Strategic and scientific
approaches to developing
bioassays to support biologics
Darren Kamikura, Ph.D.
Bioassay Development
Eli Lilly and Company
Indianapolis, IN 46221
Why do we care about Potency?
For starters…it’s the law!!!
PHS Act: 42USC262
(B) The Secretary shall approve a biologics license application -(i) on the
basis of a demonstration that -(I) the biological product that is the subject
of the application is safe, pure, and potent;
21CFR601.2
To obtain a biologics license …the manufacturer…shall submit
data…which demonstrate that the manufactured product meets
prescribed requirements of safety, purity, and potency…
Adapted from Susan Kirchner (FDA)-USP-Bioassay Workshop 2008
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What is potency?
Potency:
21CFR600.3(s) “The word potency is interpreted to mean
the specific ability or capacity of the product(…) to effect a
given result.”
• Potency is a measure of the ability of a drug to elicit its
function.
If that ability is to induce (or abrogate) a biological response,
then a potency assay should be a bioassay.
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What is a Bioassay?
• A bioassay is defined as an analytical procedure
measuring a biological activity of a test substance based
on a specific, functional, biological response of a test
system.
• (WHO/NIBSC, J. Immunol. Methods (1998), 216, 103-116. International
consensus, Dev. Biol. Standard. (1999) vol 97)
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Why do we need Bioassays?
• Biologics are complex and heterogeneous in
composition and can exist in a number of physical and
chemical conformations that can impact product safety
and efficacy (eg. glycosylation, deamidation,
conformational changes, polymer).
• By measuring the potency, we can infer the ‘structural
integrity’ of complex biologicals. Thus, bioassays are a
measure of ‘quality’ of the therapeutic.
• ICH Q6B (Specifications: Test Procedures and acceptance criteria for
Biotechnological/Biological Products)
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What a Bioassay can and cannot tell us
• A bioassay CAN
1. indicate product quality
2. be relevant to mechanism of action, and give insights
into the way a therapeutic functions at a molecular
level
3. be reproducible and suitable for use in a QC
environment
• A bioassay CANNOT directly predict clinical
efficacy/outcome (eg. Biodistribution, PK, non-specific
interactions, anti-drug antibodies, etc)
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Types of Bioassays
Quantitative
Physiological Relevance
•Cell based bioassays can be both quantitative and physiologically relevant
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Cell based assays
• Cell based bioassays represent a space between drug
composition and the clinic (ie. Part way between “chemistry”
and “biology”)
Potency assay
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Where do we begin?
• Design of a relevant bioassay begins with understanding
how a drug affects the target biology
• eg. Growth factor/cytokine neutralization by NAb
• What are the critical biological pathways and can they
be exploited to develop a bioassay?
• Acquire the right reagents
• choice of cell type relevant to disease
• downstream read-out (reporter gene, proliferation, etc)
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Cells are Complex Systems
• Cells, being part way between “chemistry and the clinic”
are more complex than a defined set of chemicals but
less complex than an organism
• The cells are the foundation of a cell-based assay
 cells represent the most critical and difficult to
control reagent for bioassay performance (large
contributor of assay variability)
• Two critical, but difficult to answer questions:
1) Why can cells be difficult to control?
2) What defines “The happy/well behaving cell”?
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Cells are Complex Systems
• Human cell
• ~23,000 protein coding genes/haploid (~1.5% of
genome)
• Different proteins perform specific tasks and have
different locations, based on that task.
• There are many events that culminate in a given
response
Kamikura and Cooper, Traffic (2006) 7:324
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Cells are Complex Systems
• Decision making at a cellular level is stochastic
• Many genes contribute to the probability of a given
response in any particular cell (Ansel et al, PLOS Genet
(2008) 4:1)
Antibiotic Kill curve
EC50 = 50% dead
•What was different about the cells that survived?
•Not antibiotic resistant
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Cells are Complex Systems
• The ultimate biological response is also influenced by
the metabolic state and health of the cells
(-) HGF
(+) HGF
• How many events go into the decision of these cells to scatter in response to
HGF?
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Cell based Bioassays can be sensitive
• In an average person ~50 x 1012 cells (clinical trials)
• In a cell based assay
• ~25 x 103 cells/well (2 billion times less cells)
• EC50s can be in the range of pg/mL (10-10 - 10-12 g/mL)!
• for an antibody (150kDa), 1pg/mL is ~600
molecules/uL
• in 100uL, this is ~ 60000 molecules!
• just a few molecules are enough to perform the
function
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Cell based Bioassays
• High sensitivity allows us to detect how various species
present contribute to activity and product quality
• glycosylation
• deamidation
• clipping
• But… it also means that slight differences or modifications
in assay set-up can affect the performance of the assay.
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Generic Bioassay Protocol – Manipulations and
Considerations
1. Add Target cells to plate (Numbers?)
•
Rinse, trypsinize, resuspend, count, dilute, add to plate (6-7 steps)
2. Pre-incubate cells at 37°C (Time?)
•
Plate position in incubator/heating effects
3. Add Ligand/Antibody/other biologic dilutions (Range?)
•
Each dilution step is an independent manipulation (8-11 steps for each dilution series)
4. Incubate at 37°C (Time?)
5. Add detection reagents
6. Read in a luminometer
 How do we know which factors are critical?
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Considerations for Assay Optimization
Variables to consider include:
• temperature (growth, media for seeding & stimulation)
• time of cell growth prior to stimulation
• time of stimulation
• confluence of cells (seeding & stimulation)
• ligand incubation time
• ligand concentration
• Therapeutic dilution curve
• Brand of 96-well plates
• Plate sealers/evaporation controls
• Media composition (FBS, Defined Media, antibiotics, etc)
• eg. Cells may require FBS for survival, but FBS may interfere with
the assay outcome.
• Plate Layout
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DoE: Consult your local statistician
• Optimizing each variable independently and one at a
time is inefficient
• Effects of some of the variables may not be mutually
exclusive. Single factor optimization will not lead to truly
optimal conditions
• Make friends with your local statistician for success!
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Qualification/Validation of a Bioassay: A
critical role for statisticians
Post Development: Demonstration of method accuracy, repeatability,
intermediate precision, linearity, range and specificity (optional:
reproducibility)
Early phase qualification is less rigorous
• Meet release specifications of 50%-150% relative potency
• Demonstration of stability indication via forced/thermally degraded
reference standard
Late phase validation is much more rigorous
• Meet tighter release specifications (eg., 80%-120% relative potency)
• Use of multiple DS and DP lots
• Capable for assessing primary degradation pathway
• Demonstrate in-use sample stability
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Qualification design: 9 total plates
Plate #
Assay
#/Set/Day
Analyst
Experiment (UNK1, UNK2, UNK3)
UNK1
%RP
UNK2
%RP
UNK3
%RP
1
1
A
50%, 100%, 150%
52.3
101.5
160.5
2
2
B
50%, 100%, 150%
48.6
91.1
127.9
3
1
A
50%, 100%, 150%
55.6
101.6
174.7
4
2
B
70%, 100%, 130%
68.4
90.3
105.7
5
1
A
70%, 100%, 130%
82.0
106.6
142.5
6
2
B
100%, Specificity (alternative
molecule 1), control
87.4
ND
70.6
100%, Specificity (alternative
molecule 2), control
95.9
ND
76.1
100%, Interference (heat-killed),
Interference (Spike)
100.9
ND
94.4
100%, control
100.6
75.7
88.4
7
8
9
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3
3
3
A, B, or C
A, B, or C
A, B, or C
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Validation design: >100 plates
Example Drug Substance Experimental Design
Validation Study
Analyst
A
B
A
50%
75%
100%
125%
150%
L, A, I, R, P
3 plates1,2,5
3 plates1
3 plates1,2,3,4,5
3 plates1
3 plates1,2,5
A, I, R
3 plates2
3 plates2,3,4
3 plates2
A, I, R
3 plates2
3 plates2,3,4
3 plates2
B
A, I, R
3 plates2,3,4
A
A, I, R
3 plates2,3,4
B
A, I, R
3 plates2,3,4
Example Drug Product Experimental Design
Analyst
Validation Study
50%
75%
100%
125%
150%
3 plates1
3 plates1,2,3,4,5
3 plates1
3 plates1,2,5
B
L, A, I, R, P
3 plates1,2,5
A
A, I, R
3 plates2
3 plates2,3,4
3 plates2
B
A, I, R
3 plates2
3 plates2,3,4
3 plates2
A
A, I, R
3 plates2,3,4
B
A, I, R
3 plates2,3,4
A
A, I, R
3 plates2,3,4
L = Linearity1, A = Accuracy2, I = Intermediate Precision3, R = Reproducibility4, P = Repeatability Precision5
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Conclusion
• Cell based assays represent a space between chemical
assays and the clinic
• Biological systems are extremely complex and sensitive
to small variations in environment
 During assay development, statistics are invaluable in
being able to identify interacting biological factors that can
impact a given readout
 Post-development, statisticians are critical for the
design and analysis of qualification/validations to ensure
that an assay is suitable for its intended use
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