CONTINUOUS IMPROVEMENT IN BIOLOGICS

MANUFACTURINGAn Opportunity for PAT

Charles L. CooneyDepartment of Chemical

EngineeringMIT

Cambridge, MAFebruary 20, 2007

Points for Discussion

• Drivers of Change in Pharmaceutical Manufacturing

• Managing Uncertainty and Risk• PAT and QbD• Process modeling• Miniaturization and process understanding• Genomic arrays and process design

WHERE IS MANUFACTURING GOING?

• Insight from the Clinic– Increasing molecular complexity– Larger pipelines, e.g. no. of molecules– Higher late stage rejection of candidate drugs– Personalized medicine defining smaller markets– Products with multiple API’s

• Insight from the Regulators– Increased focus on risk– Increased interest in understanding process science– Reduced tolerance to uncertainty– Focus on consistent quality– Increased regulatory burden from new products and

amended applications

WHERE IS MANUFACTURING GOING? (continued)

• Insight from the Market– Increasing number of dosage forms– Increasing diversity of package inserts (global

Markets)– Novel delivery formats

• Insight from Discovery– Increased potency and decreased dose– Use of molecular diagnostics in prescribing therapy– Large number of monoclonal antibody and vaccine

products

Process Design and Operation

We need to know where we want to go (critical metrics for quality), have the analytics to measure where we are (relative to our product specifications) and understand how the process affects the important properties of the product.

“There is uncertainty and thus risk”

UNCERTAINTY

• Uncertainty is the foundation of risk• Risk is the probability of an event and its

impact if it occurs?• We need to assess and reduce uncertainty

to an acceptable level to manage risk• Need to know where we want to go and

have a measure of where we are

WHAT ARE THE PROBLEMS?• We look at what we can measure and not

necessarily what is important• Our understanding of the underlying science is

often inadequate, creating uncertainty • While the biology of the drug is critical, we often

do not know how this relates to molecular or process complexity

• Formulation and storage add an additional layer of complexity to complex APIs

• There are organizational and intellectual barriers between therapeutic evaluation and process evaluation

DEFINING PATProcess Analytical Technology is:a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality.

http://www.fda.gov/cder/OPS/PAT.htm.

Need to know what to measure

Need the analytical tools

Do we know where we want to go?

Process Modeling and Simulation with Mapping of

Uncertainty

A PAT Tool

Process Flow for a MABInoculum Prep

P-1 / TFR-101

T-Flask (225 mL)

P-2 / RBR-101

Roller Bottle (2.2 L)

P-5 / SBR1

First Seed Bioreactor (1000 L)

P-6 / V-102

Media Prep

P-7 / DE-101

Sterile Filtration

P-11 / V-104

Media Prep

P-12 / DE-102

Sterile Filtration

P-10 / SBR2

Second Seed Bioreactor (5000 L)

P-20 / PBR1

Production Bioreactor (20000 L)

P-21 / V-106

Media Prep

P-22 / DE-103

Sterile Filtration

S-101

S-102

S-103

S-111

S-112 S-113

S-115

S-114

S-119

S-130S-121

S-122S-123

S-125

S-124

S-129

S-149

S-140

S-141 S-142

S-144

S-143

S-120

P-30 / V-101

Surge Tank

S-148

P-33 / V-103

Storage

P-34 / UF-101

Concentration

S-155

S-156

S-157

P-35 / V-105

Virus InactivationP-36 / DE-104

Polishing FIlter

P-37 / V-107

Storage

S-158

S-159

P-40 / C-101

Prot-A Chromatography

P-42 / V-108

Prot-A Pool Tank

P-38 / DF-102

Diafiltration

S-160

S-162

PrA-Equil

PrA-Wash

PrA-Eluat

PrA-Reg

S-181

S-164

S-163

S-161

S-165

S-169

P-41 / DE-105

Polishing FIlter

S-180

S-183

S-182

P-50 / C-102

IEX Chromatography

S-184

P-52 / V-109

IEX Pool Tank

IEX-Equil

IEX-Wash

IEX-WFI

IEX-Eluat

IEX-Strip

S-190

S-194

P-60 / C-103

HIC Chromatography

P-62 / DE-106

Dead-End Filtration

P-70 / V-110

HIC Pool Tank P-71 / DF-103

Diafiltration

P-72 / DE-107

Final Polishing Filtration

P-73 / DCS-101

Freeze in Plastic Bags

S-195

HIC-Equil

HIC-Wash

HIC-Eluat

HIC-Reg

S-200

S-205

S-204

S-210

S-211S-212

S-213

S-215

S-217

S-216

Final Product

IEX-Rinse

P-31 / DS-101

Centrifugation

P-32 / DE-108

Polishing Fitler

S-150

S-152

S-151

S-154

S-153

P-3 / BBS-101

Bag Bioreactor (20 L)

S-104

S-105

S-107

Bioreaction

Primary Recovery

Protein-A

IEX Chrom HIC Chrom

Final Filtration

P-4 / BBS-102

Bag Bioreactor (100 L)

S-106

S-108 S-110

S-109

S-214

P-39 / MX-101

Mixing

S-166

S-167

S-168

P-51 / MX-102

MixingP-61 / MX-103

Mixing

S-191

S-192

S-193

S-201

S-202

S-203

P-9 / AF-101

Air Filtration

P-8 / G-101

Gas Compression

S-116

S-117

S-118

P-13 / G-102

Gas Compression

P-14 / AF-102

Air Filtration

S-126

S-127

S-128

P-24 / AF-103

Air Filtration

P-23 / G-103

Gas Compression

S-145

S-146

S-147

Allocation of operating costs for monoclonal antibody production across different process sections

Operating Cost

0 2 4 6 8 10 12 14 16 18

Inoculum preparation

Bioreaction

Primary recovery

Protein A chrom

Ion exchange chrom.

Hydrophobic interaction chrom.

Final filtration

$ million/year

UPC for the MAb process model at different MAb concentrations

0

50

100

150

200

250

0 0.5 1 1.5 2 2.5 3 3.5Final Product Concentration (g/l)

UPC

($/g

)

What is the effect of “normal” variation in process performance on the desired outcome?

Allocation of Cost to UPC at Varying MAb Concentrations

0

25

50

75

100

125

150

0 0.5 1 1.5 2 2.5 3 3.5

Final Product Concentration (g/l)

($/g

)

Raw MaterialsFacilityLaborConsumables

Uncertainty Analysis

Process Model (SuperPro Designer)

Monte Carlo SimulationsScenarios Sensitivity

Analyses

Uncertainty Analysis

Process Data, Literature, Estimates

Economic Assessment

Monte Carlo Simulation

Return on Investment

Environmental Indices

Unit Production Cost

Market parameters e.g. product selling price

Technical parameterse.g. product

concentration

Supply chain parameters

e.g. media price

Uncertain variables: Objective functions:

P-1 / V-1Blending / Storage Me P-4 / ST-1

Heat Sterilizat

P-2 / V-1Blending / Storage Glu

P-3 / MX-1Mixing

P-5 / G-1Gas Compres

P-6 / AF-1Air Filtratio

P-7 / V-1Fermentati

P-8 / AF-1Air Filtratio

P-20 / RVF-Removal Biom

P-21 / HX-1Coolin

P-22 / MX-1Acidificatio

P-23 / CX-1Centrifugal Extrac

P-25 / V-1Re-ectraction + Crystall

P-26 / BCF-Basket Centrifuga

P-29 / MX-1Adding Fresh Butyl Ac

P-31 / FBDR-Fluid Bed Dry

P-32 / V-1Storage Penicillin Sodium

S-10

S-10

S-10

S-10

S-10

S-10

S-10

S-10

S-11 S-11

S-11

S-11

S-11

S-15 S-15

S-15

S-15

S-15

S-15

S-15

S-16

S-16

S-16

S-16

S-16

S-16S-16

S-17

S-17

S-17

S-17

S-17

S-17

S-10

S-11

S-11 S-11

P-27 / CSP-Component Split

S-16

S-17

S-15P-9 / V-1Storag

S-11S-11

P-24 / MX-1Neutralizati

S-15

S-15

S-16

P-28 / MX-1Neutralizati

S-16

S-17

S-17

Monte Carlo simulations

Probability Distribution: Input Variables

0.00000.00500.01000.01500.02000.0250

2.1 7.6 13.0 18.4 23.8

0.0000

0.0100

0.0200

0.0300

44.6 52.3 60.0 67.6 75.3

0.00000.00500.01000.01500.0200

1.5 1.9 2.3 2.7 3.1

Final product concentration:Normal distribution, Std.-Dev.: 10%

Agitator power:Normal distribution, Std.-Dev.: 20%, min: 1.5 kW/m3, max: 3.5 kW/m3

Price glucose:Beta distribution, α = 3.49; β = 1.2,Distribution type fits best actual data

Probability distribution of the UPC(10,000 trials)

0.00

0.01

0.02

0.03

0.04

0.05

1 21 41 61 81UPC ($/g MAb)

Prob

abili

ty

Probability distribution of the unit production costs

(10,000 trials, 100 groups in each graph, The peak of the MCS-

SCMP is at p = 0.4 )

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

50 100 150 200 250 300 350

UPC ($/g)

Prob

abili

ty

MCS-AP

MCS-TP

MCS-SCMP

MCS-FP

MCS-DSP

-75% -50% -25% 0% 25% 50% 75%

Final MAbConcentration

Product Yield HIC

Fermentation Time

Product Yield IXC

Product Yield Prot AChr.

Rplc. FrequencyProtein A

Technical Parameters

Contribution to the unit production cost variance

-75% -50% -25% 0% 25% 50% 75%

Media Powder Price

Unit Cost Protein AResin

Unit Cost IXC Resin

Unit Cost HIC Resin

SC parameters

-75% -50% -25% 0% 25% 50% 75%

Final MAb Concentration

Product Yield HIC

Fermentation Time

Product Yield Prot AChr.

Product Yield IXC

Rplc. Frequency ProteinA

All Parameters

P-11 / V-104

Media Prep

P-12 / DE-102

Sterile Filtration

P-10 / SBR2

Second Seed Bioreactor (5000 L)

P-20 / PBR1

Production Bioreactor (20000 L)

P-21 / V-106

Media Prep

P-22 / DE-103

Sterile Filtration

S-122S-123

S-125

S-124

S-129

S-149

S-140

S-141 S-142

S-144

S-143

Bioreaction

P-13 / G-102

Gas Compression

P-14 / AF-102

Air Filtration

S-126

S-127

S-128

P-24 / AF-103

Air Filtration

P-23 / G-103

Gas Compression

S-145

S-146

S-147

Measuring Where You AreSynthesisPost translational modificationProteolytic clipping

STRATEGIES FOR PROCESS DESIGN AND OPTIMIZATION

Drill Down in Understanding& Building Robustness

Drill Out for Optimization& Maximizing Performance

Microscale Analysis

Macroscale Analysis

22© 2005 BioProcessors • http://www.bioprocessors.com • info@bioprocessors.com • (781) 935-1400

Overcoming Limitations of Common Cell Culture Development Platforms

ThroughputExperimental capacity per

researcher

QualityAbility to predict manufacturing bioreactor performance

Benchtop bioreactor

Well plates

Shake flask

1’s

10’s

100’s

1000’s

Low High

High qualityHigh quantity

Add automationand software

Improve model data quality

Micro-Reactor Technology Enabling Large Scale Design of Experiments

Multiple Bioreactors

Working volume~ 300µl

Microfluidic channels for inoculation. feeds, pH adjustment & sampling

• Mammalian Cell Cultures with 5x107 Viable Cells/ml

• Simulates standard production modes: Batch, Fed Batch and Perfusion

• Enables full factorial DoE

Gas-permeable materials

Culture monitoring via optical interrogation

Source:BioProcessors, Inc. Woburn, MA USA

24© 2005 BioProcessors • http://www.bioprocessors.com • info@bioprocessors.com • (781) 935-1400

SimCell™ On-Line Measurements: Cell Density• Measured using Optical Density at 633 nm on Sensor

Station– Forward light scatter method

•OD calibration based on hand-counted cells (> 95% viable) and serial dilution of stock cell suspension (8x106

cells/ml)

•Each data point is average of 9 measurements (3 chambers x 3 OD readings/chamber)

•Error bars are +/- 1 std. dev. (+/- 16% variance)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0

1x106

2x106

3x106

4x106

5x106

6x106

7x106

8x106

9x106

1x107

biom

ass

OD633nm

OD calibration curve

MicroBioreactor Performance CHO-SEAP Batch Comparison

Cell Density Comparison

100,000

1,000,000

10,000,000

0 50 100 150

Hours

Log

cells

/ml

MBABRFL

Doubling Time Variability

0

5

10

15

20

MBA Flask Bioreactor

Vessel

Dou

blin

g Ti

me

(hou

rs)

N=22 N=10 N=3

• Growth in MBAs is similar to flasks and bench-top bioreactors.

• MBAs have low variability.

-125 -100 -75 -50 -25 0 25 50 75 100 125

123456789

101112131415161718192021222324252627282930

Spot

Num

ber

Average IntensityBR MBA

-125 -100 -75 -50 -25 0 25 50 75 100 125

123456789

101112131415161718192021222324252627282930

Spot

Num

ber

Average IntensityBR Flask

BioreactorMBA Flask

MBAs are more similar to bench-scale bioreactors

MBA vs Bioreactor

MicroBioreactor Performance CHO-SEAP : Gene Expression Profiles

Source:BioProcessors, Inc. Woburn, MA USAFLASK vs. Bioreactor

27© 2005 BioProcessors • http://www.bioprocessors.com • info@bioprocessors.com • (781) 935-1400

Observations• SimCell™ Microbioreactor Arrays are a highly representative

bioreactor scale-down model.– Flexible design and operation facilitates control of micro

bioreactor conditions and enables matching culture conditions in bioreactors.

– Automated online measurement of OD, pH, Oxygen and CO2

– Comparable results for:• Growth rate• Metabolism• Production

– Protein expression profiles show good similarities to bench-scale bioreactors

28© 2005 BioProcessors • http://www.bioprocessors.com • info@bioprocessors.com • (781) 935-1400

Automation Enables Very High Throughput

Incubation Modules

Loading Cell

Sampling Module

Optical Sensing Modu

Fluidic Module

Central Robot

Flexible, modular cluster design around central robot.

Layout used in semiconductor manufacture for >10 years

Platform can support 252 micro-bioreactor arrays in 6 incubators (1512 cultures).

Design of micro-bioreactor array and operation of system can be tailored to simulate common bioreactor modes

•Batch operation•Fed-batch operation•Perfusion

• Control of Shear Magnitude– Size of mobile secondary phase

– Chamber geometry

– Rotation rate

THE OXYGEN DILEMMA

•Required for efficient growth and recombinant protein expression

•Potential in vivo or in vitro protein oxidation e.g. Met, Cys

•Oxygen induced stress response

O2 Gradients in Large-Scale Fermentors

10,000 L

DO

10%

40%

100 mL

Homogeneous

10 L

Homogeneous

• How do O2 gradients affect cells ?

• How do cells respond?• Effects on recombinant

protein production?

100μl

Homogeneous

Heterogeneous

Model System: α1-Antitrypsin• Elastase inhibitor (44 kDa) 10 met and 1 unpaired cys• Activity lost with oxidation of active site MET358

Oxidation of met358 --> partial loss of neutrophil elastase activity & complete loss of porcine pancreatic elastase

• Use in protein replacement therapy• Cytoplasmic expression in E. coli BL21

methioninemethioninesulfoxide

H2N-C-HCOOH

CH2

CH2

CH3

S

H2N-C-HCOOH

CH2

CH2

CH3

S Ooxidation

M358

M351

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60

N2AIRO2

Nor

mal

ized

a1A

T

Time (min)

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60

N2AIRO2N

orm

aliz

ed a

1AT

Time (min)

Wild Type SG1146A (ClpP-)

Some background degradation (~18%) remains• Other proteases responsible (?)

O2-Enhanced Degradation is Eliminatedin ClpP- Strain

Do we have the analytical techniques to probe a cell’s

global response to its physical environment?

Can we locate where the problems are and then focus on solving the

right problem

DNA Microarray Experiments

• 3,812 Genes representing 89% of the E. coli genome

• Multi-Gene Groups– 167 protein complexes– 190 pathways– 333 transcription units

Selecting Differential Expression• List of Up-Regulated Genes Includes

– dnaJ– dnaK– ibpA– ibpB– htpG– topA

-10

-8

-6

-4

-2

0

2

4

6

-10 -8 -6 -4 -2 0 2 4 6Expression Values Pre-Induction

Expression Values60 min

FollowingInduction

GenesNotSelectedGenesSelected

DifferentialExpression

*Richmond et al. (1999) Nucleic Acids Res 27(19):3821. Lesley et al. (2002) Protein Eng 15(2):153. Rohlin et al. (2002) J Chin Inst Chem Eng 33(1):103.

• Heat-Shock (σ32) Genes Activated*

Differentially Expressed Genes

• Many Genes Affected by N2

• Focus on O2-Air Differences

050

100150200250300350400450500

0 30 60 90Post-Induction Time (min)

Numberof Genes

DifferentiallyExpressed

N2AIRO2

Hyperoxic Stress Responses

• Increasing N2 → Air → O2

• Sustained Response from SoxRS Regulon

• Increasing Air → O2

• Short-Term Response from OxyR Regulon

OxyR Regulon - ahpC /ahpF /dps /grxA /katG

-1.5-1

-0.50

0.51

1.52

0 30 60 90Post-Induction Time (min)

RelativeExpression(Average

Log Ratio)

O2AIR

SoxRS Regulon - fur /nfo /sodA /soxS

-5-4-3-2-101234

0 30 60 90Post-Induction Time (min)

RelativeExpression(Average

Log Ratio)

O2AIRN2

O2 Dependent Genes

• SoxRS Response– soxS, fur, sodA, nfo

• Iron Uptake– fur, sodA, fepB

• Fe-S Proteins– bioB, ilvD, leuB, mutY, fdx,

yfhI• Fe-S Cluster Assembly

– b2530 (iscS), b2531, hscA, fdx

Iron Supplementation• Improves Aerobic Growth of SOD Mutants in E.

coli* and Yeast‡

Effects of Iron – Alleviates α1AT

degradation

*Benov & Fridovich (1998) J Biol Chem 273:10313.‡De Frietas et al. (2000) J Biol Chem 275:11645.

Pulse-Chase Data - Wild-Type BL21 in O2

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60Time After Chase (min)

NormalizedAntitrypsin

Level

FeCl3FeCl2Water

MediumSupplement

Medium Supplements in O2(Water or 500 μM Autoclaved Iron)

500 μM Autoclaved FeCl2

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60Time After Chase (min)

NormalizedAntitrypsin

Level

N2O2AIR

– Eliminates oxygen dependence of α1AT degradation

Proteomics vs. Genomics• Transcriptional response provides insight into

what a cell wants to do• Protein synthesis tells us what the cell is doing• Protein isoforms may be important, e.g. narrow

glycosylation pattern for Enbrel.• DNA arrays are comprehensive but not precise• Broader dynamic range, increased resolution

and shorter duty cycle makes mass spec of complex protein mixtures feasible.

MANUFACTURING SCIENCEIn

tens

ity o

f Pr

oces

s U

nder

stan

ding

LEVEL 1

LEVEL 2

LEVEL 4

LEVEL 5

LEVEL 3

CORRELATIVE KNOWLEDGEWhat Is Correlated to What?

“CAUSAL" KNOWLEDGEWhat “Causes” What?

MECHANISTICKNOWLEDGE

How?

FirstPrinciples Why?

DESCRIPTIVE KNOWLEDGE: What?

IMPLEMENTATION OF PAT

• Develop and deploy the sensors to monitor process behavior and understand the process

• Identify critical control points that link process operation to product quality and if maintained assure product quality

• Replace the original quality assurance and control measures with those that link process understanding to product quality

This will require multiple sensors, new sampling techniques and strategies to interrogate the process.

Need to put in place an IT infrastructure that can handle the date and relationships.

This is an evolutionary process to accommodate new knowledge into new processes.

There needs to be a risk and learning focused organization in place.

STEPS INTO THE FUTURE• Understand what metrics (quality attributes) are

important• Build a roadmap for manufacturing science to focus on

what’s important • Expand high-throughput processes to high-insight

processes• Link advances in process & manufacturing science with

biomedical science – systems approach• Establish PAT and QbD early in process and product

development, aligning critical quality attributes along the value chain