of Retention time of a given metabolite dividied
by the retention time of the Internal Standard
# from [106, 120, 123], shaded cells indicate hydroxylation products whose rates were
quantified as part of this thesis.
Table 5--1also lists the approximate retention times of the hydroxylated products as
well as the relative retention times of the various metabolites with respect to the internal
standard. The absorbance spectrum of each individual metabolite was compared against the
spectra of the standards to validate the detection of any hydroxylated product. A sample
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spectrum of one of the hydroxylated products - 6P hydroxy-testosterone is shown in Figure 511.
6-beta hydroxytestosterone t = 34.535 min.
4.0
I
y = 0.0162x
R2 = 0.9993
h 2.0
U
4
1.5
i o
0.5
Fit
0.0
0
20
40
I
60
80
100
I
120
]
140
Concentration (uM)
Figure 5-10: Specimen standard curve used to calculate the normalized slope of AUC ratio
(UV peak absorbance of metabolite divided by the UV peak absorbance of internal standard of
known concentration) versus concentration of metabolite. The slope thus determined was
used to calculate the concentration of hydroxylated product in the sample.
The calculated concentrations of the hydroxylated product are then converted into an
average rate of product formation using experimentally determined values of the total cell
number per unit volume in the culture (usingmeasuredvaluesof totalRNA or 18sgenein cltatreand
a cellnumberversustotalRNAI or 18sgenestandardcurve- seeAppendix and Section6.2), and the total
time over which the drug or xenobiotic was administered to the culture.
itcnlg rcs niergCne
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Peak :8 at 34.535 min Name : 68hudroxutestosterone
:
200
225
250
275
300
325
350
375
n
Figure 5-11: Specimen absorbance spectrum of a hydroxylated product and recorded
retention time.
The spectrum and the retention time are unique for each metabolite detected.
5.5 Theoretical validation of the zeroeth order Michaelis-Menten Kinetics at
high substrate concentrations
As described in Chapter 3, the metabolite concentration gradient set up in the tissue is
a function of two main parameters - the Peclet Number that characterizes the crossflow
through the channels and the Thiele Modulus or the Damkohler number for the metabolite.
The Peclet number in the channel is sufficiently high (Pe > 100) so that convective transport
dominates, and the boundary layer set up at the tissue fluid interface is of negligibly small
thickness. For the case of testosterone, the largest value of K, among all enzymes that are
involved in the biotransformation pathways, is on the order of 50 [M. Thus, the concentration
of testosterone used in the experiment, namely 250 }xM,is atleast 5 times the value of the
largest K, It is therefore expected that the kinetics of product formation should be zeroeth
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order with respect to substrate concentration. Additionally, the large value of the substrate
concentration results in a maximum value of thiele modulus of I)2 =
V
x
DC0
n, = 0.03 for
the enzyme with a K, of 50 M (assuming a tissue length scale (L) of 100 Jim (Chapter 3),
substrate diffusivity (D) of 2 x 10-5 cm 2 /sec [96], a volumetric tissue cell density (n,.) of 4.0 x
1013 cells/m3 (Chapter 3, Figure 3-9) and a maximal enzyme activity (,,)
reported in
literature of 1300 pmoles/106 cells/min [50]). Since the value is significantly less than 1.0, the
mass transfer resistance to the transport of testosterone to the individual cells in the tissue is
negligible, so that there is hardly any concentration gradient set up in the tissue. The schematic
in Figure 5-12 describes such a scenario.
brug Metabolism in the Bioreactor - the limiting case
Q- .
Single Channel Side view
Q,..c-
1
vrVDAn
"I ntracellular" View
Fn m in
.
.
.. hennatnrvt,
.p.tl,YV i..
Products
ot
..
-
4* metabolism
-
) _..!A >>1ha
-NT
-
Conc.Gradients- MassTransfer Effects (Minimal)
-
"Macroscopic" View
- CellularMetabolism
Apparent rates of metabolism (experimentally
measured- includesmasstransfer effects)
-
Intrinsic rates (basedpurely on enzyme kinetics)
,LV
~A~
'channel
(Pe
>>e
(P >>
1)
)
"Rate of Metabolism"
In the linear regime of
Michoelis-Menten kinetics:
(AC)*
I
At n,,,
-Vm
Figure 5-12: A schematic analysis of the limiting case of high concentration of drug
(substrate) added to the 3D culture.
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This results of the simple schematic analysis is also borne out by the prediction made
by the model developed in Chapter 3 to describe the tissue distribution of any metabolite (In
Chapter 3, results were shown for oxygen).
The minimum scaled concentration of
testosterone anywhere in the tissue is nearly 9 9 % of the inlet substrate concentration (Figure 513), using Scenario 2 modeling analysis.
'
.. .
I2
1
"..x
nrA-*
- - U'.UJLsub
C,,b + 0.25
'
Co
Ub= 200uM
0.996
KM = 5 0 uM
0.996
High
Substrate
conc.regime
0.994
Testosterone
Metabolism
0.992
6Bhydroxylation
0.99
KM 0.988
Figure 5-13: Results for the tissue distribution of testosterone
The comprehensive mass transfer model developed in Chapter 3, predicts a negligible tissue
concentration gradient at large values of substrate concentration of testosterone (greater than
200 rtM). This result confirms the intuitive schematic analysis of Figure 5-11.
5.6 Summary of key results and conclusions
The total internal volume of the reactor and the fluidics was reduced by incorporating
a small-volume conical reservoir into the fluidic circuit, instead of the 15 mL reservoir that was
used in the original design. In addition, the use of small-bore Teflon® tubing in the fluidics
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helped decrease the dead volume in the reactor fluidics. The CFLEX ® pump tubing was
coated with a pin-hole free layer of Parylene that helped minimize the adsorption/ absorption/
binding of the drug or xenobiotic to the tubing, to less than 5%. Testosterone at
concentrations high enough to saturate the various CYP450 enzymes involved in its
biotransformation, was used as a substrate to measure the average rate of formation of many
of the hydroxylated products.
A method to isolate, detect and quantify the various
biotransformation products of testosterone, at high concentrations of substrate that leads to
the saturation of the enzymatic biotransformation pathways, was developed.
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Chapter 6
Comparing Basal Biotransformation Capacity
of various in vitro Systems
In this chapter, the 3D microfabricated bioreactor that was designed and characterized
in the previous chapters is compared with a number of standard in itmrcultures currently used
in the pharmaceutical industry.
The suitability of the tissue formed in the 3D bioreactor
cultures, as an in vitromodel to study metabolism and toxicity of xenobiotics was assessed by
comparing the basal expression of the p450's (i.e. Phase I), Phase II genes and genes that
regulate the expression of liver-specific proteins, in the 3D bioreactor cultures and in standard
in vitro cultures with gene expression profiles seen in ivoand in percolled isolated hepatocytes
Using a Rat U34A Affymetrix Chip, genes that are differentially expressed between
the 2D collagen sandwich cultures (2DCSW) and a variety of 3D cultures, including the 3D
bioreactor (3DB), were identified at multiple time point's up to 20 days in culture, relative to
the expression profiles in the in ivo liver. Using RT-PCR, the basal expression of a number
different CYP450's and Phase II metabolism genes were compared in seven day old 2DCSW
and the 3DB cultures. Since the expression of a number of liver-specific genes are regulated by
transcription factors [17, 23, 124-129], the basal expression of these transcription factors were
compared between the 2DCSW and 3DB systems, seven days after cell isolation.
The
expression levels of all these genes in 2D and 3D cultures have been compared to the levels
seen in both freshly isolated hepatocytes as well as in in vivo liver. In addition, the activity of
the key Phase I enzymes have been compared across the 2DCSW and 3DB liver tissue
phenotypes, using testosterone as a candidate substrate.
6.1 Global gene expression profiling of tissue phenotype
The global gene expression profiles of the rat genome as represented by the
Affymetrix Gene Array (U34A) was compared across isolated cells and tissues formed in 2D
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collagen sandwich cultures, 3D matrigel spheroids, spinner flask 3D spheroid cultures, and the
3D microreactors. The comparative expression levels of all genes of the culture systems
relative to the in vivocells were determined in order to identify major gene classes that differ
substantially across the different culture systems.
The Affymetrix small sample labeling
protocol (www.affymetrix.com) was used for all total RNA samples to generate labeled cRNA.
Labeled samples were hybridized in triplicate to the Rat Genome Array (U34A) Gene Chips.
Data from arrays were processed using non-linear normalization approach through DNAChip Analyzer version 1.2. The average and the standard deviation of replicates were
calculated and probes that were "Absent" throughout the data set were eliminated from
further analysis. Fold changes were calculated by dividing the mean of replicates of the culture
condition by that of intact liver tissue for each gene. Quality filtering using coefficient of
variation (CV) was performed to data with high variability. Using S-Plus software (Mathsoft,
Cambridge,
MA), a 95% CV quantile was calculated
for each sample.
Significance was
assigned for samples within 95% CV threshold and samples with high variability eliminated.
The number of probe sets remaining after filtering was 2098 probes (23.84%). A detailed
report on the specific protocols used in the microarray experiment is available elsewhere [130].
The global transcriptional analysis showed that of the -2100 genes that were present
following data analysis (see methods), more genes remained unchanged (relative to in tvi) in
3D microreactor culture than in other culture methods at comparable times points (Table 6-1).
Of particular interest for pre-clinical drug biotransformation and p450 induction studies, genes
that were differentially expressed between the long term 3D microreactor culture and other
culture methods include the CYP450's. A hierarchical clustering of the expression of the
CYP450s reveals that the 3D microreactor culture system maintains expression levels closer to
in vivo liver and isolated hepatocytes than the other culture systems (Figure 6-2). For each
culture method, the basal expression levels of CYP450's observed seem stabilized at the two
later time point's, and from day 7 forward in the 3D system. Stabilization of basal CYP450
genes by day 7 in 2D collagen sandwich cultures has previously been observed [131] and in
house RT-PCR studies (described in the next section) were also used to confirm that
expression of several key CYP450 genes were stabilized by day 7 (Figure 6-1). Based on these
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results, the 7 day time point was chosen as a reasonable predictor of long-term differential
gene expression among the culture systems, and the gene and protein expression profiles were
probed more broadly and quantitatively using RT-PCR and biochemical activity studies.
I
-
FC Base
Liver
Reference
2D Collagen
Sandwich
< -4 fold or
highly down
regulated
-
-
-4 to -2 or
|
-2 to +2 or
moderately
unchanged
down regulated
-
-
-
+2 to +4 or
moderately up
reaulated
+4 to max or
highly up
regulated
Perfused
Liver
I
4
1950
138
5
Isolated Liver
Cells
1
7
2024
61
5
Purified
Hepatocytes
8
12
2043
30
5
Day 3
129
177
1330
343
119
Day 13
98
165
1264
357
214
Day 20
291
445
1209
114
39
Day 3
56
108
1567
317
50
Day 13
84
163
1312
336
203
Day 20
89
113
1301
345
250
Day 3
92
120
1285
400
201
Day 7
60
95
1396
404
143
Day 13
64
120
1433
333
148
Day 20
72
145
1335
348
198
cultures
Matrigel
Cultures
Spheroids
3D micro
reactors
Table 6-1: Global comparison of gene expression profiles across different in vitrocultures at
various time points
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n
U
w
-
-
==
_;
i
.S
e
I
a
I
ae
0E
Xi
aa
L
I
-1l
(i)
@M *
II
*
*
o
o
Day 3
Day 7
Day 10
Day 14
_- *ft
1A2
2E1
3A2
2C11
Figure 6-1: Stable expression (though downregulated in many cases) of many of the CYP
genes is seen in the 2D collagen sandwich cultures after seven days in culture.
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LI
z
0
<-4 Fold
>4 Fold
Hierarchical Clustering
--
I1
.
...- i
{ !..............-- __ _
--
I-
i
I
I
-I
i
,
..
-
I
-.
Gene Syml
Cyp2c13
Cyp2c22
Cyp2d 18
CYP3A9
CYP4F4
CYP4F5
CYP4F6
Cyp2al
Cyp2b15
Cyp2c39
Cyp3a 1
Cyp4a10
Cyp4a12
Cyp7al
Cyp17
Cyp19
Cyp1bl
Cyp27b1
Cyp2a2
Cyp2fl
Cyp4bl
Cyp51
Cyp7bl
Cyp2c
Cyp2c2
Cyp2d3
Cyp4f14
Cypl 1b2
Cyp2d5
Cyp2b3
Cyp3A1
CYP3A2
Cyp2E1
-5
&S75
0
Zsc
-d -2 I
Z-n
Figure 6-2: Log ratios of all culture systems (2I), 3D) versus intact liver tissue were clustered
for all CYP450 genes as represented
on the Affvmetrix Rat Genome (U73A) arrays.
Genes Nith higher expression than in intact liver are shown in green, and with lower
expression are shown in red.
The 2D collagen sandwich culture system (henceforth called "2D culture") was chosen
as representative of standardcultures to compare to the 3D microreactorsystem as both the
matrigel and collagen gel cultures clustered closely together in dhe p45 0 heat map (Figure 6-2),
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while the 3D microreactor clustered closest to liver (Figure 6-2). However, in order to be able
to compare the various in .itro systems quantitatively using the expression of specific genes
identified by the global gene expression experiment as a metric, it is important to identify a
well-conserved housekeeping gene that does not change across the different systems. More
quantitative RT-PCR studies need such a gene to be able to normalize the liver specific gene
expression data.
6.2 Quantifying relative gene expression: The two step process: Reverse
Transcription - Polymerase Chain Reaction (RT-PCR)
In order to validate the'results of the global gene expression data shown in the form of
the cluster map in Figure 6-2, more quantitative Reverse Transcriptase-Polymerase Chain
Reaction or RT-PCR studies are needed. Real-time RT-PCR is one of the most sensitive
methods for quantitation of gene expression levels [132]. This two-step assay measures the
accumulation' of fluorescent DNA product during amplification in the polymerase chain
reaction (PCR) and correlates this to the initial amount of target RNA. The various steps
involved in RT-PCR are detailed in Figure 6-3. First, template RNA is reverse transcribed to
i nDNAs
cDNA using specific primers, random hexamers, or oligo-dT primers. cDNA is then copied
and amplified in PCR to generate double stranded DNA products.
s
extraction
Sample
D
-l-ll
RNA
(
r
<a
5D
SYBH green
.
PCR,
RT Reagents!,
Separated RNA
(in vivotissue, isolated
Analysis
cDNA
......
.L_--
L __
heps, and in vitro
samples)
_
Figure 6-3: The various steps involved in an RT-PCR experiment used to quantify relative
gene expression
In PCR reactions there are three regions (Figure 6-4): (1) 'baseline' phase -- where
primer is in excess and cDNA is limiting; (2) 'exponential growth' phase - where primer and
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__
__
cDNA concentration are on the same order of magnitude; and (3) the 'plateau phase' - where
cDNA is in excess and primer is limiting. There are currently four competing techniques
available that detect amplified products with about the same sensitivity: (1) Molecular beacons;
(2) Hybridization probes; (3) Hydrolysis probes - like TaqMan 5'-exonuclease assay; and (4)
DNA binding dyes. Each technique has its own unique merits, but DNA binding dyes are
relatively cheap and are known to be sensitive enough to detect binding for genes expressed at
moderate to high levels in the samples.
Figure 6-4: The three regions seen in the fluorescence-cycle number amplification curve
obtained following RT-PCR
DNA-binding dye detection involves the direct binding of a fluorescent dye (SYBR
Green I) into I)NA during PCR. SYBR Green I has an undetectable level of fluorescence
when it is in its free form, but each time the cDNA is copied (cycle) the SYBR Green I is
bound to a dsDNA (Figure 6-5) and begins emitting fluorescence [132].
SYBR Green
*
Og
0.0
0.................................
. ",,7 -.m'
Figure 6-5: Fluorescence following binding of the SYBR Green dye to ds-DNA
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When monitored in real-time, this results in an increase in the fluorescence signal that
can be observed during the 'exponential growth' phase. In the 'exponential growth' phase a
fluorescence
signal threshold,
C.r (Figure 6-4) is set to determine relative cycle numbers i.e.
number of cycles needed to produce a given amount of product that is proportional to the
fluorescence signal corresponding to the threshold. This threshold is calculated after
normalizing for the global minimum background fluorescence. The value of C,. is directly
proportional to the amount of starting template and this forms the basis for calculating rRNA
or mRNA expression levels as follows:
a(t)
PCR,i2c(t)
where 'a' is fluorescence intensity, and '
is the efficiency of PCR amplification of target
primer 'i' (should be unity). These gene expression levels can be quantified to a resolution of
that seen even in a single cell [133]. RT-PCR is thus a highly sensitive, fast, accurate and
quantitative means of measuring gene expression compared to other end point assays [134].
Further, the technique is sufficiently high throughput, allowing 96 samples to be run
simultaneously in two hours.
Quantification using gene expression is prone to variability due to a numbr of
technical and other reasons [135-1371. There exists the possibility of co-amplification of
genomic DNA and primer-dimers [138]. These unwanted products can be controlled, but
require careful primer design (e.g. by choosing primers on the intron-exon boundary and by
using primers that result in a product with melting temperatures greater than those of primerdimers and genomic DNA products) and experimental control (by including DNA digestion
steps in the protocol and by always having a no-RT control i.e. an mRNA sample that has
gone through a DNA digest step but has not been converted to cDNA). Additionally, small
pipetting errors propagate into large differences in gene expression levels, so extreme care
must be taken to ensure the technical consistency of plate loading for PCR. Furthermore,
variations in amplification efficiency have been observed in different primers (in housestudies).
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Thus, each primer must be validated to ensure a linear trend in the concentration or amount of
gene product and threshold cycle number (keeping fluorescence constant or vice-versa). Figure
6-6 shows the linear relationship obtained between the threshold cycle number at constant
fluorescence versus the amount of 18s gene present (represented here as absolute cell numbers
assuming that the amount of 18s gene per cell is constant).
4A
I
L+
y = -1.5617Ln(x)+19.043
) 10e3
I-
Go
dI!
100
1000
Cell Number (1000s)
Figure 6-6: Linear relationship between threshold cycle number and quantity of gene product
for the 18s gene.
The above plot (mean+SEM, n=18) was generated by combining a known volume of
concentrated cDNA in a real-time RT-PCR reaction.
Theoretically, the slope of the line should have been 1/ln(2) = 1.4, as we should ideally
expect twice the amount of product (18s gene) per unit change in Cl value. However, in reality
primer binding efficiency as well as the efficiency of binding of the DNA dye, may not be
100% leading to higher values of the slope (and hence less than two-fold change in product
amount per unit change in threshold cycle number for the same fluorescence). A detailed
analysis of RT-PCR protocols, techniques and methods is available elsewhere [54].
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6.3 Identification of a well-conserved housekeeping gene for RT-PCR studies
Normalizing mRNA expression level data of various genes is of critical importance
when comparing systems containing different cell numbers as well as cells that have been
under different culture environments, as extraction efficiencies may not be equal in all systems.
In addition, the normalization can help differentiate between technical errors associated with
performing the assay and actual differences in gene expression that can then be ascribed to
differences in the culture paradigm or cell treatment methods used. It is necessary to find a
gene whose expression is equally conserved across all cell culture systems being investigated, as
well as in the presence of any toxin or drug added to the system. This latter is an important
consideration that needs to be bome in mind if data on enzyme biochemical activities of
specific p450's are to be used to compare the various in itm systems.In addition, data on
changes in the expression of specific p450's in response to the addition of any drug or inducer
will need a well-conserved gene that is unaffected by the addition of the drug/ inducer, to
normalize the data. Previous studies have used total DNA, GAPDH, P3-Actin,total RNA, and
ribosomal RNA (e.g. 18s and 28s) as a means to normalize data [135, 139]. Certain studies
have expressed concern over the consistency of these expression levels between various in vitro
cell culture systems as well as repeatability within the same culture system [140, 141]. DNA is
degraded slowly in comparison to RNA and may be indicative of both live and dead cell
densities. Previous studies, as well as in house data in our lab [130] have suggested that
mRNA expression levels for GAPDH may not be equally conserved [142]. Expression levels
for 3-Actin may be significantly different in 2D and 3D cell culture systems because of
variations in structural complexity between 2D and 3D in vitro culture systems and this is
indeed seen to be the case (Figure 6-3). Total RNA levels are thought to be largely a function
of cell number, but cannot be used to normalize real-time RT-PCR data because of differences
in cDNA conversion efficiencies between samples. Ribosomal RNA makes up 85-90% of
total RNA and we hypothesize that it also scales linearly with cell number (Figure 6-6 validates
our hypothesis). These methods of normalizing data must be evaluated to accurately validate
experimental data. It may also be possible to determine the approximate the number of cells
in a sample using either total RNA or critical threshold (C.,) values for 18s expression from
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real-time RT-PCR in combination with a standard curve of known RNA concentrations.
Figure 6-7 plots the linear relationship obtained between percolled isolated hepatocyte cell
numbers and the total RNA isolated. Together with Figure 6-6, this proves that our hypothesis
- namely that the amount of 18s RNA per cell is constant and that the total amount of 18s
RNA as a fraction of the total isolated RNA is constant.
y=0.065x
R2 =0.98
0)
=
0
I-
1
0
100 200
300 400
500 600 700 800
Cell Number (1000s)
Figure 6-7: Isolated Hepatocytes vs. Total RNA
A Beckman Coulter DU 460 spectrophotometer was used to measure absorbance at 260nm
and to determine total RNA levels in isolated hepatocyte samples containing various cell
numbers (10IK-800K, n=47). Total RNA was plotted (mean+SEM) and shown to be a linear
function of cell number.
For the chosen housekeeping gene to be used to normalize gene expression data from
different in vitr culture systems, specifically from different 2D and 3D tissue culture systems, it
is important that the amount of the housekeeping gene per cell be invariant across the
different culture systems. RT-PCR reactions against the 18s gene were performed on equal
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quantities of total RNA isolated from 2D and 3D culture. From Figure 6-8, where the C,. value
is indicative of the number of cycles needed for a given amount of 18s DNA product (given
that all three systems started off with the same amount of total RNA), it can be seen that the
amount per cell of thel 8s gene is statistically invariant across the different culture systems.
15.
10-
5-
n.
v
Isolated Hepatocytes
2D Cultures
3D Bioreactors
Figure 6-8: 18s gene per cell is invariant across the different culture systems.
Real-time RT-PCR was used to determine C.. values for freshly isolated hepatocytes
(13.93+0.20, CV=0.07, n=24), for 2D collagen sandwich cultures (13.22+0.10, CV=0.05,
n=42), and the 3D liver bioreactor (13.98±0.08, n=3). The coefficient of variance of C.
values across all samples was 0.06 or 6%. 2D collagen sandwich cultures and the 3D
bioreactor had the same level of 18s expression for the same amount of total RNA isolated
from the three different systems, as freshly isolated hepatocytes as determined by the
Bonferroni Method. Plot shown above is mean+SEM.
Figure 6-9, shows the effect of addition of a drug testosterone on the expression of
18s in 2D collagen sandwich cultures. Testosterone is an important substrate used in
quantifying the biochemical activity of a number of pharmaceutically relevant p450's. RT-PCR
reactions against the 18s gene were performed on equal quantities of total RNA isolated from
different 2D cultures taken down after being exposed to the same concentration of drug (250
pM) for different intervals of time. From Figure 6-9, where the Cr value is indicative of the
number of cycles needed for a given amount of 18s DNA product (given that all the samples
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started off with the same amount of total RNA), it can be seen that the amount per cell of
thel8s gene is indeed statistically invariant across the different samples and is therefore not
affected by the addition of testosterone as a candidate drug.
15-
-7
10-
0I5-
._L
nV-
_
15
30
45
60
90
120
Time after testosterone addition (min.)
Figure 6-9: Effect of addition of testosterone on the per cell 18s expression levels in 2D
collagen sandwich cultures
There was no statistical difference in the C. values across the different cultures taken down at
different time points after administration of the drug (n= 18 at each time point)
6.4 Analysis of RT-PCR data: Use of normalized fold change as a metric to
compare gene expression data
There are two primary methods used to quantify gene expression levels: absolute and
relative quantification. The former method finds a lot of use in literature [132, 134, 135, 138,
139, 143]. The mathematics for the use of the most generalized expressions needed to quantify
the relative expression of a particular gene, following normalization with a chosen
housekeeping gene, was also developed as part of this thesis. This is described in detail in subsection 2 of this section.
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1)Absolute Quantification
In this method of quantification, samples of known concentrations of gene products
are used to construct a standard curve that relates the C.1 value with the concentration of the
gene in a sample. Logarithmic regression is used to fit slope, m, and the intercept, b, into the
form:
CT=mln(CA)+b
(6-1)
Thus the absolute unknown concentration of a particular gene A in a sample, may be
calculated using the logarithmic relationship and the calculated value of C. from the RT-PCR
amplification curve.
2) Relative Quantification
Relative quantification involves reporting the expression of a gene A in sample 'a'
relative to the expression level the same gene A in another sample 'b'. The difference in the
value of the threshold cycle numbers corresponding to the same amount of fluorescent gene
product, quantifies the relative up-regulation or downregulation of the gene A in sample 'a'
relativeto 'b' or vice-versa.
Whether the data is reported in absolute terms or in relative terms it is important to
normalize the data using relative expression of a house keeping gene (in our case 18s) between
the two samples. The mathematical relationship that determines the expression of gene A in
sample 'a' relative to the expression of the same gene A in sample 'b', is derived as below:
Let the concentration of gene A in sample 'a' be CA, that of the 18s housekeeping
gene in sample 'a' be CB8s . Let the concentration of gene A in sample 'b' be CA and that of
the 18s housekeeping gene in sample 'b' be Cb8 . From equation (6-1-) we have:
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CTa = mA n (Ca) + bA
(6-2)
n (Cb) + b
CTAb
=m
(6-3)
Subtracting (6-3) from (6-2):
C C;,a -C
T,bn=
In
C aA A
A
(6-4)
Similarly for the housekeeping gene 18s, we have:
18s
O18s
CT,a CT,b
n ( C a s8s m18
iC
(6-5)
Cb)
Subtracting (6-4) from (6-5):
/
II
A
(C185 S
(c8s
-- Ta CA
T,a -
s
_ Tb)
Ab)=lnn
T, -8s
(6-6)
Cb2)
t bA
(C,18s
kT*a -
i.e.
where
l
[(FC ) 18s
in 'a' over
A
T,a
II
(C18s CA
\ T,h
T,b
(6-7)
is the Fold change in 'a' over 'b' of 18s and
the
[(FCb)Auis
fold change
' of gene A.
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Thus
T )
FC(C)
8
ba
TCb
j)
(6-8)
Theoretically, for a unit change in the value of CT and CT , the concentration of the 18s
gene and gene A should respectively double. This will mean that the theoretical values of:
Thus
mA
=
) = mss
(
(6-9)
In reality, a unit change in C. may result in a less than two fold increase in the amount of gene
product. Hence the value of ml and m,, may be higher than that suggested in (6-9). Also, a
closed form solution and simplification for equation (6-8) is only possibly by the theoretical
assumption made in (6-9).
Using (6-9) in (6-8), this simplifies equation (6-8) to:
=,CTa
Tb-CTb)2 C
7
(6-10)
Thus we can define a relative normalized fold change of gene A in sample 'a' over sample'b',
normalized using the expression of the 18s housekeeping gene as:
Normalized Fold Change (NFC) of A in sample 'a' over sample 'b' is given by:
(NFC )-=
2
(CT'a
CTa
Y(T
,
b ))
(6-11)
The normalized log fold change (LNFC) of sample 'a' over sample 'b' in given by:
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(LNFC)=
{{CA
_18s
log 2 (2((Ca -c,CT
\
-
A
bb
18s
)
(6-12)
Figure 6-10 shows the log fold change in the expression of 18s and L-actin relative to the
expression in isolated hepatocytes, 2D collagen sandwich culture and 3D bioreactor
18s vs.
P
-Actin Normalization
6 N=6, n=6
n
C
.
0)
LIu
O
n mRNA
,_1
LL
lagen
0)
-j
ich
Culture
Figure 6-10: 18s versus b-actin as a normalization gene
P-actin, a commonly used housekeeping gene is seen to be upregulated in 3D cultures relative
to in cicoand is seen to be downregulated (again relative to in tviv) in the 2D cultures. On the
other hand 18s is seen to be a very stable housekeeping gene whose expression amount per
cell is seen to be invariant across 2D and 3D cell cultures.
6.5 Comparing liver specific gene expression between in vitro cultures using
Affymetrix® microarrayand RT-PCR studies
As described in Section 6.1, an initial assessment of the relative basal metabolic
capacities of different culture systems compared to rat liver was obtained by analyzing the
global gene expression profiles of the rat genome as represented by the Affymetrix Gene Array
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(U34A). A hierarchical clustering of the expression of the 24 significant CYP450s revealed that
the 3D microreactor culture system maintained expression levels closer to in vim liver and
isolated hepatocytes than either matrigel or collagen gel sandwich cultures after 7, 13 or 20
days of culture (Figure 6-1). At day 20, most CYP450's were strongly downregulated og2 fold
change < -4) in collagen gel sandwich culture and matrigel culture (15-16 genes of 24), while
only a few (5 genes) were strongly downregulated in 3D microreactor culture (Figure 6-11).
The basal expression levels of CYP450's appeared to be stabilized after a week in
culture, consistent with what has been previously observed in 2D collagen sandwich cultures
[131]. Westerns blots (Figure 4-10) and RT-PCR studies (Figure 6-1) confirm that expression
of several key CYP450 genes were stabilized perhaps by day 3 and certainly by day 7 in
collagen gel sandwich cultures and 3D microreactor cultures. Based on these results, the 7 day
time point was chosen as a reasonable predictor of long-term differential gene expression
among the culture systems, and the gene expression was probed more quantitatively using RTPCR and biochemical activity studies. The 2D collagen sandwich culture system was chosen
as representative of standard cultures to compare to the 3D microreactor system to, as both
the matrigel and collagen gel cultures clustered closely together in hierarchical clustering, while
the 3D microreactor clustered closest to liver.
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16
14
U)
C 12
0)
o 10
I)
:-
8
L
6
E4
.
Z
2
min to -4
-4 to
-2 to +2
+2 to +4
+4 to max
Log2 fold change relative to native tissue
1I
16
14
S 12
a)
A
10
Ve
8
0
0.
0
L.
6
Z.
z
2
0
min to -4
-4 to -2.
-2 to +2
+2 to +4
.+4to max
Log 2 fold change relative to native liver tissue
Figure 6-11: Relative CYP450 gene expression across cultures - from RatU34A microarray
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The CYP450 enzymes are diverse in their expression and activity levels, substrate
specificities, and regulatory mechanisms. For quantitative analysis by RT-PCR, a subset of nine
CYP450 genes were chosen that reflect this diversity and that have previously been studied in
vivo and in vitro under a variety of conditions and are of relevance in pharmaceutical
development. Similar criteria were used for selecting Phase II genes, with an emphasis on
genes that have previously been reported as down regulated in vitro.
The expression patterns in the 3D microreactor and 2D culture were strikingly
different after 7 days in culture (Figure 6-12). Whereas all Phase II genes and almost all Phase
I genes (7 of 9) were strongly down-regulated in 2D, most genes in 3D culture are either
unchanged or slightly down regulated (Figure 6-12). CYP 2E1 was the only gene that
approached the category of being strongly downregulated in 3D culture, and its expression
level was about 30-fold higher in 3D microreactor culture than in 2D culture. CYP 2E1 gene
expression is reportedly down-regulated by insulin [144] and is hence expected to be down
regulated in a culture responding physiologically. With the exception of CYP 1A2, which
tends to be stably expressed in most cultures [80] and is slightly upregulated in both 2D and
3D cultures here, loss of CYP expression in primary rat hepatocyte cultures has been
documented in a number of different culture configurations [8, 12, 65, 80].
CYP 2C1
is a major constitutively-expressed CYP450 in male rat liver and is
particularly susceptible to changes in the hepatocyte environment [120]. The early loss of CYP
protein activity in primary hepatocyte cultures has been attributed to the activation of nitric
oxide synthesis, due to the generation of reactive oxygen species during the isolation procedure
[80, 145], and may involve other mechanisms as well [146]. Formation of three dimensional
multicellular cell aggregates may mitigate oxidative-stress induced tissue damage [80]. Indeed,
2C11 expression is observed to be only slightly down-regulated in the 3D culture but is
strongly downregulated in 2D culture (Figure 6-12). The expression of CYP2B1/2 is
moderately upregulated in 3D microreactor cultures (Figure 6-12), for reasons that are as yet
unknown. Induction by agents leached from components of the 3D microreactor system as
one possible reason for this upregulation was ruled out - 2D cultures that were cultured up to
7 days with conditioned medium circulated in the 3D microreactor system did not show any
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'~.~~
6 ·
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i
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upregulation of 2B1/2 relative to control (data not shown). CYP 2B1/2 shows a zonal activity
gradient in vivo [42, 147], with more activity in the pericentral zone of the hepatic acinus, and
thus the environment in the microreactor may reflect these conditions.
Among the Phase II genes, downregulation of some of the UGT's, GST-Ya and
specifically that of HSST to lower than 20% of the in i values within 24 hr after cell isolation
has been reported in a variety of primary cultures, independent of the culture matrices and
media formulation used [148]. However, the expression of these genes, along with that of
UGT-1A, is maintained at near in vivolevels in the 3D microreactor tissue.
6.6 Relative expression of liver transcription factors between 2D and 3D cultures
Liver-specific gene expression is believed to be controlled primarily at the level of gene
transcription [128, 129] and the expression of drug metabolizing genes are regulated by key
liver transcription factors that play a major role in the overall maintenance of the differentiated
state of hepatocytes [23, 124, 126]. Expression of HNF's, a heterogeneous class of
evolutionarily conserved transcription factors that contain several families of liver-enriched
transcription factors required for cellular differentiation and metabolism, is strikingly different
in 2D and 3D culture, with most factors unchanged in 3D culture and downregulated in 2D
culture (Figure 6-13). HNF-4a is an upstream regulator of HNF-1 a expression [149] and both
HNFla
and HNF4a, are regarded as important regulators of the transcription network in
liver-specific gene expression [124]. HNFla
and HNF13 are both down regulated in isolated
hepatocytes, but show recovery in 3D over 7 days of culture and sustained downregulation in
2D culture. The strong downregulation in HNFa
at day 7 in 2D culture is consistent with
the moderate downregulation of HNF4a seen in these cultures (Figure 6-13). The expression
of CEBP-[, is upregulated under all conditions, with by far the strongest upregulation
observed in 2D. Over expression of CEBP-P has been reported in literature in response to
tissue injury and stress [150]. The 3D perfused microreactor may help mitigate the effect of the
isolation stress more effectively than the 2D environment. The transcription factor expression
profiles for the two culture conditions are thus consistent with the expression profiles of the
downstream Phase I and Phase II enzymes, and are further consistent with differences in the
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albumin secretion rates previously observed in the 3D microreactor system compared to other
primary rat hepatocyte culture systems [47]
.g
tr.
0
C.
0
la
-s
Figure 6-13: Basal expression of hepatic transcription factors in isolated hepatocytes, 2D
collagen sandwich culture (day 7), and 3D microreactor (day 7) cultures expressed as log2 -fold
change relative to liver in vie.
6.7 Measurement of testosterone metabolism in 2DCSW and 3DB cultures
The activity of the Phase I CYP450 enzymes measured by the rates of testosterone
hydroxylation to products 16a, 16p, 6P hydroxytestosterone parallels the differences seen at
the mRNA level (Table 6-2). The basal 16a hydroxylation rates, a measure of CYP 2C11
activity, are about 8-fold higher in 3D culture compared to 2D, a difference corresponding to
the -8-fold greater 2C11 mRNA level observed in 3D microreactor culture compared to 2D
culture (Figure 6-12). The combined activities of CYP2B1 and CYP2B2 measured by rate of
production of the 16[ hydroxylation product [106, 118] of testosterone are nearly 20 fold
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higher in 3D cultures than in 2D (Table 6-2), a result that parallels the trend but is muted in
terms of absolute magnitude of the -200-fold greater expression observed in RT-PCR analysis
of CYP2B1 mRNA levels. These differences in magnitude underscore the importance of
validating results observed by PCR with activity data, as activity depends on a multitude of
downstream events including mRNA stability and translation, protein stability, and co-factors
needed for protein activity. For example, nitric oxide and/ or peroxynitrites induced during
isolation can cause specific blockage of the hemes of the CYP450s, resulting in alteration of
enzyme activities in ittv through mechanisms downstream of mRNA [151]. Among the
several CYP450s that
have
been
reported
to
hydroxylate
testosterone
to
form
6P hydroxytestosterone, CYP3A1 and CYP3A2 , perhaps along with CYP1A2, appear to
dominate the activity [120, 146, 152]. The 20-fold difference between 2D and 3D microreactor
cultures in the combined activity of enzymes that contribute to formation of 63 products
trends with the -100-fold difference in CYP3A1/3A2 mRNA levels (Figure 6-12), as well as
with differences in the message levels of some minor enzymes that might contribute (1A2,
2C11).
Testosterone hydroxylation rates reported in the literature cover a wide range,
reflecting variations due to differences in animal strain (different strains have different basal
expression rates), measurement protocols (liver microsome fractions compared to whole cells),
and methods for normalization to obtain a specific rate per cell [120, 146, 152]. With these
considerations in mind, our rates of metabolite formation in the 3D bioreactor are reasonably
comparable to those for freshly-isolated cells or in vivorates, while the metabolism rates in 2D
culture are generally substantially below or at the low end of the reported rates (Table 6-2).
. Also, in normalizing the activity data, the total number of viable cells were calculated
based on a measurement of the total 18s rRNA content (see Section 6.4, 6.5, Figure 6-6 or 6-7
may be used to back out a 'viable' cell number, see Appendix for RNA isolation, cDNA
preparation and RT-PCR protocols), while most reports in literature use total cell number
from total DNA measurements that may account for both live as well as dead cells.
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Testosterone
Hydroxylation
Product
16a
CYP450's
Testosterone Hydroxylation rates on Day 7
mediating basal
(pmoles/10 6 cells/min)
metabolism'
2C11
2D collagen
3D
Other systems (in viv or
gelb
microreactorb
isolated cells)
100 ± 42
840 + 206
850 + 60 to 1880
500'
825 to 1200 d
16P
6j3
2B1, 2B2
Major:3A1, 3A2,
2A2 Minor: A1A1,
1A2, 2C11
49 + 22
878+ 271
145 + 57
2970 +1620
15 to 100'
2700 + 330 to 3500 + 790f
500 to1250d'
6000g
Some variability has been reported depending on species and measurement method (i.e.,
microsomes, isolated cells, or in vivo). Enzymes shown are those reported as primary
metabolizers in the most relevant experimental system for which data is available (primary
Fisher hepatocytes for 16a27 ; in vivo Sprague Dawley for 1644; Sprague Dawley microsomes
for 6[39'though several other enzymes have been suggested as involved in other studies).
bValues represent the mean + standard error for N = 4 biological replicates (i.e., different rats)
with at least 3 replicates within each animal.
c Basal rates for freshly isolated male Fisher rat hepatocytes, this work, range reflects
measurements taken after either 15 min (1880 pmoles/10 6 cells/min) or 30 min (850
pmoles/10 6 cells/min) incubation with testosterone; the decreasing rate with time reflects the
expected rapid loss of CYP450 activity in suspension culture.
d Basal rates for freshly isolated male Fisher rat hepatocytes - ref. [120]
Basal rates for Sprague Dawley rats in ivo - ref. [152]
f Basal rates for freshly isolated male Fisher rat hepatocytes, this work, range reflects
measurements taken after either 15 min (3500 pmoles/106 cells/min) or 30 min (2700
pmoles/ 106 cells/min) incubation with testosterone.
gDexamethasone-induced rates for Sprague Dawley rats in vivo - ref. [152]
a
Table 6-2: Higher basal activity of 2C11, 2B1, 2B2 and 3A1, 3A2 seen in the 3D
microreactors over the 2D collagen sandwich cultures.
16[3 and 16a hydroxylation of testosterone, indicative of the activity of CYP2B1/2 and 2C11,
are seen to be at least 10 fold higher in 3D cultures over 2D. The rate of 6[ hydroxylation is
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seen to be 20-fold higher in 3D over 2D and is even higher than rates seen in liver
microsomes. Since multiple p450's including CYP2A and CYP2B, have been reported to
contribute to the 613 hydroxylation of testosterone [106, 118], the observed upregulation of
CYP2B may also be contributing to this result. In normalizing the activity data, we calculate
the total number of viable cells based on a measurement of the total 18s rRNA content. Data
from N=3 different rats, with n=3 different replicates in each experiment
6.8 Biochemical regulation of tissue function: effect of soluble factors in the
media
The biochemical regulation of the various biophysical processes that take place during
tissue morphogenesis has been well documented in detailed reviews in literature [8, 50, 88].
Thus, in addition to providing a 3D environment that mimics the biophysical process of in
vivo like morphogenesis through cell-cell, cell-matrix, soluble and shear mediated signalling
mechanisms, a suitable media formulation with appropriate factors that help mimic the in vivo
biochemistry can further improve the tissue phenotype. For e.g. both in 2D collagen sandwich
cultures as well as in 3D bioreactor cultures, relative to the in ivogene expression, the bile acid
transporter genes are seen to be highly down regulated (see Figure 6-13), possibly due to the
absence of Hepatocyte Growth Factor (HGF) in the media [21, 22, 153]. HGF is needed for
the maturation and function of the bile duct cells that carry many of the bile acid transporters.
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Figure 6-14: Downregulation of Bile and Fatty Acid transporters seen in both 2D as well as in
3D reactors.
It is hypothesized that this downregulation may in whole or part be mediated by the presence
or absence of specific components in the media used to culture the cells
Similarly, insulin present at a high concentration in the media is known to
downregulate the expression of CYP2E1 and PEPCK (Phosphoenolpyruvate Carboxykinase)
in culture [144, 154-158]. This is seen in the downregulated expression of these genes in both
2D and 3D cultures (see Figure 6-14). The presence of Dexamethasone in the media may
result in the upregulation of common genes such as Tyrosine Amino transferase (TAT) and
Tyrosine 2-3 dioxygenase (TO) (Figure 6-15).
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Figure 6-15: Other genes modulated by specific biochemical components in the media
PEPCK and 2E1 are downregulated by insulin, while TAT and TO are marginally induced by
Dexamethasone. Catalase is known to be affected and downregulated by peroxynitrites
induced during the isolation of the primary cells.
6.9 Summary of key results and conclusions
Using a broad spectrum of gene expression and biochemical activity metrics, the cell
phenotype during extended culture was seen to be substantially closer to that of liver in the 3D
microreactor compared to other traditional in vitroculture methods used in the Pharmaceutical
industry, particularly the 2D collagen sandwich cultures. Global transcriptional profiling was
first used to identify genes that are differentially expressed in the various culture formats at
multiple time points up to 20 days in culture, relative to the liver expression profiles in vio.
From these studies a specific time point to compare the basal expression levels of several
important CYP450 and Phase II metabolism genes was chosen. In addition, the basal
expression levels of transcription factors that regulate broad programs of liver-specific genes
was compared across various in itro culture systems, more quantitatively via RT PCR. It is
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hypothesized that the better preservation of the basal expression of the hepatic transcription
factors may possibly be responsible for more in vivolike expression of the liver specific genes in
the 3D microreactor than in the 2D collagen sandwich cultures. Genes that are likely regulated
by soluble factors present in the media were down-regulated in both the 2D collagen sandwich
as well as the 3D microreactor cultures. Such genes include the CYP2E1 (regulated by insulin
in the media), and specific transporters - the Bile Salt Export Pump (BSEP), the organix anion
transporter (OATP1 and OATP2), and the nulti drug resistance gene (MDR2). It is
hypothesized that the addition of specific biochemical stimuli such as HGF may help preserve
the expression of the specific transporter genes that are present specifically in the bile duct
cells.
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Chapter 7
Inducibility as a QuantitativeFunctional
Response Marker
While maintenance of basal expression of CYP450 is important for the use of in vitt
systems as predictive screens for mechanistic toxicity studies, inducibility of CYP450's by drug
like compounds is used as an important yardstick to fail new chemical entities (NCE's) during
pre-clinical testing [3, 50, 65, 159]. A characteristic of a subset of enzymes of the P450 super
family able to metabolize xenobiotic compounds, is their relatively low basal expression in the
absence of substrate and their highly elevated expression in the presence of their own
substrates or other inducer compounds. In particular, members of the CYP1A, CYP2B,
CYP2C, CYP3A, and CYP4A gene subfamilies are known to be highly inducible by certain
prototypical xenobiotics. This xenobiotic induction usually is tissue-specific, rapid, dosedependent, and reversible upon removal of the inducer [50, 131, 160, 161]. Most in itm tissue
models of the liver are known to lose their potential to be induced by common xenobiotics
that elicit an induction response in vivo. Thus, inducibility of CYP450's may be used as a
quantitative, fundamental assay to assess the phenotype of a tissue culture model. We
compared the effect of addition of prototypical inducers to 2D Collagen Sandwich cultures
(2DCSW) and 3D Bioreactor (3DB) cultures. 2D collagen sandwich cultures and 3D
bioreactor cultures were set up according to standard protocols described in the Appendix.
Prototypical inducers were added four days after cell isolation, and were added to fresh media
each day until day 7. Media in both the 2DCSW and 3DB cultures were changed everyday and
received the same concentration of inducer. The concentrations of all the inducers used were
optimized for maximal induction in 2D collagen sandwich cultures (basedon experimentsdonein
the Tannenbaum Lab, by Dr.Kevin Leach).
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7.1 The cue-signal-response hypothesis
The induction phenomenon was first recognized because they produced alterations in
pharmacological responses to drugs or other xenobiotics. Inducers are often substrates to the
induced enzymes; thus, enzyme activity increases only as needed. Second, enzyme induction
often increases detoxification, particularly when low to moderate concentrations of substrate
are present; thus under most conditions induction is a protective mechanism, whereby the cell
can detoxify lipophilic compounds that might otherwise accumulate [162]. Both characteristics
are likely to facilitate the survival of the cell in a potentially toxic chemical environment.
Induction of CYP450 enzyme activity can be disadvantageous in some instances. For example,
the induced enzymes often metabolize numerous substrates; thus enzyme induction by one
chemical may lead to increased metabolism of other compounds; if one happens to be a drug
of low therapeutic index, the increase in metabolism may lead to the loss of efficacy [163].
Occasionally, enzyme induction
may enhance chemical toxicity. For example, high
concentrations of the analgesic acetaminophen saturate detoxification pathways, leading to
reactions via cytochrome p 4 5 0 that generate reactive electrophiles, which bind to cellular
macromolecules and produce hepatic necrosis. CYP450 induction increases the severity of
acetaminophen toxicity by increasing the production of reactive metabolites and accentuating
the imbalance between
detoxification and activation pathways for the drug [164].
Understanding the molecular basis for the induction effect of small molecules, drugs,
environmental toxicants on the cellular phenotype, can help provide insights into gene
regulation of relatively broad interest. Typical changes seen in cellular phenotype as a result of
xenobiotic induction is the enhanced transcription of specific genes, including the CYP450's.
Great efforts have been made to decipher the molecular mechanism involved in the
CYP induction. Recent studies suggest that major classes of CYP genes are selectively
regulated by certain ligand-activated nuclear receptors (NR) [165-167]. The nuclear receptors
are a superfamily of genes coding for transcription factors that transform extracellular and
intracellular signals into cellular responses by triggering the transcription of NR target genes.
Nuclear receptors contain a DNA-binding domain and a ligand-binding domain, which is
similar to those classical steroid- and thyroid-hormone receptors [168]. The response element
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of a nuclear receptor is usually composed of two half-sites related to the hexamer AGGTCA.
The nuclear receptor superfamily also contains receptors for non-steroid ligands such as
retinoid acid, prostaglandins and fatty acids and so-called orphan receptors, for which no
physiologically relevant activators or ligands are yet known. In eukaryotes, DNA molecules are
tightly wrapped up by histone proteins forming a structural unit called chromatin.
Transcriptional activation of eukaryotic genes usually involves in acetylation of histone
proteins sequentially resulting in alteration in chromatin structure. According to the current
model of nuclear receptor activation, ligand binding induces significant structural changes in
the folding of the ligand binding domain of NR to form a hydrophobic patch that is accessible
to common co-activators and co-integrators [168]. Co-activators either possess intrinsic
histone acetyltransferase activity or recruit additional histone acetyltransferases that relieve
suppressive effects of the chromatin to activate transcription. Although some nuclear
receptors show certain differences in the transcription process, transcriptional activation by
nuclear receptors usually includes ligand-dependent dimerization of receptors, interaction
between transcription factors and a xenobiotic responsive enhancer, transmission of the
induction signal from the enhancer to a promoter and changes in chromatin structure. In some
cases, protein phosphorylation and dephosphorylation are also involved in the transcriptional
activation by nuclear receptors. Different CYP genes are regulated by different transcriptional
factors [169-171]. Liver-predominant expression of CYP is driven by distinctive liver-enriched
transcription factors. For example, inducible expression of CYP3A is regulated by ligandactivated nuclear receptor CAR (constitutively active receptor) and can be activated by a wide
variety of antibiotics, barbiturates and other drugs such as glucocorticoids [165, 166, 172]. For
experiments on induction of rat CYP3A's, done as part of this thesis, a non-glucocorticoid 16a Pregnelone Carbonitrile was used as the inducer. A complete list of prototypical inducers
of rat CYP450's as well as the primary ligand binding nuclear receptors/ transcription factors
involved in the induction pathway are listed in Table 7-1. In the case of CYP1A isoforms,
induction is through the binding of a ligand to the aryl-hydrocarbon receptor (AhR) [170],
translocation of the ligand-bound AhR into the nucleus, and association of AhR with its
dimerization partner AhR nuclear translocator (Arnt). The AhR-Arnt complex then binds to
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xenobiotic response element and turns on the CYP1A gene transcription. Similarly, for
CYP4A, induction is through the binding of a ligand, in this case, clofibric acid to the
(Peroxisome Proliferation Activation Receptor) PPAR-a nuclear receptor, followed by the
translocation of the NR to the nucleus, and association of the NR with the retinoid receptor
RXRa. In this case, the dimer binds to the PPRE (peroxisome proliferator response element)
present on the CYP4A1 gene. For the induction of CYP3A the nuclear receptor involved is
the Constitutively Active Receptor (CAR). Detailed description of the signalling cascade
mechanisms involved in the induction of specific CYP's studied as part of the experimental
protocol are discussed in specific sections of this chapter.
Although CYP induction is normally the consequence of an increase in gene
transcription, some none-transcriptional mechanisms also have been reported to be involved
[170]. For example, human CYP3A4 induction by troleandomycin appears to be nontranscriptional. Troleandomycin does not activate CYP3A4 gene transcription, but increases
enzymatic activity by preventing CYP3A4 proteins from degradation [162]. Similarly,induction
of human CYP2E1 by ethanol and isoniazid is not transcriptional, but it results either from
protein stabilization or increased protein translation [173].
In this study, 3-Methyl Cholanthrene, 16a Pregnelone Carbonitrile, and Clofibric Acid
were used as prototypical inducers to compare the response to 2D and 3D cultures. The
mechanism of action and regulation of the effect of these inducers have been well-studied and
characterized in both in tivo and in vitmro
systems [67, 174-182]. Separate experiments on 2D
collagen sandwich cultures were used to determine inducer concentrations that produced
maximal induction in 2D collagen sandwich cultures (Dr.KevinLeach, TannenbaumLab: seeFigure
7-3 for an exampledoseresponse).Inducers were added at the same concentration to both 2D as
well as 3D cultures. This helped obviate issues of mass transfer effects of inducers on the
tissue phenotypic response in 3D. At the same time, reported literature data were used to
verify that the chosen concentrations of the inducers were non-toxic to cultures. For e.g. from
Fugure 7-3, it is clear than a 10 ±Mconcentration of PCN produces a maximal induction in
2D cultures as measured by the amount of 63 hydroxylated product formed - a measure of
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activity of CYP's that are induced by PCN. The concentration of CLO and 3MC to be added
to both the 2D as well as the 3D cultures were similarly optimized.
CYP Induction Mediated by Nuclear Receptors
P450 inducing agents
Prototypic responsive
rat liver CYPs
Polycyclic aromatic
hydrocarbons
Phenobarbital
Dexamethasone
Fibrate drugs
Cholesterol
Bile acidsb
IAI,
2B1,
3AI,
4A1,
7AI
7AI
Thyroid hormone
P450 reductase
A2, 1131
2B2
3A2, 3A23
4A2, 4A3
Receptor
Ah receptor'
CAR
PXR
PPARa
LXRa
FXR
TR
'PAS transcription family member, not a nuclear receptor.
b Inhibitors of CYP7A transcription.
Figure 7-1: Prototypical inducers and nuclear receptors that mediate the induction of some of
the important CYP450's found in rat liver
(akenfrom
reference[179])
The role of nuclear receptors in gene induction and regulation has been described in
Figure 7-2. The binding of the heterodimer leads to the activation of transcription of the target
gene with the xenobiotic response element. In essence, the mechanism of induction of
CYP450's in the presence of xenobiotics may be considered to be composed of three main
components
- a cue (the xenobiotic), a signal (the cascadeof events thatfollow the initial binding of the
xenobiotic to the nuclear receptor),and a response (the downstreamupregulationof the targetgene).
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Xenochemlcal
XenoR-RXR
-
Retinoids
I4>
CYP Trnscriptof
1---
ERx
Figure 7-2: Role of nuclear receptors in CYP gene induction.
Shown is the structure of a xenobiotic receptor-RXR heterodimer (e.g., CAR-RXR, PXRRXR, or PPAR-RXR) bound to two copies of a hexameric DNA response element based on
the sequence AGGTCA spaced by Xnucleotides. The hexameric repeat can be arranged as a
DR, ER, or IR motif, as indicated. Naturally occurring DNA response elements for these
nuclear receptors are generally imperfect repeats of the AGGTCA sequence. The extent to
which retinoids that bind to RXR synergize with the xenobiotic in stimulating CYP gene
transcription is uncertain [179]
14
la
I-O
co
$_
T
0
Basal
IM
PCN
10 nM
PCN
100 nM
nM
PCN
100
pm
PCN
10 pm
PCN
100 pm
PCN
Figure 7-3: Dose response in 2D collagen sandwich cultures: Case of PCN
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7.2 Inducibility of 2D and 3D cultures using 3MC and clofibric acid
Figures 7-4 gives a schematic of the mechanism of induction of 3MC responsive
genes. No significant difference is seen in the basal or induced expression of CYP1A2 between
2DCSW and 3DB cultures in response to 72 hr. incubation with a 5 FtM solution of 3-Methyl
Cholanthrene (Figure 7-6 (a)). Again, even though it may not be prudent to compare fold
induction data across different labs that use different media formulations, a survey of the
literature was done to get an estimate of the range of fold induction data seen in different labs.
F
Induction of CYP450 1A2
72 hr. induction by 5 uM, 3-Methyl Cholanthrene, Culture Medium replaced
everyday.
/
/
9
Metaolism
f
nd v CYP1
rms
~~~~~~Mtaboisam
of Iinand by CYPi fnrmns
-- ".- - --1 - -- -·
(ED
"1QII'hMA1
,
%.a0
-affIma-
N
a
k
-R
t
3-Methyl
Cholanthrene
ARNT
4
AHR ligand _
AHR
CUE
L
\I-
-~
Increased
CYPlA1, CYP1A2,
transcription
CYPiB 1
A
I
/ j
?,
\r
-
RESPONSE
Figure 7-4: Mechanism of induction of 3MC-responsive genes
The fold induction seen in 2DCSW and 3DB cultures of CYP1A2 at the mRNA level
(9-14 fold over basal) is comparable to that reported in some in vivo studies in SpragueDawley rats, at both the activity and mRNA levels [143, 152].
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Figures 7-5 gives a schematic of the mechanism of induction of Clofibric Acid
responsive genes. Fold induction relative to basal as well as the induced expression relative to
in vivo of CYP4A1, in response to a 72 hr. incubation of a 100 1LMconcentration of Clofibric
acid, are not significantly different between 2DCSW and 3DB cultures (Figure 7-6(b)).
Induction of CYP450 4A1. 4a3 by Clofibric Acid
72 hr. induction by 100 uM, CLO, Culture Medium replaced everyday.
/r
F
----
cyp4a
"SIGNAL"
1*
j
(7j
41
PPARa ligand
r-I
Increased
r1YP4al
VP4AS(P4AS~~~~~~~~
transcription
v
CUE
.
- JA
,
j
-11-11
RESPONSE
I
Figure 7-5: Mechanism of induction of Clofibric Acid-responsive genes
The fold induction seen in 2DCSW and 3DB cultures of CYP4A1 at the mRNA level
(25-30 fold over basal) is comparable to that reported in some in vivo studies in SpragueDawley rats, at both the activity and mRNA levels [143, 152]. However, the basal expression
of CYP4A1 is significantly down regulated (nearly 3-fold on a log2 scale, p<0.05) relative to in
vivo,in 2DCSW cultures. The basal expression of 4A1 in 3DB cultures is comparable to that
seen in vivo (p>0.95).
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1 A2
6
4
4A1
+/- 1.2
,
(b)
(a)
5.9 +1- 0.8
4
2
0
; .,
'
|
-2
-4
3.7 +/- 0.2
3.1 +/- 0.6
·4
.
.. i
CO
72 hr. induction by 5 uM
3MC, Medium changed
everyday, N=4, n=3
U
-
..
o 0
72 hr. induction by 100 uM
CLO, Medium changed
everyday, N=4, n=3
-6
Figure 7-6: Results of prototypical induction studies: 3MC, CLO studies
(a): No statistical difference seen (p<0.0 5 ) in basal expression, induced expression relative to
in vivo as well as in fold induction over basal of CYP1A2, CYP4A1 between 2D and 3D
cultures. Data from N=4 different rats, with at least 3 replicates of each culture in each
experiment (b): No statistical difference seen (p<0.0 5 ) in induced expression levels relative to
in vivo as well as in fold induction over basal expression, between 2DCSW and 3DB cultures.
Basal expression of CYP4A1 is seen to be significantly downregulated (2.9 fold on a log2 scale)
in 2D over in vivo and 3D cultures (p=0.01). Data from N=2 different rats, with at least 3
replicates of each culture in each experiment
7.3 Inducibility of 2D and 3D cultures using PCN
Induction of CYP450 3A is used as an important yardstick to judge new chemical
entities (NCE's) during pre-clinical testing. The rat CYP3A1 and CYP3A2 enzymes share
many properties of human CYP3A4, a prominent and inducible pathway for human drug
metabolism. Figure 7-7 gives a schematic of the nuclear receptors and downstream signalling
cascade events that lead to the upregulation of the PCN responsive genes.
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Induction of CYP450 3A1 by 16-Preanenolone Carbonitrile
72 hr. induction by 10 uM, PCN, Culture Medium replaced everyday.
/101",
~~MPtahnlismnf inand
hv CYP3A fnrmc
F
"SIGNAL"
PXR ligand
CUE
-
-. 31
i11)
)
Ix
Increased
CYP3A1,3A2
transcription
OATP2, CYP2C6
L
\1
RESPONSE
Figure 7-7: Mechanism of induction of PCN responsive genes
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6
4
2
0
-2
-4
-6
-8
-10
-12
-14
Figure 7-8: Results from the PCN Induction studies
Significant downregulation (p<0.05) in basal expression of CYP2C6, 3A1 and 3A2 is seen in
2DCSW cultures relative to in vivo and 3D cultures. However basal expression of OATP2 is
seen to be significantly downregulated (p<0.05 ) in 2D as well as 3D cultures, relative to in
vivo. Both the induced expression relative to in vivo as well as the fold induction over basal of
the genes CYP2C6, 3A1, 3A2 and OATP2 is seen to be significantly (p<0.05) higher in 3D
cultures over 2D cultures. Data from N=3 different rats, with at least 4 replicates of each
culture in each experiment
Basal levels of both CYP3A1 and 3A2 are strongly downregulated in 2D culture (2-9fold) and are unchanged in 3D culture compared to in vivo (Chapter 6, Figure 6-13, & Figure
7-8). When induced with 16a Pregneleone Carbonitrile (PCN), the expression of 3A1
increases 7-fold in 2D culture, but remains strongly downregulated (2.6 fold). In contrast,
CYP3A1 expression in 3D culture shows basal levels comparable to in vivo and is induced 39fold (or 5.3-fold on a log2 scale - Figure 7-8) above basal - comparable to the levels reported
for in viv induction [160]. CYP3A2, though showing comparable -5-7 fold induction over
respective basal levels in 2D and 3D, remains strongly downregulated in 2D (induced level is
<5% of in vi).
Thus, while enzyme induction is observed in 2D culture for this important
enzyme family, the absolute levels are far below physiological, and thus any downstream
consequences of metabolism (e.g., toxicity events) would not likely be reproduced in the 2D
culture system. The 3D microreactor system provides an approximately physiological level of
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induction for CYP3A2, perhaps through better maintenance of cytoskeletal integrity needed
for optimal CYP3A induction [80]. Two other enzymes induced by PCN, CYP2C6 and
OATP2, also show more physiological behavior in the 3D microreactor than in 2D. CYP2C6
is mod and CYP3Arately downregulated and is not inducible in 2D culture, but shows normal
basal level of expression and 15-fold induction in 3D culture.
OATP2 is strongly
downregulated in both 2D and 3D culture, but is more highly inducible in 3D cultures (16fold) compared to 2D cultures (2.6-fold).
7.4 Key regulatory mechanisms that affect p450 basal expression and induction
Other studies in literature have also shown that there may only be minimal differences
in the response of a 2DCSW culture or a 3D static tissue system to the addition of the
prototypical inducers of CYP1A and CYP4A genes [80]. Our data supports the hypothesis that
there are negligible differences in the extent of induction seen in 2D and 3D cultures in
response to prototypical inducers of CYPIA and CYP4A.. Interestingly, unlike previous
studies we notice that the basal expression of CYP4A1 is higher in the 3D perfused bioreactor
cultures (and closer to in vi
levels) than the levels seen in the 2DCSW cultures. No such
differences are seen in the basal expression level of CYP1A in 2D and 3D cultures. However,
CYP1A is known to be pretty stably expressed in a number of different culture systems, and
has even been seen to be not very sensitive to the culture conditions used [80]. Remarkably,
the basal expression of CYP3A1, 3A2, and 2C6, are seen to be expressed at levels very close to
that seen in the in vivo liver in the 3DB culture (p>0.95), while the basal expression levels of
these genes are highly down regulated in 2DCSW cultures (p<0.05). This may be attributable
to the better maintenance of key liver transcription factors in the 3DB (Chapter 6), at levels
close to those seen in viw. Also, the induced expression relative to in vim, and therefore the fold
change with respect to basal of CYP3A1, 3A2, and 2C6, is seen to be higher in the 3DB
cultures than in the 2DCSW cultures. It has been shown in literature that cytoskeleton integrity
is a pre-requisite for optimal CYP3A induction [183] and studies in literature have emphasized
the need for matrigel substrata or a sandwich configuration for optimal induction of 2B and
3A genes [80]. Previous studies have concluded that while both 2D sandwich cultures as well
as 3D cultures on Matrigel, allow increased longevity of rat hepatocyte cultures and optimal
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induction of CYP3A's and CYP2B's, constitutive CYP dependent activities were all drastically
decreased in possibly all primary culture configurations [80]. However, unlike other 3D tissue
systems used in literature, the 3D perfused microfabricated bioreactor cultures helps maintain
even the basal CYP mRNA levels of a large number of very common CYP's at levels similar to
those seen in vivo.In addition, we see a much higher induction of CYP3A's and CYP2C6, in
the 3D bioreactor cultures, in response to PCN. The gene expression of nuclear receptors that
mediate the induction of CYP's through prototypical inducers, in in ivo liver, isolated
hepatocytes, 2DCSW cultures and 3DB cultures were also(Figure 7-9).
AHR
CAR
PXR
FXR
RXRax
PPARa
4
2
0
T LI
...............................
....f.....j.............T.......
T"
.................
IL4I
..
1._.'._._..
...I
-2
,
I
-4
-6
I
1
-R
-n
-103
Ml
IbUIslaLU
Hepatocytes
I
2D collagen
2D
collagen
sandwich
cultures
3D
3D3
microreactor
cultures
Figure 7-9: Basal expression of ligand-binding nuclear receptors
Significant downregulation (p<0.05) in expression of CAR, PXR and FXR seen in the 2D
collagen sandwich cultures. HNF4a has been shown to regulate expression of CAR and PXR
[184]. Downregulation of CAR may be one reason for the differences seen in the inducibility
of the 2D collagen sandwich cultures and 3D microreactor cultures by PCN. Data from N=3
different rats, with at least 4 replicates of each culture in each experiment.
Interestingly, the expression of AHR, RXRa and PPARa that mediate the induction
of CYP1A2 and CYP4A1 are comparable in both 2DCSW and 3DB cultures and similar to
expression levels seen in vivo. However, the expression of PXR, that has been shown to
mediate the induction and regulate the basal expression of CYP3A1, 3A2, 2C6 and OATP2
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[179, 184, 185] is seen to be downregulated in 2DCSW cultures compared to in vivo and 3D
bioreactor cultures. HNF4a has been reported in literature to regulate the expression of CAR
and PXR [184]. Thus, we may hypothesize that the differential levels of induction seen in
2DCSW and 3DB cultures even at high non-toxic concentrations of PCN that give maximal
induction in 2D cultures, may be due to differences in the expression of the liver transcription
factor HNF4
between 2D and 3D cultures which regulates the expression of the nuclear
receptors CAR and PXR that bind to the prototypical inducer. Thus the 3D microfabricated
perfused bioreactor is more sensitive to the addition of prototypical inducers that cause the
over-expression of CYP3A, 2C6 or OATP2's, than 2DCSW cultures. A large majority of
NCE's and xenobiotics are metabolized by or induce the CYP3A class of enzymes - thus the
bioreactor may serve to be a more sensitive model than the 2DCSW culture systems, as a
screen for CYP3A metabolism or inducibility studies. In addition, unlike most static 3D
culture systems (e.g. 3D matrigel spheroids) where the basal expression of a number of Phase I
and Phase II genes have been reported to be significantly down regulated, independent of the
nature of the extra cellular matrix environment used in the primary cultures [80]., the ability of
the 3D perfused microfabricated bioreactor to maintain the basal expression of many of the
Phase I and Phase II genes at levels very similar to those seen in vivo, indicates the suitability
of the system as a tool for xenobiotic toxicity studies and also points to the important role that
perfusion may play in maintaining liver-specific gene expression.
7.5 Relative inducibility of 2D and 3D cultures - a summary
Induction of CYP450's is used as an important yardstick to judge new drugs during
pre-clinical testing. The rat CYP3A1 and CYP3A2 enzymes share many properties of human
CYP3A4, a prominent and inducible pathway for human drug metabolism. Basal levels of both
CYP3A1/2 are strongly downregulated in 2D culture (2-9- fold) and are unchanged in 3D
culture compared to in viW (Figure.6-12, Figure 7-8(a)). When induced with 160a pregneleone
carbonitrile (PCN), the expression of 3A1 increases 7-fold in 2D culture, but remains strongly
downregulated (2-6 fold). In contrast, CYP3A1 expression in 3D culture shows basal levels
comparable to in tivoand is induced 39-fold (or 5.3-fold on a log2 scale in Figure 4a) above
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basal - comparable to the levels reported for in vivo induction [160]. CYP3A2, though
showing comparable -5-7 fold induction over respective basal levels in 2D and 3D, remains
strongly downregulated in 2D (induced level is <5% of in vim). Thus, while enzyme induction
is observed in 2D culture for this important enzyme family, the absolute levels are far below
physiological and thus any downstream consequences of metabolism (e.g., toxicity events)
might fail to be reproduced in the 2D culture system. The 3D microreactor system provides
an approximately physiological level of induction for CYP3A2, perhaps through better
maintenance of cytoskeletal integrity needed for optimal CYP3A induction [80]. Two other
enzymes induced by PCN, CYP2C6 and OATP2, also show more physiological behavior in
the 3D microreactor than in 2D. CYP2C6 is moderately downregulated and is not inducible in
2D culture, but shows normal basal level of expression and 15-fold induction in 3D culture.
OATP2 is strongly downregulated in both 2D and 3D culture, but is more highly inducible in
3D cultures (16-fold) compared to 2D cultures (3-fold).
CYP1A2 is among the most robust and inducible of the CYP450's [80] and exhibits no
significant difference in basal or induced expression levels in 2D compared to 3D culture, and
the magnitude of induction (Figure 7-6(a), 9-14 fold over basal) is comparable to that reported
in vivoin Sprague-Dawley rats, at both the activity and mRNA levels [143], [186]. These results
are consistent with previous reports of the relative insensitivity of induction levels of the
CYP1A and CYP4A genes in 2D collagen gel sandwich or 3D static culture [80].
The pattern of near-normal physiological CYP450 induction in 3D culture with
subnormal behavior in 2D culture is observed for clofibrate induction of CYP4A1 (Figure 76(b)). Although the magnitude of induction above basal levels appears greater for 2D culture
(60-fold) than for 3D culture (25-fold), the initial basal level in 2D culture is moderately downregulated in 2D culture but unchanged compared to in vivo for 3D microreactor culture, and
thus the induced level in 2D is far below the induced level in 3D. The magnitude of induction
in 3D is comparable to the value of 25-30 fold over basal observed in Sprague-Dawley rats in
viv at both the activity and mRNA levels [143, 152].
Previous studies have concluded that while both 2D sandwich cultures as well as 3D
cultures on matrigel allow increased longevity of rat hepatocyte cultures and optimal induction
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of CYP3A's and CYP2B's, constitutive CYP450 dependent activities were almost all strongly
decreased in possibly all primary culture configurations [80]. The results for 2D collagen gel
sandwich cultures in the induction experiments done as part of this thesis are consistent with
that conclusion. However, the same experiments also show that near-physiological basal and
induced levels of most CYP450s can be achieved in 3D microreactor culture. This result is
consistent with the more physiological expression levels of key liver-enriched transcription
factors and suggests that perhaps broader programs of liver-specific gene expression may be
obtained in 3D microreactor culture (Figure 6-13).
Based on the induction results, it is expected that the expression of the nuclear
receptor PXR that mediates the basal and induced expression of PCN-inducible enzymes [179,
184, 185] be more physiological in 3D than in 2D culture. Indeed, this is the case (Figure 7-9).
These results are also consistent with better retention in 3D of the liver-enriched transcription
factor HNF4a, which regulates the expression of CAR and PXR [184]. Expression levels of
nuclear receptors that mediate the induction of CYP1A2 and CYP4A1 -- AHR, RXRa and
PPARa -- are comparable in both 2D and 3D cultures and similar to expression levels in vivo,a
result consistent with the expression patterns of CYP1A2 and CYP 4A1. The Farnesoid X
receptor or FXR, that is known to regulate cholesterol synthesis and bile acid metabolism, is
also maintained at near in vivolevels in the 3D microreactors unlike the 2D cultures where they
are almost 4-fold downregulated on a log2 scale.
7.6 Summary of key results and conclusions
The key results and conclusions that may be drawn from the set of inducibility studies
that compared the basal and induced expression of CYP450's are:
1. Statistically, there is no difference in the basal and induced expression of CYP1A2 between
the 2D collagen sandwich and 3D bioreactor cultures. The levels of induced expression of
CYP4A1 relative to in tvi, are comparable in 2D and 3D cultures, however the basal
expression of CYP4A1 is better maintained in 3D cultures than in 2D collagen sandwich
cultures.
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2. The 3D bioreactors have been shown to be a more inducible (and therefore a more
sensitive) system than the standard 2D collagen sandwich cultures, in response to the addition
of the inducer Pregneolone 16a Carbonitrile.
3. The basal expression of key nuclear receptors, that regulate the basal and induced expression
of CYP's are maintained are close to in viva levels in the 3D bioreactors.
4. Liver enriched transcription factors - specifically HNFla and HNF4 a - that regulate the
expression of the ligand-binding nuclear receptors CAR and PXR, are maintained at close to in
vim levels in the 3D bioreactor, but are downregulated in 2D collagen sandwich cultures.
5. The basal expression of the organic anion transporter protein - OATP2 is downregulated in
both 2D and 3D cultures. This may be attributable to the absence of specific biochemical
stimuli in the media solution used in cultures [21]. However, the 3D bioreactors still retain
their ability to induce the transcription of OATP2 in response to the addition of the
prototypical inducer PCN.
6. The 3D bioreactor may serve as a more sensitive in vitroscreen than standard 2D cultures to
evaluate the induction potential of NCE's.
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Chapter 8
Conclusions and Recommendations
This thesis focused on the design, fabrication, modeling and characterization of a
microfabricated bioreactor system that mimics the key facets of the in tvio microenvironment
in the hepatic acinus by allowing for the three dimensional morphogenesis of liver tissue under
continuous perfusion conditions. A key feature of the bioreactor is the distribution of cells into
many tiny (-0.001 cm3 ) tissue units that are uniformly perfused with culture medium. The total
mass of tissue in the system is readily adjusted for applications requiring only a few thousand
cells to those requiring over a million cells by keeping the microenvironment the same and
scaling the total number of tissue units in the reactor.
A computational fluid mechanical model in ADINA® was used to describe the velocity
and shear stress distribution within the bioreactor. The model helped identify a set of
crossflow (perfusion) flow rates that simulated physiological shear stress conditions at the
tissue surface within a channel of the bioreactor. A mass transfer model in FEMLAB® was
used to describe the tissue distribution of metabolite concentration gradients. The mass
transfer model helped identify appropriate bioreactor operating conditions (Peclet and
Damkohler numbers) under which the tissue oxygen demand was theoretically satisfied. These
predicted conditions were then experimentally validated, by looking at the expression of
hypoxia-responsive genes as a function of perfusion rate. The mass transfer model may also
be used to determine the tissue disposition of any metabolite, so long as the requisite model
parameters - the Peclet Number and the Thiele Modulus or the Damkohler number are
known. It may also be used to determine the intrinsicreactionrate given the experimentally
measured apparent reaction rate at non-saturable concentrations of the drug (that has
embedded effects of mass transfer) or given the steady state concentration of the product of
reaction at the reactor outlet. (measured in a one-pass through configuration of the reactor).
However, the MilliF reactor with - 20,000-40,000 cells would not able to produce enough (or
detectable) amount of product metabolite in a one pass operation of the reactor given the
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reaction rates of most enzyme-substrate reactions of interest to the Pharmaceutical industry.
The giant reactor which accommodates at least 250 times the cell numbers seeded into the
MilliF reactor, may be used in a one-pass mode to get first pass apparent extraction rates that
can then be used in the mass transfer model to determine the intrinsicreactionrate.However, the
giant reactor must be operated in a one-pass back mix mode, so that the reactor volume (now
equal to the volume of the reactor plus the recycle loop) operates like a CSTR, similar to the
fluidic design suggested by Catapano and co-workers [187, 188]. The reactor volume (and
hence the reactor residence time) must be minimized so that local changes in concentration are
quickly equilibrated throughout the system. This fluidic design can help ensure that every
channel in the reactor sees a constant concentration of the drug, even at low doses. A
schematic of the fluidic design that may be used in the giant reactor is described below in
Figure 8-1.
One Pass Back mix Reactor
Qin + R, C
Qout = Qin, Cout
)
111
111t
I
I
11
'W"
2000 als / channel
20(06 clla
I
C
I channel(
-I
Recycle Ratio
Qr=-
As Qr> > 1, Cout - C', at steady
Qin
Back mix rxtr.: as Qr
state
O0Rxtr. has PFR characteristics
as Qr -oo
Rxtr. has CSTR characteristics
Figure 8-1: A one-pass back mix fluidic design for the measurement of one-pass extraction
rates at non-saturable concentrations of the drug
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Using a broad spectrum of gene expression, protein expression and biochemical
activity metrics, the liver tissue phenotype maintained during culture in the 3D bioreactor was
seen to be substantially closer to that of native liver than that of cells maintained in standard in
tite cultures, especially the 2D collagen gel sandwich cultures.. Global transcriptional profiling
was first used to identify genes that are differentially expressed between 2D collagen sandwich
cultures (2DCSW) and a variety of 3D cultures, including the 3D bioreactor (3DB), at multiple
time points up to 20 days in culture, relative to the expression profiles seen in liver in tvio and
compared to primary isolated cells. From these studies, a specific time point to compare the
basal and drug-induced expression levels of several important CYP450 and Phase II
metabolism genes more quantitatively via RT PCR and biochemical activity assays was
identified. Semi-quantitative RT PCR assays were used to compare basal expression levels of
key hepatic nuclear receptors and transcription factors that regulate the basal and induced
expression of the CYP450 genes and other broad programs of liver-specific genes. It is
hypothesized that the better preservation of the basal expression of the hepatic transcription
factors may possibly be responsible for the more in vivo like expression of the liver specific
genes in the 3D microreactor than in the 2D collagen sandwich cultures. In addition, the 3DB
cultures were also found to be more sensitive to induction by certain specific prototypical
inducers than the standard 2D collagen sandwich cultures. The better preservation of the
ligand-binding hepatic nuclear receptors in the 3D bioreactor is one possible reason for the
more sensitive response of the 3D bioreactor in response to the addition of inducers, than the
2D collagen sandwich cultures.
Genes that are likely regulated by soluble factors present in the media were downregulated in both the 2D collagen sandwich as well as the 3D microreactor cultures. Such
genes include the CYP2E1 (regulated by insulin in the media), and specific transporters - the
Bile Salt Export Pump (BSEP), the organix anion transporter (OATP1 and OATP2), and the
multi drug resistance gene (MDR2). It is hypothesized that the addition of specific biochemical
stimuli such as HGF (Hepatocyte Growth Factor) may help preserve the expression of the
specific transporter genes that are present specificallyin the bile duct cells. In
addition,
data
from Dr.Kevin Leach, a Research Scientist in the Tannenbaum Lab, have shown improved
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maintenance of the hepatic transcription factors and the CYP genes as a result of the addition
of anti-oxidants to the 2D collagen sandwich cell culture (either immediately after cell isolation
form the rat liver or at the time of plating). Antioxidants are known to help scavenge reactive
oxygen intermediates that are generated due to ischemic-reperfusion injury following liver
perfusion and cell isolation [80, 145] and can thus help minimize the downregulation of the
CYP proteins that is primarily caused due to the nitric oxide and/ or peroxynitrites produced
during isolation that causes specific blockage of the hemes of the CYP450s, resulting in
alteration of enzyme activities in vitro through mechanisms downstream of mRNA [151]. A
possible future work would be to study the effect of the addition of antioxidant treated cells
(or spheroids) into the bioreactor. It is hypothesized that this should help improve the
functional phenotype of the tissue in the bioreactor, as well as the tissue phenotype of the
spheroids formed from these cells.
This study has attempted to show the utility of three dimensional tissue constructs as
a more predictive screen for metabolism and toxicity, by looking at a very broad set of
fundamental tissue phenotypic as well as regulatory markers. The study has also identified key
regulatory mechanisms that may be responsible for better retention of hepatic xenobiotic
biotransformation phenotype in 3DB cultures over 2DCSW culture. We hypothesize that the
3D liver tissue constructs - because of their more in ivo like phenotype seen in short-term as
well as in long-term cultures; could also serve to fulfill the unmet need of suitable in vitro
models to study disease progression such as Hepatitis C. In addition, the microfabricated 3D
bioreactors are poised to be used to test and quantify the toxicity of known pharmaceutical
liver toxins such as Troglidazone, Acetaminophen, etc. The capacity of the 3D liver tissue
bioreactor system to maintain tissue phenotype over long periods in culture (at least upto 3
weeks) posits a hypothesis that they may be more responsive to chronic toxins, and those not
amenable to study by cryopreserved hepatocytes. Clinically, chronic overdose outcome is
inferior to intentional overdose, suggesting an important role for assessment of chronic
toxicity. In addition, a key set of experiments would be to test for the toxicity of compounds
(sourced from Pharmaceutical companies) in the rat 3D bioreactor model, of compounds that
were found to be toxic in human clinical trials, but not in traditional in vitm screens used by the
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Pharmaceutical industry. This can be done pending the ready availability of human cells for use
in the bioreactor.
The use of human cells in the bioreactor would help open up new application areas for
the bioreactor. While the biophysical and biochemical conditions that may be needed to help
maintain the tissue phenotype of primary human cells may be different from those needed for
primary rat hepatocytes, some of the learning derived from the use of rat hepatocytes in the
system can help accelerate the process of optimization of biophysical and biochemical
conditions for the retention of healthy human tissue phenotype in the bioreactor. The human
tissue constructs created in the 3D bioreactor may be used to test for the toxicity of known
human liver toxins. Concurrently, xenobiotics that were found to be toxic during late stage
human clinical trials, may be tested on the human liver tissue bioreactor to determine the
ability of the system to pick up the toxicity of such compounds.
The in tivo like microenvironment provided by the bioreactor also opens up the
possibility of its use as a tool to detect and isolate biomarkers. Genomics, Proteomics and
Metabolomics studies on a disease-modeled bioreactor tissue or a toxin-responding bioreactor
tissue [189] can help provide a 'systems-biology' approach to the identification of biomarkers
[190-193].
However, the industrial use of the bioreactor for one or more of the above high
throughput applications will require its re-design into a 6/24/96 well plate 3D perfused tissue
format. Such a format, will allow the system to transition from a 'high information content'
assay system to a 'high throughput, high information content' assay system.
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Appendices: Protocols and Experimental
Methods
Appendix 1: Isolation of primary rat hepatocytes using Percoll®
Appendix 2: Isolation of total RNA from isolated hepatocytes, in vivotissue, 2D and 3D
cultures
Appendix 3: Primer design procedures and guidelines
Appendix 4: cDNA Preparation and Reverse Transcriptase-Polymerase Chain Reaction (RTPCR) protocols
Appendix 5: Hepatocyte isolation and Spheroid Formation
Appendix 6: Measurement of the total number of viable cells in cultures using the measured
amount of total RNA and RT-PCR against the 18s gene
Appendix 7: Preparation of 2D collagen gel sandwich cultures
Appendix 8: Preparation of 3D spheroid and 3D Matrigel spheroid cultures
Appendix 9: Induction experiments on 2D collagen sandwich and 3D microreactor cultures
Appendix 10: Accession numbers for mRNA and complete cds sequences used
Appendix 11: Primer sequences used in RT-PCR studies
Appendix 12: Melting and annealing temperatures of designed primers
Appendix 14: Microarray Processing
Appendix 15: Testosterone Metabolism studies
Appendix 16: The Drug Development Process - an Overview
fTcnlg Inttt
Masacustt
Massachusetts Institute ofTechnology
9
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Appendix 1
Isolation of primary rat hepatocyte enriched fraction
using Percoll
Materials Needed: PercollU Solution (Sigma, P1644-1L), 10X HBSS (Gibco #14025-076),
Trypan Blue (Gibco #15250-0), aluminum foil, haemacytometer
1) Add 21.6mL of Percoll to 2.4mL of 10X Hank's Balanced Salt Solution in a 50mL conical
tube to create a Percoll concentration of 90% v/v and put on ice. (his must be done the day
of the isolation because the salt in the HBSS begins to dissolve the Percoll silica beads when
stored)
2) Dilute hepatocytes to a density of 5-7 million cells/mL in 24 mL of HGM. (Need a
minimum 125 million hepatocytes). Higher cell densities decrease the efficiency of separation
as contaminant cells are carried through the gradient and lower densities reduce the yield.
3) Gently mix 25 mL of cell suspension with 24 mL of the 90% Percoll suspension to form a
uniform isotonic gradient.
4) Centrifuge at 50 x g for 10 min at 4"C in a swinging bucket centrifuge (maximum
acceleration and brake setting of 3)
5) Aspirate the dead and compromised cell from suspension, and re-suspend the resulting
pellet in 45 mL of HGM.
6) Centrifuge at 50 x g for 3 min at 4"C in a swinging bucket centrifuge. Remove the
supernatant and re-suspend cells in the appropriate volume of HGM. Determine cell count
and viability using trypan blue exclusion on a hemacytometer. Keep cells on ice until use, but
try to process hepatocytes immediately, as they begin to dedifferentiate rapidly in suspension
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Appendix 2
Isolation of total RNA from isolated hepatocytes, in vivo
tissue, 2D and 3D cultures
Materials Needed: Trizol Reagent (Gibco #15596-026), Chloroform, Qiagen RNeasy Mini
Kit (Qiagen #74104), 100% Ethanol, Cell scraper, Electric blade homogenizer, RNase-free
water (such as DEPC-treated water), and RNase-free tips and tubes, ice bucket with lots of ice,
25gage needle, 3 mL syringe
Day 1: Collection of in vivo tissue samples, isolated hepatocytes samples, 2D and 3D
samples
For isolated hepatocytes
1) Re-suspend final known number of viable isolated hepatocytes in HGM at a density of -1
million cells/mL and place in a 1.5 mL eppendorf tube.
2) Centrifuge at 50g for 3 min. at room temperature
3) Aspirate medium using a 1000 tL pipetteman set to 800 iL being very careful not to touch
or suction the media-cell interface.
4) Add 1 mL of Trizol® reagent to the eppendorf tube
5) Mix the cells thoroughly with TRIzol®, use a 25guage needle to aspirate and pipette the
TRIzol® 15-20 times within the eppendorf tube. This helps homogenizing the cells.
6) *** store sample for further processing in the -80"C for upto 1 month ****
(note - Qiagen sells RNALater® a preservation media solution that needs to be used in case
the RNA samples will be stored for more than a month)
For in vivo tissue samples
1) Surgically remove 1-3 cm thick tissue samples (slices) from the rat liver taking care to
remove multiple samples from different lobes of the liver.
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2) Transfer the samples into separate centrifuge tubes (one tube for each lobe) filled with
TRIzol® (use - 2 mL of TRIzol® for each 1-3 cm thick piece)
3) Using a rotary electric blade homogenizer
(in the Tannenbaum
Lab) completely
homogenize the in vivo tissue samples in the four centrifuge tubes. The solid tissue samples
should be homogenized till very few solid pieces are seen in the centrifuge tubes. The
homogenization needs to be done thoroughly, else some of the RBC's (red blood cells) that
are left unhomogenized can clump together and clot.
5) *** store sample for further processing in the -80"C for upto 1 month ****
(note - Qiagen sells RNALater® a preservation media solution that needs to be used in case
the RNA samples will be stored for more than a month)
For 3D spheroids
1) Size separate the spheroids of the desired range (usually 100-300 glm),centrifuge the
spheroid solution at 50g for 3 min., aspirate the media out and add 1 mL of TRIzol® per
million cells taken from the spinner flask (i.e. roughly for every 3 mL of a 30 million cells
seeded spinner flask). Using a 25gage needed attached to a 3 mL syringe, homogenize the
spheroid sample till a clear solution is seen.
2) *** store sample for further processing in the -80"C for upto 1 month ****
(note - Qiagen sells RNALater® a preservation media solution that needs to be used in case
the RNA samples will be stored for more than a month)
For 2D collagen sandwich cultures and 3D Matrigel® cultures
1) Aspirate the medium out of the wells
2) Add 5001I of TRIzol® solution to each well of a 6 well plate. The volume can be scaled
accordingly for tissue culture plates of other sizes.
3) *** store sample for further processing in the -80"C for upto 1 month ****
(note - Qiagen sells RNALater® a preservation media solution that needs to be used in case
the RNA samples will be stored for more than a month)
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For 3D cultures
1) Disassemble the reactor and transfer the cell-scaffold into an eppendorf filled with 500-750
Vl of TRIzol® (in general 1 mL of TRIzol® is added per million cells, but adding too much of
TRIzol ® is not a big issue)
2) Using a 25gage syringe attached to a 3 mL syringe, aspirate and pipette the TRIzol® through
the cell-scaffold at least 15-20 times, to dislodge the cells from the scaffold
3) Take a look at the silicon chip under the microscope to make sure that the cells have indeed
been removed from the scaffold. In the rare cases when some cells still remain, place the
eppendorf with the scaffold in the sonicator and sonicate the sample for 5 minutes to remove
all cells from the scaffold
4) *** store sample for further processing in the -80"C for upto 1 month ****
(note - Qiagen sells RNALaterx a preservation media solution that needs to be used in case
the RNA samples will be stored for more than a month)
Day 2: Isolation of RNA from the in vivo, isolated hepatocytes, 2D collagen sandwich,
3D Matrigel® and bioreactor samples
For in vivo samples - take not more than 500 L of the homogenized sample stored at -80"C
(samples take 15 min. to thaw), from each centrifuge tube and transfer into a 1 mL eppendorf
tube
For the case of 2D collagen sandwich cultures - thaw the plates from the -80°C freezer, use a
cell scraper to scrape the collagen and cells off the plate. Use a 25 gage needle attached to a 3
mL syringe, to homogenize the collagen-cell solid pieces that remain after scraping - the
homogenization must be done thoroughly - no portion of the collagen-cell solid pieces must
be visible after homogenization. It takes nearly 45 min. to thoroughly homogenize one 6-well
plate. The homogenized solution from each well must be transferred to a separate 1 mL
eppendorf tube.
For the 3D Matrigelf cultures: repeat all the steps for the 2D collagen sandwich cultures
All other samples that are already in eppendorf tubes (isolated hepatocytes samples and 3D
bioreactor samples) can be removed from the -80"C freezer and thawed.
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1) Add 200 1iL (add 250 gLl/ mL of TRIzol® per mL of TRIzol® though there is no
disadvantage of adding more - only that the downstream steps will take longer as more
volume of sample needs to be treated for RNA extraction and cleaning) of Chloroform to
each eppendorf tube.
2) Vortex for 30 sec (Solution should become cloudy pink and be consistent throughout). It
will slowly phase separate. Leave solutions to stand at for 2 min. at room temperature.
3) Centrifuge in the cold room (4"C) at 12,000 rpm for 15 min to allow the samples to separate
into 3 phases. Transfer the samples onto ice.
4) Take upper clear, aqueous phase into a new 1.5 mL eppendorf tube paying particular
attention not to aspirate any of the pink interphase, using a 200
L pipetteman (approx.
acquire 400-700 FL of clear aqueous solution of RNA is recovered per mL of TRIzol
)
(NOTE - The bottom organic phase contains protein and DNA that will reduce the
quality/purity of you RNA sample if aspirated along with the RNA). Make sure that the
samples are on ice. Replace the un-used protein and DNA fractions into a cryobox and place
the same back into the -80"C freezer
5) Add an equal volume of 70% EtOH to the aqueous phase and pipette the solution up and
down till a clear solution emerges.
6) Label RNEasy® filter columns provided in the RNEasy® Mini Kit - one for each sample,
add 650 yIL (capacity of the columns is 700 VtL)of the ethanol-RNA solution, and centrifuge
for 15 sec. at 2000 rpm (room temperature).
7) Capture the column elutant in the collection tube and pass the collected elutant through the
same filter column once again i.e. repeat step 12, then discard elutant.
8) Repeat steps 12 & 13 until all RNA solution has been passed twice through the filter
column
9) Discard collection tube, replace the RNA containing filter column into .a clean new RNasefree collection tube (provided in the kit)
10) Add 500 jIL of RPE Buffer (provided in the kit, add ethanol to the RPE stock solution as
indicated on the bottle top - this has to be done when a new RPE bottle is used - ethanol has
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to be added only once to the RPE stock, RPE stock diluted in ethanol can be stored at room
temperature) to each filter column and spin for 15 sec (this helps dean the RNA out removes the contaminating DNA present in the sample)
Qiagen manual, pg 80
-
Replacing the collection tube before the RPE wash is essential because the RNA in EtOH is
'dirty' from the isolation process. The RPE buffer is cleaning the RNA, and if same collection
tube is used, the cleaning protocol can be compromised
11) Add 500 jiL more of RPE buffer, spin for 1 min, and discard the elutant. Centrifuge the
empty filter column with bound RNA, with a collection tube at the bottom, for 1 min. This
can help remove the last bit of RPE that remains in the filter.
12) Place column in a fresh 1.5 mL eppendorf (provided in the kit), add 30 L of RT-PCR
grade water (or DEPC water or Tris water) to the filter, taking care that the entire filter area is
wetted, and spin for -30 sec
10,000 rpm
20) The collected RNA solution may be checked for purity and quality, by measuring the
A260/ A280 reading in a spectrophotometer. Store RNA solution at -80"C - for long term
(more than a month's storage, it is advisable that the RNA is stored in a RNALater® stabilizing
agent).
~
Massachusetts~~
Massachusetts Institute of Technology
~~
ofTcnlg
22-BoehooyPoesEgneigCne
Inttt
Biotechnology Process Eineering
-202-
Center
Appendix 3
Primer design procedures and guidelines
Step 1: Identify Gene Sequence
>
Use NCBI Nucleotide Search (http://www.ncbi.nlm.nih.gov/)
to find the target
genes' mRNA sequence, as far as possible use complete cds or the mRNA sequence.
Indude species (rattus nonvegicus)and RNA type (i.e. mRNA, rRNA, etc.) in your search
>'
Step 2: Dengn Initial Pnimer
>
Input the resulting gene sequence into MITs Primer3 software (Whitehead Institute)
Specify the following conditions (shown in the field below as well)
o
100-200 basepair target sequence
o
o
18-30 nudeotides in length
45-55% GC content
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Massachusetts Institute of 'Technology
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Biotechnology Process Engineering Center
-
Try and keep the product melting temperature as high as possible, so that it is much
greater than the melting temperature of the primer-dimers and genomic DNA products
Keep the primer melting temperatures of the forward and reverse primers as close to
>
each other as possible
>
Software will give a list of candidate primers in ranked order as determined by their
algorithm
>
Selectprimersfrom thegeneratedoptionsthat:
>
Avoid runs of 3 or more G/C at the 3' end
> Avoid complementarities within the primer sequences and between the primer
> Avoid complementarities and mismatches of 2 or more bases at the 3' ends of the
primer and the target-template sequence
> Avoid a 3' end T pair
> Use primers that give a product with a high melting temperature
> As far as possible, make sure to select a primer set that binds to a sequence on the gene
that lies in the intron-exon boundary (this can be known by looking at where the primer
binds to in the mRNA sequence and then comparing the sequence where the primer
binds to with the complete cds sequence)
Step 4: Verijy Targets of Ptimer Design
>
>
Click on the 'short,
Go to NCBI Blast (http://www.ncbi.nlm.nih.gov/BLAST/).
nearly exact matches link' and input the determined primer sequence into the input box.
Click on the BLAST tab and look up the set of genes that are listed each of which can be
an RT-PCR product for chosen primer set.
The primer sequence should yield the target gene with considerably greater significance
than the next closest match for the same animal species (Rattusnorvegicus)
Similarities between the gene specificity of different species is not important
- 204 Massachusetts Institute of Technology
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Appendix 4
cDNA Preparationand Reverse TranscriptasePolymerase Chain Reaction (RT-PCR) protocols
Material Needed: Deoxyribonuclease I (Invitrogen #18068-015), Omniscript Reverse
Transcriptase Kit (Qiagen #205111), QuantiTect SYBR Green PCR Kit (Qiagen #204143),
T7-Oligo(dT) 24 primer (Affymetrix #900375), RNase inhibitor (Ambion #2684) designed
primers for genes of interest, RT-PCR grade water, DNA Engine Opticon, RNase-free tips,
tubes, and 96-well plates, oligo-DTs and random hexamers (Qiagen, CA)
Step l: Quantifiation of RNA concentration
1) Add 2 LLof RNA sample to 98 tL of DEPC-water (1:50 dilution) (Witrup UV
requires 200 1 L of sample or 4 RNA:196 H 2 0O- use lower dilutions if there is too little
RNA sample to give a good spec reading)
2) Blank UV Spec with DEPC water (or whatever the dilutant is)
3) Spec samples at 260 nm and 280 nm
(NOTE - The UV spec in the Witrup Lab has much more precision and accuracy than
Leona's)
Determine quality (QRN,)and quantity (WRN)of sample:
RNA
WRNA= A260
QRNA
,
mLJ
I.mpl
]ua,.or
[pL]
where aRNA =40
1.7 > QRNA> 2.1
A280
4) If the quality of the RNA sample falls outside of the defined range it should not be
used.
Step 2: DNase Treatmentof RNA
1) Add the following to an RNAse-free, 0.5 mL Eppendorf, on ice
·
200 ng RNA sample
*
1 iL 10X DNAse I reaction buffer
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·
1 tzL DNAse
·
DEPC treated water to make upto 10 ~LL
I, amplification
grade, 1
(Note: reaction is optimized from 50ng-2[g of RNA. Regardless, of the initial amount of RNA be sure to
perform the RT reaction at the same RNA concentration everytime to standardize experimental error)
2) Incubate tubes for 15 min at room temperature.
3) Inactivate the DNAse by adding 1 tL of 25 mM EDTA solution to the mixture.
4) Heat for 10 min. at 65"C. After that, keep the mixture on ice for use in the reverse
transcription, or store at -80"C until its time for cDNA preparation.
Step 3: ReverseTransciption:Total sample volume before the start of this step: 11 [zL
1) Make fresh dilution of T7 primers (or random hexamers) if 18s gene also needs to be
transcribed to cDNA), and RNase inhibitor
·
RT buffer from 10x to x in RT-PCR H20 (1:10 dilution).
·
RNase Inhibitor (40 U/iL stock) to 10 U/xL in RT buffer (1:4 dilution)
*
T7 primer (50 FM stock) to 10 VtMin RT-PCR H2 0 (1:5 dilution) or random
hexamers (dilute stock in RT-PCR water i.e. RNAse free water to 10 FM)
2) Prepare the master mix (everything in table below except the RNA template) on Ice
(the values below are per 11 jiL solution of 200 ng of DNA'sed RNA). If random
hexamers are being used, use same volume as oligo-DT in Master mix.
CempN"a
Find eo wi dk,
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3) Add the appropriate amount of master mix - 9 .L to a 11 ZILsample of DNA'sed
RNA i.e. 9p.L per 200 ng of starting RNA
4) Do make 2-3 samples of no-RT control using the RNA from some of the samples, ie.
add 9 IlL of a special Master Mix solution that has all the components listed in the
table above except the Omniscript Reverse Trasnscriptase. Instead of the 1 L
Omniscript Reverse Transcriptase enzyme, use 1 L of DEPC/ RNAse free/ RT-PCR
water in the special Master Mix.. The no-RT control can be used during the PCR step
to see if all the genomic DNA has been digested by the DNAse step. Ideally we should
see no amplification of the no-RT controls during PCR.
5) Centrifuge the samples with the master mix added at 2000 rpm, room temperature and
then incubate @ 37"C for 60 min. After that time, store cDNA
@-80"C (or -20°C)
until PCR
(NOTE: cDNA is much more stable than RNA, its often good to store cDNA than RNA
for long time periods)
Step 4: Quantification of cDNA concentration
1) Add 2 gIL of RNA sample to 98 pL of DEPC-water (1:50 dilution) (Witrup UV
requires 200 jgLof sample or 4 RNA:196 H 2 0 - again use lower dilutions (more
concentrated cDNA sample) if there is too little cDNA that a good spec reading is not
possible.
2) Blank UV Spec with DEPC water (or whatever the dilutant is)
3) Spec samples at 260 nm and 280 nm
(NOTE: The UV spec in the Witrup Lab has much more precision and accuracy than Leona's)
4) Determine quality (Qc,,,)
=
and quantity
A
L
1.8
> QNA
ecDNA
QDNA = A26
(WINA)
facor
1000[I]
>
of sample:
where
soN A
= 33
2.0
A 28 0
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5) If the quality of the cDNA sample falls outside of the defined range it should not be
used.
Step 5: Real-Time PCR
1) Prepare master mix according to the following table (1.5 [.L of forward and reverse
primers per 1 Figof cDNA, add that amount of RNAse free water to make the total
reaction volume equal to 50 tL). The master mix is everything in the table below
except the template cDNA.
2x WWTO V Gf..t..
2S..
m
MdW AMd'
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2) Aliquot master mix into white PCR tubes or plates (MJ Research, MA)
3) Add 1 Fdof the cDNA template to 49 Vl of the master mix.
4) Cover the plate/ tubes with clear caps (DON'T forget to do this!)
5) Program DNA Engine Opticon as follows. Line 3 should be changes depending on
the annealing temperature of the designed primers. Do not run for more than 45
cycles.
' -- Prot
toco
.
I
P
i:
II
I
I
iI
_
1. Incubate at 95.0 C for 00:15:00
2. Incubate at 94.0 C for 00:0015
3. Incubate at 57.0 C for 00:01:00
4. Incubate at 72.0 C for 00:00:30
5. Plate Read
& Goto ine 2 for 45 moretimes
7. Incubate at 72.0 C for 00:02:00
8. MeltingCurvefrom 50.0 C to 98.0 C, read every 1.0 C, hold 00:00:01
END
iI.
f eholg nsiut
Maschstt
Massachusetts Institute of echnology
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Appendix 5
Hepatocyte isolation and Spheroid Formation
Hepatocytes were isolated from 150-230 g male Fischer rats with a modification of
Seglen's two-step collagenase perfusion procedure [101] as described previously [47]. The
resulting cell suspension was centrifuged three consecutive times at 50g (2 min each). Next,
21.6 ml of Percoll (Sigma-Aldrich, St. Louis, MO), was mixed with 2.4 ml of Hanks Balanced
Salt Solution (HBSS, Sigma, MO), and added to 25 ml of the centrifuged cell suspension at a
cell density of 5-7 million cells per milliliter of solution, as described in the literature [102]. The
solution was further centrifuged at 50g for 10 minutes. Percoll isogradient centrifugation
resulted in the separation of dead cells as well as a significant portion of the non-parenchymal
cells in a floating top layer that was discarded. The sedimented hepatocytes were then resuspended in hepatocyte growth medium (HGM) [103], [47] but without hepatocyte growth
factor (HGF).
The final cell viability, as determined by trypan blue exclusion, was
approximately 90-95%. Spheroidal cell aggregates were formed in suspension cultures similar
to those described by Wu and co-workers [20] wherein one hundred milliliters of HGM was
mixed with 30 million cells from the Percolled cell suspension solution in a 250-mL spinner
flask (Bellco Glass, Vineland, NJ). The flasks were stirred at 85 rpm for up to 72 h. On the
third day, spheroids of the desired size range (100 - 300
.un)[47]., were separated using
appropriately sized filter meshes (Sefar America, Kansas City, MO) and re-suspended in 25 mL
of rinse medium. The rinse solution comprised phenol red-free Dulbecco's modified Eagle's
medium (DMEM) with sodium pyruvate (110 mg/mL)
and glucose (1 g/L)
(Life
Technologies, Rockville, MD) supplemented with bovine serum albumin (2 g/L, Sigma, St.
Louis, MO) and penicillin-streptomycin
centrifuged at
4 0g
(100 U/mL). The size-separated spheroids were
for 3 min. The floating debris was then removed and the spheroid pellet
was re-suspended in 30 mL of HGM.
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Appendix 6
Bioreactor Seeding and Maintenance of Culture
imaging & spectroscopy
vent filt
The bioreactor system was primed with the rinse solution to passivate the bioreactor,
connector and tubing surfaces, and to remove air bubbles from the flow paths. Prior to
seeding, reservoir bottles were aspirated and refilled with 15 mL of HGM.
For hydrodynamic seeding, a syringe filled with 1 ml cell-spheroid suspension was
placed at the inlet of the upper chamber (port #1) and a second syringe containing -0.5 ml of
PBS was placed at the bioreactor upper outlet (port #2). The bottom bioreactor outlet (port
#3) was unclamped by removing the tubing from the peristaltic pump rotor and the bioreactor
was tilted approximately 450 with the bioreactor outlet (port #2) higher than the inlet (port
#1). The cell-spheroid suspension was manually injected into the upper chamber at the flow
rate of -0.5 ml/min. The spheroids entered into the scaffold channels through a combination
of settling and flow of cell culture medium from the upper chamber into the lower chamber
aided by the resistance of the syringe on the bioreactor upper outlet (port #2).
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Following seeding, cell culture medium was pumped through the upper bioreactor
chamber at 0.5 ml/min (with pump A) and collected in a waste container for one minute to
clear cells from the top surface of the scaffold.
The upper recycle tubing was then
reconnected to the bioreactor (port #2). At this time, the bottom tubing was inserted into the
peristaltic pump B and the perfusion flow rate (cross-flow) down through the seeded channels
was set to a desired flow rate. The selection of flow rates was based mainly on physiological
shear stress conditions and mass transfer considerations (Chapter 3). After one hour, cell
culture medium in the reservoir was replaced with 15 ml of fresh medium to reduce residual
cells/debris trapped in the circulation loop.
Spheroids seeded into the channels are initially held in place by the membrane or filter,
and after initial, attachment and reorganization (-1 day), by adhesion to the collagen-coated
channel walls. During the "top down" perfusion, the residual cell debris is likely to gradually
dog the micro porous filter. To maintain a constant perfusion rate and keep the filter in the
bioreactor free of debris, 24 hours after the seeding we placed an inline filter between pump B
and the reservoir, and reversed the direction of the cross-flow while maintaining the same
value of the perfusion or crossflow rate. We kept this direction of the cross-flow for the
duration of the experiment. For five-micron pore-size DURAPORE filter (Millipore Corp.,
Bedford, MA) in the bioreactor, we used a 0.8/0.2-pm pore-size double layer inline syringe
filter (Pall Corporation, Ann Arbor, MI). In this way, cell debris capable of clogging the filter
in the reactor was captured in the inline filter. By replacing the inline filter every 72 hours, a
constant "bottom up" cross-flow was maintained through the channels with cells. In case of
the control "zero-cross-flow" bioreactors, 24 hours after seeding the bottom support silicon
scaffold with channels was removed and replaced by a solid, 230 Vtm thick polycarbonate
piece. The reason for using the solid polycarbonate piece was to drastically reduce gas
exchange between cell culture medium in the bottom reactor chamber and the cells in the
upper scaffold. Therefore, the cross-flow pump B was used only in the bottom-down flow
direction immediately following the seeding and was disconnected when the blocking lower
scaffold was installed. Cell medium was changed every 3 days by replacing the reservoir with a
new one containing 15 mL of fresh HGM.
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Appendix 7
Measurement of the total number of viable cells in
cultures using the measured amount of total RNA and
RT-PCR against the 18s gene
Total RNA isolated from the cells in the 2D and 3D cultures was used to quantitate
the total number of viable cells in the culture and the average viable cell number per channel in
the scaffold (in case of the 3D bioreactor culture). Total RNA was isolated and purified from
the cultures as described in Appendix 2. cDNA was prepared from the total RNA as described
in Appendix 4 and RT-PCR against the 18s gene (designed as described in Appendix 3) was
performed as described in Appendix 4. In order to determine the unknown viable cell number
in the scaffolds, the total RNA from a number of samples of Percoll-separated hepatocytes of
known cell densities were isolated. Total number of viable cells in a bioreactor was determined
from the total RNA versus number of isolated hepatocytes standard curve (similar to Figure 67 in Chapter 6). The number so determined was validated using the 18s standard curve plotted
using C,r values generated from RT-PCR reactions against the 18s gene done on all the cell
standard (similar to Figure 6-6, in Chapter 6).
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Appendix 8
Preparationof 2D collagen gel sandwich cultures
Two dimensional collagen sandwich cultures were prepared similar to previous
methods suggested by Dunn and co-workers and later reviewed extensively in literature [18,
44, 122, 194]. 35 mm tissue culture treated six-well plates (Falcon, USA), were coated with 600
ul of a collagen mixture (4.7 ml sterile water, 1.3 ml of modified PBS solution [g glucose,
1.85g sodium bicarbonate, in 50 mL PBS solution], 6.9 ml of a 3mg/ml collagen (type I)
solution (Vitrogen, USA)), and with pH of solution adjusted to - 7.4. Plates were coated
overnight to allow the collagen to gel, in an 8.5% C02, 37°C environment. Percolled primary
rat hepatocytes were seeded at a density of 50,000 cells/! cm2, and evenly distributed on the
plate, in lmL of Hepatocyte Growth Medium (HGM). Following an overnight incubation at
37'C and 8.5%C02, that allowed the cells to attach to the substratum, the media was aspirated
from the plate and 300 ul of the collagen mixture was added to form the second layer of the
sandwich. The collagen was allowed to gel over a 45 -60 min period, following which 1 mL of
HGM was added to the well. The cultures were incubated in a controlled 37°C, 8.5%C02
environment. Media was changed everyday.
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Appendix 9
Preparationof 3D spheroid and 3D Matrigel spheroid
cultures
30 million percolled isolated primary rat hepatocytes were added to 100 mL of HGM
in a 500 mL spinner flask (BellCo, NJ, USA) on a spinner table set at 85 rpm [20]. The cells
aggregated to form spheroids. Flasks were taken down on Day 2 and Day 3 after cell seeding,
for RNA extraction. Day 3 or Day 4 spheroids were used to seed the bioreactors.
BD Biocoat'M Matrigel Matrix 6-well Multiwell plates were used. The Matrigel plates
were kept frozen at -20° until the day of use. On the day when cells are to be seeded on the
plates, the multiwell plate is brought to a temperature of 4°C over a twio hour incubation
period. The plates are then placed in a tissue culture laminar hood for 30 min. Next the plates
are transferred to an incubator at 37°C and 8.5%CO2 for a period of one-hour. Percolled
hepatocytes are added to the plates at a density of 80,000 cells/cm2 (As suggested in supplier's
optimized protocol). 2mL of HGM media are added to each well after the addition of the
hepatocytes. Medium was changed every other day, as suggested by the supplier. Within three
days the cells forms into spheroids, and the cultures are taken down after 7, 13 and 20 days
after cell seeding.
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Appendix 10
Induction experiments on 2D collagen sandwich and 3D
microreactor cultures
Media was changed in both the 2D and 3D cultures everyday, and prototypical
inducers were added on Day 4 after cell seeding. Control cultures that did not receive the
inducers, were treated with an equal volume of the solvent in which the inducers were
formulated (e.g. DMSO for 3MC, HGM for CLO and DMSO for PCN). Cultures were taken
down on Day 7 for RNA extraction. Inducer concentrations that produced maximal induction
in 2DCSW cultures relative to basal were used - 10 FiMPCN, 5 FiM3MC, 100 1tMCLO.
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Appendix
11
Accession numbers for mRNA and complete cds
sequences used
The specific mRNA or cDNA sequences obtained from the NCBI database accessible
at http://www.ncbinlm.nih.gov/entrez/query.
fcgi?db=Nucleotide was used to design primers
using the primer3 software at the Whitehead
bin/primer3/primer3_www.cgi).
Institute (http://frodo.wi.mitedu/cgi-
Specific accession numbers
used include X63410 -
Hydroxysteroid Sulfotransferase, IK01931- GST-Ya, M13506 - UDPGT, NM_022180 -
HNF4a, NM_012742 - HNF3a, XM_343991 - HNF1P, NM_012669 - HNFl,
NM_012524 - CEBPI. NM_024125 - CEBPa, AF082126 - AHR, AB105071 - CAR,
AF151377 - PXR, NM_013196 - PPARlX, U18374 - FXR, NM_012805 - RXRoc L00319 CYP2B1, XM_2152557 - CYP2C6, U33173 - CYP2C11, X64401 - CYP3A1, NM_153312 CYP3A2, NM_031543 - CYP2E1, NM_012541 - CYP1A2, NM_175837 - CYP4A1,
NM_012942 - CYP7A1, NM_131906 - OATP2 (or Slc21a5), AJ277827 - Hif3a, CYP7A1
J02722 - Heme Oxygenase-1, X01117 - 18s housekeeping gene.
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Appendix 12
Primer sequences used in RT-PCR studies
Oligonucleotide
primers
for
CTTCAGTTCCAAGGCCAAG-3
Hydroxysteroid
forward
and
reverse), UDPGT
CATCA-3
forward
and
reverse), HNF1P
forward and 5-
reverse), HNF-4a (5-CTGAGACTCCACAGC
5-CTAGATGGCTTCCTGCTTGG-3
TCAACGCTTGT'rCGTCAAG-3
forward
and
reverse),
forward and 5-CTL'CAAC
GCGTCTGTACCA-3 reverse), HNFlat 5-GCCATCTGGGTGGAGATAAA-3
reverse),
reverse), CEBPO (5-ACTTCA
GCCCCTACCTGGAG-3 forward and 5-GGCTCACGTAACCGTAGTCG-3
forward
and
reverse), CAR (5-GGAGGACCAGATCTCCCTTC-3
ATCITGT-3
GTTTCATGGCCCTTCTGAAA-3
forward
and
forward and 5-GACCGCATCTITCC
reverse), FXR
forward and 5-GCCTCTGCTCGATGTCCTAC-3
GTCAAGCAGCAGACAAGCAG-3
and
ATGGACT-3
reverse),
AG-3
forward and 5-GGAAGTGTTC
CYP2C11
forward
(5-CAAGTACCAC
forward and 5-GTGGGGACAAGGTCAATATATCTC-3
reverse), CYP3A1 (5-CATCAGAGGCCCAGCTAGAG-3
forward and 5-AGGACCCAGGT
TITCCAGTGT-3 reverse), CYP3A2 (5-GTAGTACTCTTTCCATTCCTCACCC-3
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5-
reverse), RXRa (5-
reverse), CYP2C6 (5-TGGGGAAAAGGAACATGAG-3
5-GGATAAACGTGGGGTCACAG-3
TCTGAGGT
and
(5-ATGTCCGGA
forward and 5-GAGAAGGAGGCAATCAGC
reverse), CYP2B1 (5-CAACCCTTGATGACCGCAGT-3
AGGATrGGAAGC-3
forward
reverse), PPARa (5-TCACACAATGCA ATCCGTfT-3
5-GCCAGAGATTTGAGGTCTGC-3
GTTCTGTCAGG-3
reverse), AHR
5-AITCATrGCCAGGAAACCAG-3
(5-GGTCTTCAAATCTGCCGTGT-3
reverse), PXR
forward and
CEBPa (5-GCCAAGAAGTCGGTGGA
TAA-3 forward and 5-CGGTCATTGTCACTGGTCAA-3
(5-ACTACACGCCAGACCAGCTT
HNF3a (5-
5-TCACACTTGAAGCGCT1lTG-3
(5- CGTAGCAAGGAACTCCCAAG-3
5-ACCAGTCCCACAGTGTCCTC-3
(5-
forward and 5-GTITTGCATCCAT
(5-GCCTTCTCACTGCCTIGAAG-3
GTCTTACGGCAACAAAAGAGGCAG-3
(HSST)
5-GTGAAGTGAITCTTCCAGTC-3
reverse), GST-Ya (5-CTGGCAAAAGACAGGACC-3
GGGAAGC-3
Sulfotransferase
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forward
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and
CYP1A2
(5-TATCTACG
forward and 5-CTCTATGAGGAGACAGTCAGTCACA-3
ATACCTACCTGGAAGCC-3
reverse),
CYP2E1
reverse),
5-GGTGCTTATGCTTAGAATCCAGAC-3
5-
and
forward
(5-GGGGAAGAACCCACACCTAT-3
GTCATCCCCCTGCTTCACTA-3 reverse), CYP4A1 (5-TCATGAAGTGTGCCTTCAGC-3
forward and 5-GATGTTCCTCACACGG
AAGAG
GCTAAGACCGCCTTC-3
forward and 5-CCTCTGGCGAAGAAACTCTG-3
reverse, Hypoxia Inducible Factor GAGAGG-3
forward
and
AGAACGGGTTGATITC-3
OATP2
3-alpha
reverse),
(HiF-3x) -
(5-TCACATACTGCGAC
reverse,
5-TGCTGACTGCATCAGAGTCC-3
forward and 5-AAAAC
(5-CAATTCGGTATCCCCACATC-3
ACCAATC-3
Heme Oxygenase I (5-
GAGT-3 reverse),
18s
(5-TTTGG
GTGACCATGCTTCC-3
forward
and
reverse),
5-CTGCACATCCAAC
(5-GCAATTATTCCCCATGAACG-3
forward
and
5-
GGCCTCACTAAACCATCCAA-3 reverse) were designed using the Primer3 open source
bin/primer3/primer3_www.cgi)
software (website link: http://frodo.wi.mit.edu/cgi
and
synthesized by Qiagen, CA.
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Appendix 13
Melting and annealing temperatures of designed primers
Gene
OptnimumProduct Melting
Optimum Annealing
Temperature (C)
Temperature (C)
P450's - Phase I Metabolism Genes
CYP 1A2
51
83
Not optimized
Not optimized
55
81
51
79
55
81
CYP 2E1
55
78
CYP 3A1
CYP 3A2
59
80
55
75
CYP 4A1
55
79
CYP 4A3
55
79
CYP 7A1
51
'77
CYP
CYP
CYP
CYP
2A1
2B1
2C6
2C11
Phase II Metabolism Genes
GST-Ya
UGT
ST-1A1
-~~~~II.i.54
82
58
83
59
79
IE
I"--I"II~I-II--~~"~YIWIIU·U·C.·--
ii
11r~lrIIIIIIE~lI
I
i 111111 iII
IlI IlElIIIIlIEl
-
-
I
---
i.E
IE- II-
---
-l
-
-
· IIIYIII_·1L
-l
--
~~~~~~~"p""e"""""C"II-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
3*JIN
~C
-- Es'-'.0h.atti^.c.1.
------- ----- ------
1~9*r3
W~C~')Z:~
IrF~~~~Yw
vya~~~~~~~~~t
'
-r
-
-,
-
---
r--.
Stress/ proliferation markers
p
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5')
82
59
82
Not optinmized
--219219-
Not optimized
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I
I ...
MM"
~
vfi-4-.
v.Xk'.: -
~
mm'salffilm
1=
MINAM
111.l~b~
aU
- r·· .·- - -- I.,...."··~-.
.. .
f K ~'
MONTE.
.14'20
Oll''
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I''I AI ,I' , I~
;~ti
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·_1. .i
i.' .. f:f:
. . . f-. ....
.-.. . .- ...--.
:. ~@tMt-,
V, wrig
~sli
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t.
,.,
04.-
~~f~i~~t~s~~e~
~~~~~~~ii~~~~~~~~~~~~~~~~~~~t~~~~~~~~~~~
. IFE
a
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rw!
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t
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BSt#
50-63
MDR-2
OATP-2
.
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.
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_
76
76
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Appendix 14
MicroarrayProcessing
The Affymetrix small sample labeling protocol (www.affymetrix.com) was used for all
total RNA samples to generate labeled cRNA. Labeled samples were hybridized in triplicate to
the Rat Genome Array (U34A) Gene Chips. Data from arrays were processed using nonlinear normalization approach through DNA-Chip Analyzer version 1.2. The average and the
standard deviation of replicates were calculated and probes that were "Absent" throughout the
data set were eliminated from further analysis. Fold changes were calculated by dividing the
mean of replicates of the culture condition by that of intact liver tissue for each gene. Quality
filtering using coefficient of variation (CV) was performed to data with high variability. Using
S-Plus software (Mathsoft, Cambridge, MA), a 95% CV quantile was calculated for each
sample. Significance was assigned for samples within 95% CV threshold and samples with
high variability eliminated. The number of probe sets remaining after filtering was 2098
probes (23.84%). More detailed microarray protocols are available elsewhere [130].
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Appendix 15
Testosterone Metabolism studies
Fresh albumin-free Hepatocyte Growth Media (HGM) was added to both the 2D
Collagen sandwich (2DCSW) as well as the 3DB (3D Bioreactor) cultures. The 2DCSW
cultures were shaken gently on a shaker kept inside an incubator that provided a controlled
37°C, 8.5%C02 environment. Media was changed twice in the 2DCSW at 30 minute intervals
to help wash out the albumin present in the collagen. Media was changed twice, again at 30
min intervals, in the 3DB to clean out the rat albumin present in the system. 250 jlM
concentration of testosterone was made up in fresh albumin-free media solution, and was
added to the 2DCSW cultures as well as the 3DB. Products of metabolism of testosterone
were collected 60 min. after the addition of the substrate to both the 3DB and the 2DCSW
cultures. The use of the shaker with the 2DCSW helped eliminate concentration gradients in
the media. Linearity of the Michaelis-Menten kinetics was verified over this time-span (data
not shown). To the samples from the 2DCSW and 3DB cultures, a known concentration of an
internal standard (Methyl Testosterone) was added. The samples were them vacuum dried
(SpeedVac, Thermo Electron Corporation, MA) and testosterone and the hydroxy-products of
testosterone were extracted from the dry powder using methanol as solvent. Quantification of
the concentrations of testosterone and the products of metabolism of testosterone was done
by HPLC analysis on an Agilent 1100 series (Agilent Technologies, Waldbronn, Germany)
which consisted of a 9725 Rheodyne injector (Rheodyne, Rohnert Park, CA) and a G1314A
variable wavelength UV detector at 240 nm (Agilent Technolgies). An aliquiot of the extracted
sample (5uL) from each incubation was injected on a Capcell Pak C18 column type UG120
size 2.0mmID X 150mm, 5um particle size; Shiseido Fine Chemicals, Tokyo, Japan and eluted
at a flow rate of 200ul/minute by the gradient with the mobile phase, which consisted of
solvent A (20 mM ammonium acetate in 10% methanol) and solvent B (90% methanol). The
solvent gradient (solvent B) used for eluting testosterone and its metabolites was as follows: 0
min, 10%; 0-10min, 10%10-20min, 30%; 20-30min, 55%; 30-38 min, 55%; 38-45 min, 100%;
45-50 100%. The gradient was then returned to the initial condition (10% B) and held for 8
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minutes before analysis of the next sample. A standard curve of the ratio of AUC units of
testosterone (or products of its metabolism) to that of the internal standard, was plotted
against known concentrations of the standards. Standards were all purchased from Steraloids,
RI.
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Appendix 16
The Drug Development Process - a detailed overview
Synthesis
/
The following sequence of research activities begins the process that results in
development of new drugs:
1) Target Identification: Drugs usually act on either cellular or genetic chemicals in the body
-- known as targets -- which are believed to be associated with the disease. Scientists use a
variety of techniques to identify and isolate a target and learn more about its functions and
how these influence disease. Compounds are then identified, which have various interactions
with drug targets that are helpful in treatment of a specific disease
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2) Target prioritization/validation:
Each drug target is compared to others based on their
association with a specific disease and their ability to regulate biological and chemical
compounds in the body -- in order to select targets most likely to be useful in the development
of new treatments for disease. Tests are conducted to confirm that interactions with the drug
target are associated with a desired change in the behavior of diseased cells. This helps identify
compounds that have an effect on the target selected.
3) Lead identification: A lead compound or substance is one that is believed to have
potential to treat disease. Laboratory scientists can compare known substances with new
compounds to determine their likelihood of success. Leads are sometimes developed as
collections -- known as libraries -- of individual molecules that possess properties needed in a
new drug. Testing is then done on each of these molecules to confirm its effect on the drug
target.
4) Lead optimization:
Lead optimization compares the properties of various lead
compounds and provides information to help select the compound or compounds with the
greatest potential to be developed into safe and effective medicines. Often during this same
stage of development lead prioritization studies are conducted in living organisms (in tvio) and
in cells outside the organism (in vitro)to compare various lead compounds and understand how
they are metabolized and affect the body.
What is required before an investigational drug can be tested
in human volunteers?
5) In the preclinical stage of drug development an investigational drug must be tested
extensively in the laboratory to ensure that it will be safe to administer to humans. Testing at
this stage can take from 1 to 5 years and must provide information about the pharmaceutical
composition of the drug, its safety, how the drug will be formulated and manufactured, and
how it will be administered to the first human subjects. Results of all testing must be provided
-- to the Food and Drug Administration (FDA) in the U.S. and/or other appropriate
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regulatory agencies -- in order to obtain permission to begin clinical testing in humans.
Regulatory agencies review the specific tests and documentation that are required to proceed
to the next stage of development.
6) Setting up Chemical Manufacturing Controls (CMC): The results of preclinical testing
are used to determine methods to formulate the drug for its intended clinical use. For example,
a drug that is intended to act on the sinuses may be formulated as a time-release capsule or as a
nasal spray. Regulatory agencies require testing that documents the characteristics -- chemical
composition, purity, quality and potency -- of the drug's active ingredient and of the
formulated drug.
7) Pharmacology/Toxicology
studies: Pharmacological testing determines effects of the
candidate drug on the body using a series of screens that determine the effects of the new
chemical entity on the p450's. Toxicology studies are also conducted to ensure that any risks to
humans are identified.
How are investigational drugs tested in humans?
8) Testing of an investigational new drug begins with submission of information about the
drug and application for permission to begin administration to healthy volunteers or patients.
Investigational New Drug (IND)/Clinical Trial Exception (CTX)/Clinical Trial Authorization
(CTA) applications. INDs (in the U.S.), CTXs (in the U.K.) and CTAs (in Australia) are
examples of requests submitted to appropriate regulatory authorities for permission to conduct
investigational research. This research can include testing of a new dosage form or new use of
a drug already approved to be marketed. In addition to obtaining permission from appropriate
regulatory authorities, an institutional or independent review board (IRB) or ethical advisory
board must approve the protocol for testing as well as the informed consent documents that
volunteers sign prior to participating in a clinical study. An IRB is an independent committee
of physicians, community advocates and others that ensures that a clinical trial is ethical and
that the rights of study participants are protected. Clinical testing is usually described as
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consisting of Phase I, Phase II and Phase III clinical studies. In each successive phase
increasing numbers of patients are tested.
Phase I Clinical Studies: Phase I studies are designed to verify safety and tolerability
of the candidate drug in humans and typically take 6 to 9 months. These are the first studies
conducted in humans. A small number of subjects, usually from 20 to 100 healthy volunteers,
take the investigational drug for short periods of time. Testing includes observation and careful
documentation of how the drug acts in the body -- how it is absorbed, distributed, metabolized
and excreted.
Phase II Clinical Studies: .Phase II studies are designed to determine effectiveness
and further study the safety of the candidate drug in humans. Depending upon the type of
investigational drug and the condition it treats this phase of development generally takes from
6 months up to 3 years. Testing is conducted with up to several hundred patients suffering
from the condition the investigational drug is designed to treat. This testing determines safety
and effectiveness of the drug in treating the condition and establishes the minimum and
maximum effective dose. Most Phase II clinical trials are randomized -- randomly divided into
groups, one of which receives the investigational drug, one of which gets a placebo containing
no medication, and sometimes a third that receives a current standard treatment to which the
new investigational drug will be compared. In addition, most Phase II studies are doubleblinded -- meaning that neither patients nor researchers evaluating the compound know who is
receiving the investigational drug or placebo.
Phase III Clinical Studies:
Phase III studies provide expanded testing of
effectiveness and safety of an investigational drug, usually in randomized and blinded clinical
trials. Depending upon the type of drug candidate and the condition it treats this phase of
development usually requires one to four years of testing. In Phase III, safety and efficacy
testing is conducted with several hundred to thousands of volunteer patients suffering from
the condition the investigational drug treats.
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Authorization Application (MAA) NDAs
9) New Drug Application (NDA)/Marketing
(in the U.S.) and MAAs (in the U.K.) are examples of applications to market a new drug. Such
applications document safety and efficacy of the investigational drug and contain all the
information collected during the drug development process. At the conclusion of successful
preclinical and clinical testing, this series of documents is submitted to the Food and Drug
Administration (FDA) in the U.S. or to the applicable regulatory authorities in other countries.
The application must present substantial evidence that the drug will have the effect it is
represented to have when people use it or under the conditions for which it is prescribed,
recommended or suggested in the labeling. Obtaining approval to market a new drug
frequently takes between 6 months and 2 years.
Does testing continue after a new drug is approved?
After the FDA (or other regulatory agency for drugs marketed outside the U.S.)
approves a new drug, pharmaceutical companies may conduct additional studies, including
Phase IIIb and Phase IV studies. Late-stage drug development studies of approved, marketed
drugs may continue from several months to several years.
Phase IIIb/IV Studies: Phase IIIb trials, which often begin before approval, may
supplement or complete earlier trials by providing additional safety data or they may test the
approved drug for additional conditions for which it may prove useful. Phase IV studies
expand testing of a proven drug to broader patient populations and compare the long-term
effectiveness and/or cost of the drug to other marketed drugs available to treat the same
condition.
Post-Marketing Studies: Post-marketing studies test a marketed drug in new age
groups or patient types. Some studies focus on previously unknown side effects or related risk
factors. As with all stages of drug development testing, the purpose is to ensure the safety and
effectiveness of marketed drugs.
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