UCL ‘Decisional Tools’ for the design of
cost-effective and robust bioprocesses
Suzanne S. Farid PhD CEng FIChemE
Professor in Biochemical Engineering
University College London
s.farid@ucl.ac.uk
© UCL SF 2014
Workshop: Vaccine process development – better tools for better vaccines, London, UK, 5 Nov 2014
Acknowledgements
UCL Decisional Tools researchers (past & present) include:
 Adam Stonier
 Daria Popova
 Janice Lim
 Alma Antemie
 Anuradha Rajapakse  Sofia Simaria
 James Pollock
 Allen Joseph
 Mustafa Mustafa
 Wenhao Nie
 Michael Jenkins
 Edmund George
 Cyrus Siganporia
 Tania Chilima
 Kais Lakhdar
 Richard Allmendinger  Christos Stamatis
 Paige Ashouri
 Sally Hassan
 Yang Yang
 Inass Hassan
UCL Academic Collaborators include:
 Prof Nigel Titchener-Hooker, Dr Yuhong Zhou
 Dr Lazaros Papageorgiou
Industrial collaborators in Decisional Tools research include:
 Thomas Dazskowski (Bayer)
 Richard Turner, Ray Field (MedImmune)
 Martin Smith, Dave Pain, Ashley Westlake (Lonza Biologics)
 Sa Ho (Pfizer)
 Roger Scott, Stephen Flanagan (Eli Lilly)
 Morten Munk (CMC)
 Richard Francis (ex-BTG)
 Dave Smith, Kim Warren , Jon Rowley (Lonza Walkersville)
 Tim Allsopp (Neusentis)
 Thierry Bovy, Matthieu Egloff, Jose Castillo (ATMI / Pall)
(Reprinted with permission from Lonza Ltd)
Funding: EPSRC, BRIC BBSRC, TSB
© UCL SF 2014
2
Biotech Drug Development Cycle
Challenges & Opportunities for SUT
p(success)
66%
40%
72%
97%
=
17%
# candidates
5.5
3.6
1.4
1.0
=
1
cost/launch
=
$1.8bn
non-clinical cost per candidate
=
~$65M
FIH = First in human, FED = First efficacy dose, DP= Decision point
DiMasi
al. Clin Pharmaco Therap 2010;87:272-277. Reichert MAbs. 2009; 1(4):387-9. Paul et al. 2010. Nat Rev Drug Discov 9(3):203-214. 3
© UCL
SF et2014
Bioprocess Decisional Tools – Domain
Biotech Drug Development Cycle
Decisions
Uncertainties
Portfolio selection?
Process design?
Clinical (e.g. doses, transition probabilities)
Capacity Sourcing?
Build single / multi-product facility?
Technical (e.g. titres, equipment failure)
Commercial (e.g. sales forecasts)
Constraints
Time
Capacity
Budget
Regulatory
Skilled labour
Metrics
Speed
Ease of scale-up
Cost of goods
Fit to facility
Robustness
© UCL SF 2014
Farid, 2012, In Biopharmaceutical Production Technology, pp717-74
4
Scope of UCL Decisional Tools
Typical questions addressed:
Process synthesis & facility design
 Which manufacturing strategy is the most cost-effective?
 How do the rankings of manufacturing strategies change with scale?
Or from clinical to commercial production?
 Key economic drivers? Economies of scale?
 Probability of failing to meet cost/demand targets? Robustness?
Portfolio management & capacity planning
 Portfolio selection - Which candidate therapies to select?
 Capacity sourcing - In-house v CMO production?
 Impact of company size and phase transition probabilities on choices?
© UCL SF 2014
5
UCL Decisional Tools - Approaches
Base Case
Analysis
MultiVariate
Analysis
Sensitivity
Analysis
Process
Economics
MultiObjective
Optimis’n
Risk
Analysis
MultiCriteria
DecisionMaking
© UCL SF 2014
6
Benchmarking Costs - Capital Investment
Manufacturing facility
Capital
investment
(US $M)
Area
(sq ft)
250
310,000
53
80,000
30,000
Biogen – LSM, RTP, NC, USA (2001)
175
245,000
90,000
Boehringer Ingelheim expansion -Biberach, Germany (2003)
315
-
90,000
Lonza Biologics expansion -Portsmouth, NH, USA (2004)
207
270,000
60,000
Amgen – BioNext, West Greenwich, RI, USA (2005)
500
500,000
180,000
Genentech NIMO** - Oceanside, CA, USA (2005)
380
470,000
90,000
Imclone - Branchburg BB50, NJ, USA (2005)
260
250,000
99,000
Biogen Idec – Hillerød, Denmark (2007*)
350
366,000
90,000
Lonza Biologics- Tuas, Singapore (2009*)
250
-
80,000
Genentech expansion – Vacaville, CA, USA (2009*)
600
380,000
200,000
Genentech – Vacaville, CA, USA (2000)
Imclone -Branchburg BB36, NJ, USA (2001)
Total
Production
Bioreactor
Capacity (L)
96,000
* Expected completion date
** Originally built by Biogen Idec and sold to Genentech in 2005
Benchmark: $10’s of millions for 1,000 - 5,000L facilities
$100’s of millions for 90,000 - 200,000L facilities
© UCL SF 2014
Farid (2007) J Chrom B 848: 8-18
7
Benchmarking Costs - COG
Cost of Goods (COG)
 Emphasis on improving costs has triggered a drive to reduce commercial
manufacturing costs from $1000’s/g to $100’s/g or even $10’s/g.
 Challenges with deriving benchmark COG/g values:
– Data has little value unless the
• annual production rate and
• either titre or fermentation capacity are stated with the cost.
– Basis for cost calculations is not always specified.
 Hidden costs
– ancillary activities (e.g. media/buffer preparation, CIP, QCQA)
– process development costs (e.g. cleaning validation studies)
– batch failure, risk
– environmental monitoring (e.g. HVAC systems), change-over time
– learning curve effects
© UCL SF 2014
Farid (2009) In Process Scale Purification of Antibodies, U Gottschalk (Ed), Ch 12, pp239-261
8
Scope of UCL Decisional Tools
Manufacturing Economics Examples:
 Fed-batch versus perfusion processes for commercial mAbs
 Chromatography sequence and sizing optimisation for mAbs
 Single-use planar versus microcarriers for cell therapies
© UCL SF 2014
9
Fed-batch versus perfusion culture (New build)
 Fed-batch versus perfusion systems (Pollock et al, 2013a)



Scenario: New build for commercial mAb prodn
Impact of scale on cost
Impact of titre variability and failures rates on robustness
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
10
Fed-batch versus perfusion culture (New build)
Commercial products using perfusion cell culture technologies
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
11
Fed-batch versus perfusion culture (New build)
Scenario trade-offs: FB v SPIN v ATF
ATF Perfusion
Spin-filter Perfusion
LEVEL
CONTROL
LEVEL
CONTROL
QUICK CONNECT
FLUID
IN LET
FLUID
INLET
AD DIT ION
PUMP
ADDITION
PUMP
VALVE
FILTRATE PUMP
LIQUID
LEVEL
SPIN
FILTER
FIL TRAT E
0.2 MICRON HOLLOW FIBRE FILTER CASSETTE
HOUSING
L IQUID
LEVEL
CONT RO LLER
EXH AUST
PR OCESS VESSEL
DIAPH RAGM
AI R
IN LET
ON
OFF
ATF
PU MP
STAND
FILTER
PRO:
Investment
DSP consumable cost
Steady state cell densities
Failure rates
CON:
Equipment failure rate
USP consumable cost
Scale limitations
Validation burden
 Compare the cost-effectiveness and robustness of fed-batch and perfusion cell
culture strategies across a range of titres and production scales for new build
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
12
Fed-batch versus perfusion culture (New build)
Key assumptions
Suites
Cell
Culture
Suite
FB
SPIN
ATF
Seed #1
Seed #1
Seed #1
Reactor type
Seed #2
Seed #2
Seed #2
CC
CC
CC
Cent
DF
DF
Variable
FB
SPIN
ATF
SS/SUB
SS
SUB
Cell culture time (days)
12
60
60
Max VCD (106 cells/ml)
10
15
50
Max bioreactor vol. (L)
20,000
2000
1500
Max perf. rate (vv/day)
–
1
1.5
65%
68%
69%
22
5
5
2 – 10
20% FB
45% FB
170-850
2 x FB
6.5 x FB
Process yield
UF
Annual # batches
Product conc. (g/L)
DSP
Suite
Viral
Secure
Suite
ProA
ProA
ProA
VI
VI
VI
Pool
Pool
CEX
CEX
CEX
UFDF
UFDF
UFDF
VRF
VRF
VRF
AEX
AEX
AEX
UFDF
UFDF
UFDF
Productivity (mg/L/day)
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
13
Fed-batch versus perfusion culture (New build)
Results: Impact of scale on COG
= Indirect
= Material
= Labour
Comparison of the cost of goods per gram for an equivalent fed-batch titre of 5 g/L
Critical cell density difference for ATF to compete with FB - x3 fold.
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
14
Fed-batch versus perfusion culture (New build)
Uncertainties and failure rates
Process event
p(Failure)
Consequence
Fed-batch culture contamination
1%
Spin-filter culture contamination
6%
Spin-filter filter failure
4%
ATF culture contamination
6%
ATF filter failure
2%
In process filtration failure – general
5%
4 hour delay & 2% yield loss
20 %
4 hour delay & 2% yield loss
In process filtration failure– post viral inactivation
Batch loss
Batch loss & discard two
pooled perfusate volumes
Batch loss & no pooled
volumes are discarded
Batch loss & discard two
pooled perfusate volumes
Replace filter & discard next 24
hours of perfusate
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
15
Fed-batch versus perfusion culture (New build)
Results: Impact of variability on robustness
Annual throughput and COG distributions under uncertainty
500kg demand, 5g/L titre
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
16
Fed-batch versus perfusion culture (New build)
Results: Impact of variability on robustness
Annual throughput and COG distributions under uncertainty
500kg demand, 5g/L titre
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
17
Fed-batch versus perfusion culture (New build)
Results: Reconciling operational and economic benefits
Operational
benefits
dominate
1. FB
2. ATF
3. SPIN
1. FB = ATF
2. SPIN
1. ATF
2. FB
3. SPIN
Economic
benefits
dominate
─ fed-batch, -- spin-filter, ··· ATF
Pollock, Ho & Farid, 2013, Biotech Bioeng, 110(1): 206–219
18
Fed-batch versus perfusion culture (New build)
Summary
Process economics case study insights:
• Economic competitiveness of perfusion depends on
–
–
–
–
–
–
–
Cell density increase achievable
Media perfusion rates
Failure rate
Pooling strategy
Qualitative concerns e.g. operational complexity
Scale of production
Titre
• For a fair economic comparison it is important to
capture the impact on downstream processing
© UCL SF 2014
19
Scope of UCL Decisional Tools
Manufacturing Economics Examples:
 Fed-batch versus perfusion processes for commercial mAbs
 Chromatography sequence and sizing optimisation for mAbs
 Single-use planar versus microcarriers for cell therapies
© UCL SF 2014
20
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
21
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Scenario:
•
•
•
Large biotech company with projected portfolio of mAbs for late phase/launch
New build
Candidates have different doses, patient numbers, titres, impurity loads, etc
Questions:
 Determine optimal facility configuration in terms of:
 Fermentation scale and number of DSP trains
 Purification sequence
 Equipment sizing
So as to minimise COG and late product deliveries
whilst satisfying yield and purity targets
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
22
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Decision Levels
LEVELS
Facility
DECISION VARIABLES
USP:DSP trains
1:1 | 2:1 | 4:1 | 6:1
Product
Challenge:
Multiple levels of decisions (e.g.
sequence & sizing)
& trade-offs (e.g. cost v time)
& uncertainties (e.g. titre)
Purification sequence
SEQ1 | SEQ2 | SEQ3 | SEQ4
Unit operation
Equipment sizing strategy
(h,d,nCYC,nCOL)1,a | (h,d,nCYC,nCOL)1,b
Approach:
Genetic algorithms linked to
bioprocess cost of goods (COG)
models
h=bed height, d=diameter, nCYC=nr cycles, nCOL=nr columns
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
23
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Problem definition - Examples of possible configurations
Alternative I
Protein A
BH=20
DIAM=100
NRCYC=3
NRCOL=1
Alternative II
Protein A
BH=18
DIAM=70
NRCYC=2
NRCOL=2
Alternative III
CEX
BH=18
DIAM=100
NRCYC=4
NRCOL=1
© UCL SF 2014
CEX
BH=15
DIAM=60
NRCYC=5
NRCOL=2
CEX
BH=20
DIAM=80
NRCYC=3
NRCOL=2
Membrane
AEX
capsule size
AEX
BH=20
DIAM=50
NRCYC=2
NRCOL=1
AEX
BH=22
DIAM=60
NRCYC=1
NRCOL=1
Range of variation:
• Bed height: 15-25 cm
• Diameter: 50-200 cm
• Nr cycles: 1-10
• Nr columns: 1-4
HIC
BH=20
DIAM=80
NRCYC=4
NRCOL=1
24
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Results: Decision making: which sequence should be selected?
= Sequence with lowest COG/g

Impurity targets
• Host cell proteins (HCP)
Sequences
that meet HCP
target
Scenario: 6USP:1DSP (tight DSP window)
Product: mAb3
Titre: 5g/L. Bioreactor scale: 6 x 2,500L
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
>10% reduction
5-10% reduction
similar
5-10% increase
>10% increase
%change
in COG/g
rel. to
platform
25
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Results: Decision making: which sequence should be selected?
= Sequence with lowest COG/g
that meets HCP target

Impurity targets
• Host cell proteins (HCP)
Sequences
that meet HCP
target
Scenario: 6USP:1DSP (tight DSP window)
Product: mAb3
Titre: 5g/L. Bioreactor scale: 6 x 2,500L
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
>10% reduction
5-10% reduction
similar
5-10% increase
>10% increase

%change
in COG/g
rel. to
platform
26
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Results: Decision making: which sequence should be selected?
= Sequence with lowest COG/g
that meets HCP target
and aggregates target
Impurity targets
• Host cell proteins (HCP)

Sequences
that meet
aggregates
target


• Aggregates
Scenario: 6USP:1DSP (tight DSP window)
Product: mAb3
Titre: 5g/L. Bioreactor scale: 6 x 2,500L
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
>10% reduction
5-10% reduction
similar
5-10% increase
>10% increase
%change
in COG/g
rel. to
platform
27
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Decision making: equipment sizing strategies in a multi-product facility
EAs provide a set of alternative ‘optimal’ strategies with similar COG/g values
mAb 1
mAb 2
mAb 3
Bubble size
proportional
to diameter
Selection according to decision-maker’s preferences:
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
28
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Decision making: equipment sizing strategies in a multi-product facility
EAs provide a set of alternative ‘optimal’ strategies with similar COG/g values
mAb 1
mAb 2
mAb 3
Bubble size
proportional
to diameter
Selection according to decision-maker’s preferences:
• Maximum column diameter in facility = 1 m (red bubbles: strategies that do not meet)
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
29
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Decision making: equipment sizing strategies in a multi-product facility
EAs provide a set of alternative ‘optimal’ strategies with similar COG/g values
mAb 1
mAb 2
mAb 3
Bubble size
proportional
to diameter
Selection according to decision-maker’s preferences:
• Maximum column diameter in facility = 1 m (red bubbles: strategies that do not meet)
• Bed height between 18 and 22 cm (purple bubbles: strategies that do not meet)
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
30
Searching large decision spaces
Example: Chromatography sequence and sizing optimisation
Decision making: equipment sizing strategies in a multi-product facility
EAs provide a set of alternative ‘optimal’ strategies with similar COG/g values
mAb 1
mAb 2
mAb 3
Bubble size
proportional
to diameter
Selection according to decision-maker’s preferences:
• Maximum column diameter in facility = 1 m (red bubbles: strategies that do not meet)
• Bed height between 18 and 22 cm (purple bubbles: strategies that do not meet)
• Minimise nr of columns to purchase (grey bubbles: strategies that do not meet)
Yellow bubbles: strategies that meet all criteria
© UCL SF 2014
Simaria, Turner & Farid, 2012, Biochem Eng J, 69, 144-154
31
Scope of UCL Decisional Tools
Manufacturing Economics Examples:
 Fed-batch versus perfusion processes for commercial mAbs
 Chromatography sequence and sizing optimisation for mAbs
 Single-use planar versus microcarriers for cell therapies
© UCL SF 2014
32
Cell Therapy Bioprocess Economics
33
Cell Therapy v. mAbs: Manufacturing Differences
• Several CT failures attributed to manufacturing*:
-
High cost of goods (COG), process variability, loss
of clinical efficacy upon scale-up, inadequate
characterisation
How can cell therapies
achieve the manufacturing success of
biopharmaceuticals?
• ‘Allo’ CTs: Similar product-driven business models
• CT v. mAbs manufacturing & supply chain issues:
–
–
–
–
–
–
–
–
Image source: Lonza
Limited large-scale bioprocessing options
Adherent culture, cells from healthy donors
Serum-containing cell culture media
Single-use technologies essential
Poorly automated, labour-intensive, open
Fresh /cryo products
Costly cold-chain transportation
Point-of-use care
*Source: Brandenberger et al, Bioprocess Intnl, March 2011 Supplement 34
Cell Therapy - Cell Culture Challenges
mAbs
Cell therapies (MSCs)
Technologies used in
clinical / commercial
batches
Bioreactors
10-layer vessels
Dose per admin
100-2000 mg
100 K – 1 B cells
Annual demand
100-1000 kg
1 B – 100 T cells
Cell culture yield
1-5 g/L
25,000 cells / cm2
Scale required
@ max. demand
6 x 10,000 L SS
6 x 2,000 L SUB
100,000 (!)
x 10-layer vessels
But can only handle
50-100
x 10-layer vessels / batch
35
© UCL SF 2014
Case study: Allogeneic cell expansion decisions
Technology S-curve for cell therapy manufacture
TARGET: 10,000 BILLION CELLS PER LOT (eg lot size=10,000 doses, dose=109 cells)
.
Technology gaps:
• S-curve illustrates
performance limits of each
technology
• Microcarrier-SUBs require
x2 increase in performance
for high demand scenarios
(10,000 billion cells/lot)
© UCL SF 2014
Simaria et al., Biotech Bioeng (2014)
36
Case study: Allogeneic cell expansion decisions
Future performance targets for microcarrier applications
Billion cells/lot
Production target
Base case: 0.5M cells/ml
Performance targets
Production target can be achieved with
different combinations of cells/ml and #SUBs:
- Point A: 2.6M cells/ml with 3 SUBs per lot
- Point B: 1.3M cells/ml with 6 SUBs per lot
© UCL SF 2014
Simaria et al., Biotech Bioeng (2014)
37
Case study: Allogeneic cell expansion decisions
Future performance targets for microcarrier applications
Characteristics of point X:
Billion cells/lot
Cell harvest density
20,000 cells/cm2
Microcarrier surface area
8000 cm2/g
Microcarrier density
16 g/L
But currently:
360-5500 cm2/g
(literature)
Production target
Million cells/ml
Base case: 0.5M cells/ml
Performance targets
Production target can be achieved with
different combinations of cells/ml and #SUBs:
- Point A: 2.6M cells/ml with 3 SUBs per lot
- Point B: 1.3M cells/ml with 6 SUBs per lot
© UCL SF 2014
Windows of operation
The desired M cells/ml can be achieved with different
combinations of microcarrier density, surface area and
harvest density.
Simaria et al., Biotech Bioeng (2014)
38
Case study: Allogeneic process decisions
Cost of goods as %sales
• Typical biologics COG = 15% sales
• Assumption: cell therapies will have similar gross margins to biologics?
*Assumption: reimbursement value of $40K/dose @dose=109cells, 50 doses/lot, demand = 10,000 doses/y
© UCL SF 2014
Simaria et al., Biotech Bioeng (2014); Hassan et al. (submitted)
39
UCL Decisional Tools Summary
Cell therapy / biotech company
Candidate in early phase development with:
Early clinical data
- eg.product type, dose estimate, patient numbers
Early process data
- e.g. yields
UCL Decisional Tools researchers
UCL Decisional Tools outputs can be used to help with decision-making:
 Compare the cost-effectiveness of alternative manufacturing processes / supply chains
 Identify the most cost-effective and GMP-ready process for
 current scale of operation
 future scales for late phase / commercial manufacture
 Predict and manage the risk of process changes throughout development pathway
 Identify most promising technologies and targets to reach for future R&D investment
 Address capacity sourcing and portfolio management decisions
© UCL SF 2014
40
UCL Decisional Tools Summary
 Our research aims to provide systematic approaches to
make better decisions in a sector inherent with uncertainties
 We achieve this by creating integrated models that capture:






bioprocess economics
manufacturing logistics
stochastic behaviour and risk
multiple conflicting objectives
combinatorial decisions
multivariate statistical analysis
 We apply the tools to tackle industrially-relevant investment
decisions at both a manufacturing & drug development level
© UCL SF 2014
41
www.ucl.ac.uk/epsrccim
Academic Centre Team
Academic Network Partners
User Network
Selected UCL Publications
•
Process economics overviews
–
–
•
•
Farid, S. S. 2013. Cost-effectiveness and robustness evaluation for biopharmaceutical
manufacture. Bioprocess International 11(11):2-8.
Farid, S.S. 2012. Evaluating and visualising the cost-effectiveness and robustness of
biopharmaceutical manufacturing strategies. in Subramanian, G. (ed.) Biopharmaceutical
Production Technology. Wiley VCH, Ch 22, pp 717-742.
–
Farid,S.S. 2009. Process economic drivers in industrial monoclonal antibody manufacture. in
Gottschalk,U. (ed.) Process Scale Purification of Antibodies. New Jersey: John Wiley & Sons,
Ch 12, pp239-261.
–
Farid,S.S. 2009. Economic drivers and trade-offs in antibody purification processes. Biopharm
International Supplement, March: 37-42.
–
Farid, S.S. 2007. Process economics of industrial monoclonal antibody manufacture. J.
Chrom. B, 848, 8-18.
Disposables / Single-use
–
Farid, S.S., Washbrook, J., Titchener-Hooker, N.J. 2005. Combining Multiple Quantitative and
Qualitative Goals When Assessing Biomanufacturing Strategies under Uncertainty. Biotechnol.
Prog. 21(4):1183–1191.
–
Farid, S.S., Washbrook, J., Titchener-Hooker, N.J. 2005. Decision-Support Tool for Assessing
Bio-Manufacturing Strategies under Uncertainty: Stainless steel versus Disposable Equipment
for Clinical Trial Material Preparation. Biotechnol. Prog., 21(2): 486-497.
Cell therapy process economics
–
Simaria AS, Hassan S, Varadaraju H, Rowley J, Warren K, Vanek P, Farid SS. 2014.
Allogeneic cell therapy bioprocess economics and optimization: single-use cell expansion
technologies. Biotechnol. Bioeng., 111(1) 69-83.
43
Selected UCL Publications
•
•
Fed-batch v perfusion, Continuous chromatography
–
Pollock, J., Bolton, G., Coffman, J., Ho, S.V., Bracewell, D. G., Farid, S.S. 2013. Optimising the
design and operation of semi-continuous affinity chromatography for clinical and commercial
manufacture, Journal of Chromatography A, 1284, 17-27.
–
Pollock, J., Ho, S. V., Farid, S. S. 2013. Fed-batch and perfusion culture processes: economic,
environmental, and operational feasibility under uncertainty. Biotechnol Bioeng 110(1):206-219
–
Lim, A. C., Washbrook, J., Titchener-Hooker, N. J., Farid, S. S. 2006. A Computer-Aided
Approach to Compare the Production Economics of Fed-Batch versus Perfusion Culture.
Biotechnol. Bioeng., 93(4): 687-697.
–
Lim, A. C., Zhou, Y., Washbrook, J., Sinclair, A., Fish, B., Francis, R., Titchener-Hooker, N. J.,
Farid, S. S. 2005. Application of A Decision-Support Tool to Assess Pooling Strategies in
Perfusion Culture Processes under Uncertainty. Biotechnol. Prog. 21(4):1231-1242.
Chromatography optimisation
–
Allmendinger, R., Simaria, A.S, Turner, R. and Farid, S.S. 2014. Closed-Loop Optimization of
Chromatography Column Sizing Strategies in Biopharmaceutical Manufacture. J Chem Tech
Biotechnol 89 (10):1481–1490.
–
Simaria, AS,, Turner, R,, Farid, S. S. (2012). A multi-level meta-heuristic algorithm for the
optimisation of antibody purification processes. Biochem. Eng. J. 69:144-154
–
Stonier, A., Simaria, A. S., Smith, M., Farid, S. S. (2012). Decisional tool to assess current and
future process robustness in an antibody purification facility. Biotechnol Prog 28(4): 1019-1028.
44
Selected UCL Publications
•
•
Facility fit prediction
–
Yang, Y., Farid, S.S., Thornhill, N.F. (2014) Data mining for rapid prediction of facility fit and
debottlenecking of biomanufacturing facilities. J Biotechnol ,179:17-25.
–
Stonier, A., Pain, D., Westlake, A., Hutchinson, N., Thornhill, N. F., Farid, S. S. (2013). Integration
of stochastic simulation with multivariate analysis: Short term facility fit prediction. Biotechnol Prog
29: 368–377.
Portfolio management & capacity planning
–
–
George, E.D., Farid, S.S. 2008. Strategic Biopharmaceutical Portfolio Development: An Analysis
of Constraint-Induced Implications. Biotechnol. Prog., 24 (3): 698 -713. DOI 10.1021/bp070410s
George, E., Titchener-Hooker, N.J., Farid, S.S. 2007. A multi-criteria decision-making framework
for the selection of strategies for acquiring biopharmaceutical manufacturing capacity. Comput.
Chem. Eng., 31, 889-901.
–
Lakhdar, K., Savery, J., Papageorgiou, L.G., Farid, S.S. 2007. Multiobjective long term planning of
biopharmaceutical manufacturing facilities. Biotechnol. Prog., 23 (6): 1383 -1393.
–
Rajapakse, A., Titchener-Hooker, N. J., Farid, S.S. 2006. Integrated Approach to Improving the
Value Potential of Biopharmaceutical R&D Portfolios while Mitigating Risk. J. Chem. Tech.
Biotechnol. 81:1705–1714.
–
Rajapakse, A., Titchener-Hooker, N. J., Farid, S. S. 2005. Modelling of the biopharmaceutical drug
development pathway and portfolio management. Comput. Chem. Eng., 29 (6): 1355-1366.
45