© 2009 Genentech, Inc. Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for

© 2009 Genentech, Inc.

Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD Pilot Program Ron Taticek, Ph.D., Director, Pharma Technical RegulatoryGenentech, a Member of the Roche GroupSouth San Francisco, CA

CMC Forum, Bethesda, MD19 July 2010

Slide 2


PRESENTATION OVERVIEW• Introduction• Background on A-MAb and MAb1• Establishing a Design Space for a MAb

– Experimental Design– Progression of Experiments– Identifying Design Space– Classifying Process Parameters

• FDA Pilot Program & Lessons Learned on Design Space• Opportunities & Challenges• Acknowledgements

Slide 3


Introduction

Roche and Genentech’s QbD activities have both internal and external components:• Pharma-wide Steering Committee with multiple teams working on

implementing QbD for Biologics, Small Molecules and Drug Conjugates (Limited)

• Member of the CMC Bio Working Group that wrote the A-MAb Case Study

• Part of the FDA QbD Pilot Program with 2 applications: late stage MAb (MAb1) & eCP for DS Site Transfer

• Member of EFPIA Mockestuzumab Team writing mock S2 filing• Participating on ISPE PQLI• Interactions on QbD with ex-US Health Authorities (EMA, Health

Canada)• Attendance & Presentation at Key Conferences

Slide 4


Comparison of A-MAb and MAb1

A-MAb & MAb1Humanized IgG1

Characteristic A-MAb MAb1

IgG1

Humanized Ab

Expressed in CHO Cells

Effector Function part of MOA

Molecule engineered for TPP

Liquid formulation; IV Administered

Fed-Batch Production

Includes Platform Process Elements

Oncology Indication

Immunology Indication

Slide 5

© 2009 Genentech, Inc.5

A-MAb Risk Assessment Approach

Prior Knowledge

Process Understanding

Product Understanding

ProcessDevelopment

RiskAssessment

ProcessCharacterization

RiskAssessment

RiskAssessment

ProcessPerformanceVerification

RiskAssessment

Life CycleManagement

Final ControlStrategy

ProcessParameters

QualityAttributes

Design Space

Draft ControlStrategy

Process 2

Process 1 2

Multiple Assessments Throughout the A-Mab Development Lifecycle for Entire Process

Slide 6


MAb1: Risk Assessment Approach

Risk Ranking &Filtering

(CQA RRF) For CQA

Identification

QualityAttributes

Process Development

Platform

Knowledge

ProductUnderstanding

Scientific Literature

LifecycleManagement ofDesign Space

Robustness Assessmentof Product

Testing Strategy(Robustness

RRF)

Final CQAs& Ranges

(RegistrationDossier)

ControlStrategy

(Registration Dossier)

Overall Process Design Space & CPP Identification

(Registration Dossier)

ComparabilityDecision

Tree

Risk Ranking& Filtering for

CPP Identification(CPP RRF)

PotentialCQAs

(Ph I-III)

Risk Ranking &Filtering for

Product TestingStrategy

(ATS RRF)

Risk Ranking &Filtering for

PC Study Design(PC/PV RRF)

ProcessParameters

ProcessCharacterization

& Linkage Studies

Design of Process

CharacterizationStudies

= Risk Assessment

Slide 7


A-MAb and MAb1 Cell Culture Processes

Inoculum Train

CentrifugeHarvestProduction (N)

Seed Train

N-1N-2N-3

WCBAmpule Seed Culture Expansion

in disposable shake flasks and/or bags

Seed Culture Expansion in fixedstirred tank reactors

N-1 Seed Culture Bioreactor3,000L WV

Production Bioreactor15,000L WV

Harvest Centrifugation & Depth Filtration

Nutrient Feed

Seed Maintenance

ThawWorking Cell Bank

Clarified Bulk

Seed Maintenance

Glucose Feeds

STEP 1

STEP 2

STEP 3

STEP 4

A-MAb Process MAb1 Process

Slide 8


MAb1: Risk Ranking & Filtering Tool to Design Characterization Studies

Main Effect x Interaction Effect = Risk Score

Direct impact to output(CQA, non-CQA or process attribute)

Impact of potential interactions with other process parameters on output (CQA, non-CQA or process attribute)

Risk Score to Define

Experimental Strategy

• Impact on Process Attribute or non-CQA (1, 2 & 4) is weighted less than a CQA (1, 4 & 8)

• Impact is assessed based on likely Design Space ranges• Limited/No data result in default to major impact

Experimental Strategy (multivariate, univariate or none)

Slide 9


MAb1: Risk Ranking and Filtering (RRF)

RRF indirectly considers process parameter control capability

• First consider desired (targeted) acceptable ranges for parameters– Based on capabilities of your facilities and provides some flexibility

for site transfer– Allows for future control space changes to better control CQAs

• Relate desired range to expected control capability– No scoring of capabilities themselves however

• Main effect scores typically based on product-specific development data

• Interaction effect scores based on data when available but also on prior knowledge and literature data

Slide 10


Multivariate Acceptable Ranges (MARs)

• MARs apply to both CPPs and non-CPPs• MARs for CPPs = design space• Derived from multivariate studies or

univariate studies (with rationale supporting a lack of interaction between parameters)

MAb1: Definition of Parameter Ranges

Proven Acceptable Ranges (PARs)

• Derived from univariate studies (ICH Q8 Definition)

• PARs are not part of the Design Space, but are used to resolve manufacturing deviations

Parameter 2

Par

amet

er 1

PAR supported by univariate studies

MAR supported by multivariate studies

Slide 11


MAb1: Categorization of Parameters

Unit Operation

No. of Parameters Considered

Parameters in Multivariate Studies

Parameters in Univariate Studies

(Broader Ranges or Short Duration

Excursions)

Parameters Leveraging Platform

Knowledge

Inoculum Culture

(N‑1)

9 Culture temperaturepH

Seeding densityMedium concentration

Culture duration

Seed densityTemperature

Culture duration

Dissolved oxygenDissolved oxygen

excursions

Production Culture

(N)

16 Seed densitypH

TemperaturepH shift time

Temperature shift timeBatch feed level

Batch feed timingMedium concentration

Culture duration

pHTemperature

pH excursionsTemperature excursions

Galactose conc.Run durationSeed density

Dissolved oxygenDissolved oxygen

excursionsIn‑process media

hold times

Note: some parameters tested both in multivariate and univariate studies.

Slide 12


A-MAb: Example of Risk Assessment Tool for Process Characterization

AggredatesFucosylation

GalactosylationCEX AV

HCPDNA

N-1 Bioreactor

FeedGlucose Feed

Production Bioreactor

Harvest

Medium

Procedures

Temperature

pH

Seed

In Vitro Cell Age

Seed Density

Viability

Operations

Time of Feeding

Volume of Feed

Preparation

Concentration

pH

Age

DO

pH

Temperature

CO2

AgitationShear/Mixing

Gas Transfer

Airflow

Antifoam

Scale Effects

Amount Delivered

Number of Feeds

TimingPreparation [Glucose]

Osmolality

Concentration

ProceduresAge

Duration

Working Volume

[NaHCO3]

Pre-filtration hold time

Storage Temperature

[Antifoam]

Procedures

Age

Storage Temperature

Pre-filtration hold time

Filtration

Filtration

# of Impellers

Vessel Design

Baffles

Control Parameters

Operations

Impeller Design

Sparger Design

Nominal Volumne

Use a Fish-bone (Ishikawa) diagram to identify parameters and attributes that might affect product quality and process performance

Slide 13


A-MAb: Mid-Develoment Risk Assessment Approach

Quality Attributes Process Attributes Risk Mitigation

Process Parameter in Production Bioreactor

Aggr

egat

e

aFuc

osyla

tion

Gal

acto

syla

tion

Deam

idat

ion

HCP

DNA

Prod

uct Y

ield

Viab

ility

at

Harv

est

Turb

idity

at

harv

est

Inoculum Viable Cell Concentr DOE Inoculum Viability Linkage Studies Inoculum In Vitro Cell Age EOPC Study N-1 Bioreactor pH Linkage Studies N-1 Bioreactor Temperature Linkage Studies Osmolality DOE Antifoam Concentration Not Required Nutrient Concentration in medium DOE

Medium storage temperature Medium Hold Studies Medium hold time before filtration Medium Hold Studies

Medium Filtration Medium Hold Studies Medium Age Medium Hold Studies Timing of Feed addition Not Required Volume of Feed addition DOE Component Concentration in Feed DOE

Timing of glucose feed addition DOE-Indirect

Amount of Glucose fed DOE-Indirect Dissolved Oxygen DOE Dissolved Carbon Dioxide DOE Temperature DOE pH DOE Culture Duration (days) DOE Remnant Glucose Concentration DOE-Indirect

Potential impact to significantly affect a process attribute such as yield or viability

Potential impact to QA with effective control of parameter or less robust control

Rank parameters based on impact and control capability.

pH is red or critical at this stage due to link to glycosylation

Slide 14


MAb1: Characterization Study Approach

Where possible, initial strategy aimed to provide evidence of Design Space (DS) claims by direct testing.

Large factor fractional factorial at wide ranges

Response surface with potential CPPs at

refined ranges

Predicted worst -case(s ) to verify DS edges

1 2 3

Large factor fractional factorial at wide ranges


refined ranges


1

Large fractional factorial at wide ranges


refined ranges


11 22 33

Large FractionalFactorial Studies with wide ranges

Response Surface with Potential CPPs

at refined ranges

Predicted worst case(s) tested to verify Design

Space edges

Slide 15


MAb1: Progression of Multivariate Studies

Unit Operation Study 1 Study 2 Study 3

CQAs in Linkage Studies

Inoculum Culture

(N‑1)

Fractional Factoriala

(4 factors, 8 runs)

Resolution IV


(4 factors, 8 runs)

Resolution IV

Worst‑case acidic variants linked to both target and

worst‑case production

(2 runs)

Acidic variants (Cell Culture

process steps linked)

Production Culture

(N)

Fractional Factorial

(8 factors, 16 runs)

Resolution IV

Central Composite(3 factors, 14 runs)

Resolution V

Worst‑cases for CQAs

Worst‑cases for impurity linkage to

purification(9 runs)

Acidic variants, CHOP, LpA,

DNA, Aggregates

CHOP = Chinese hamster ovary protein; LpA = Leached protein A.aCases are assessed after target production cultures are carried out. Run numbers do not include controls or replicates. Run duration extended in all studies except production Study 2.

Slide 16


MAb1: Process Characterization

DOE study progression aligned with A-MAb example• Initial fractional factorial tested wide ranges• Follow up fractional factorial narrowed ranges of parameters• ANOVA (model fit) used to assess statistical significance and to

estimate parameter effects on CQAs as well as KPIs

Progression culminates with testing of predicted worst-case settings of parameters

• Most at-risk CQA used to determine worst-case condition• Fractional factorial designs may not include predicted worst-case

conditions• A-MAb example may have tested worst-case(s) already as a full

factorial design was described–Full factorials may be less economical if large numbers of factors

are ranked for DOE

Slide 17


MAb1: Seed and Inoculum Train Characterization

N-1 step studied in both multivariate and univariate experiments• Factors in multivariate study are eligible for inclusion in Design Space

–Multivariate (DOE) studies support potential Design Space claims–Univariate studies support wider ranges for single parameter

excursions (for manufacturing support)• Inoculated production (N) cultures with resulting N-1 test cultures

–Growth rate and index, product titer, and viability in N cultures (Key Performance Indicators (KPIs))

–Critical Quality Attributes (CQAs) only considered for CPP identification

Steps Prior to N-1: not considered for DS inclusion since negligible MAb is produced in those steps (no product quality impact)

• Univariate testing• Analyzed via growth rate in subsequent passage

Slide 18


MAb1: N-1 Inoculum Characterization Outcome

• Variation in N-1 parameters led to large variation in production culture (N) growth and titers (specific productivity less affected)

• Important to link N-1 DOE to performance in N culture (similar to A-MAb)

Figure 2. Production (N) Growth From Initial N-1 Characterization Study

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Run Time (d)

Pack

ed C

ell V

olum

e (PC

V %

)

N Control

N low seed/low pH/lowT/low mediumN low seed/low pH/highT/high mediumN low seed/high pH/lowT/high mediumN low seed/high pH/highT/low mediumN high seed/low pH/lowT/high mediumN high seed/low pH/highT/low mediumN high seed/high pH/lowT/low mediumN high seed/high pH/highT/high medium

Figure 4. Production (N) Growth From Second N-1 Characterization Study

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Run Time (d)

Pack

ed C

ell V

olum

e (P

CV%

)

N-1 Control

high seed low pH low temp highmedium

high seed low pH high temp lowmedium

high seed high pH low temp lowmedium

high seed high pH high temp highmedium

low seed low pH low temp lowmedium

low seed low pH high temp highmedium

low seed high pH low temp highmedium

low seed high pH high temp lowmedium

Slide 19


MAb1: N-1 Inoculum Characterization Result (Studies 1 and 2)

Figure 3. Improvement in Acidic Variant Levels in Production Cultures Derived from Multivariate N-1 Conditions

Study 1 Study 2

7

7.1

7.2

7.3

N-1

pH

36

34 32 30

0 0.1 0.2 0.3

N-1 Seed Density (PCV%)

7

7.1

7.2

7.3

N-1

pH

30

29

0 0.1 0.2 0.3


PARMAR PARMAR PARMARMAR

Figure 3. Improvement in Acidic Variant Levels in Production Cultures Derived from Multivariate N-1 Conditions

Study 1 Study 2

7

7.1

7.2

7.3

N-1

pH

36

34 32 30

0 0.1 0.2 0.3


7

7.1

7.2

7.3

N-1

pH

30

29

0 0.1 0.2 0.3


PARMAR PARMAR PARMARMAR

• Initial DOE (Study 1) led to undesirable levels of acidic variants• Follow up DOE (Study 2) led to greatly improved acidic variant response• All other CQAs were within acceptable levels after Study 2

Slide 20


MAb1: N-1 Inoculum Culture Worst-Case Study (3)

• N-1 conditions predicted to result in highest levels of acidic variants were tested

• Worst-case N, for acidic variants, tested in same study• Linkage of both worst-case N-1 and worst-case N also tested

• No cumulative effect in the linkage, N-1 DS conditions are not additive to worst-case N

• Worst-cases (N-1 and N) included extended run duration

Case Acidic Variants (%)

Worst Case N-1 28

Worst Case N 38

Worst Case N-1 and Worst Case N Linked 37

Slide 21


A-MAb Seed Expansion: Risk Assessment

• No product is accumulated during seed expansion steps. • Prior knowledge with platform process (X-Mab, Y-Mab and Z-Mab)

shows that process performance is consistent and robust • Prior knowledge also demonstrates that process is flexible: successful

use of multiple formats and scales (shake flasks, cell bags, spinners, bioreactors)

• Risk Assessments of seed steps up to N-2 stage shows no impact on product quality

Seed expansion process is not part of the Design Space and is

not included in the registered detail

Seed Culture StepsProduct

AccumulationRisk of Impact to Product Quality

Seed Expansion in Spinner or Shake Flasks

Negligible Very Low

Seed Expansion in Wave Bag Bioreactor

Negligible Very Low

Seed Expansion in Fixed Bioreactor

Negligible Very Low

Slide 22


A-MAb N-1 Impacts Process Performance but NOT Product

Process Parameters

P-Values N-1 Seed Bioreactor

Performance Parameters

Production Bioreactor

Performance

Production Bioreacotr Product Quality

Variables Peak VCC

% Viab Culture

Duration Titer aFucos. Galact. HCP Aggreg.

pH 0.03 0.24 0.04 0.001 0.27 0.53 0.63 0.64

Dissolved oxygen

0.31 0.25 0.19 0.35 0.77 0.73 0.31 0.49

Temperature 0.02 0.05 0.03 0.005 0.43 0.22 0.23 0.60

pH × Dissolved Oxygen

0.04 0.78 0.65 0.37 0.17 0.78 0.59 0.85

pH × Temperature 0.32 0.26 0.32 0.02 0.98 0.36 0.80 0.36 Dissolved Oxygen ×

Temperature 0.42 0.86 0.74 0.37 0.80 0.38 0.61 0.26

Seed expansion process is not part of the Design Space and is not included in the registered detail

Slide 23


MAb1: Progression of Multivariate Studies

Unit Operation Study 1 Study 2 Study 3

CQAs in Linkage Studies

Inoculum Culture

(N‑1)


(4 factors, 8 runs)

Resolution IV


(4 factors, 8 runs)

Resolution IV

Worst‑case acidic variants linked to both target and

worst‑case production

(2 runs)

Acidic variants (Cell Culture

process steps linked)

Production Culture

(N)

Fractional Factorial

(8 factors, 16 runs)

Resolution IV

Central Composite(3 factors, 14 runs)

Resolution V

Worst‑cases for CQAs

Worst‑cases for impurity linkage to

purification(9 runs)

Acidic variants, CHOP, LpA,

DNA, Aggregates

CHOP = Chinese hamster ovary protein; LpA = Leached protein A.aCases are assessed after target production cultures are carried out. Run numbers do not include controls or replicates. Run duration extended in all studies except production Study 2.

Slide 24


MAb1: Production Culture Study 1

• Very large variation in CQAs and KPIs in the initial wide-range fractional factorial (Study 1)• Study done in 3 blocks, thus varying cell ages of controls

+ 1 dayDuration

± 0.15 pH unitspH Offset

± 1 °CTemperature Offset

± 24 hrpH Shift Timing

- 12 hr; + 24 hrTemperature Shift Timing

± 0.12% PCVSeed Density

± 24 hrBatch Feed Timing

± 50%Batch Feed Level

± 15%Media Concentration

RangeParameter

+ 1 dayDuration

± 0.15 pH unitspH Offset

± 1 °CTemperature Offset

± 24 hrpH Shift Timing

- 12 hr; + 24 hrTemperature Shift Timing

± 0.12% PCVSeed Density

± 24 hrBatch Feed Timing

± 50%Batch Feed Level

± 15%Media Concentration

RangeParameter

20

30

40

50

60

70

80

90

100

110

120

130

Normalized Titer Distribution

Acidic Variant Distribution

25

30

35

40

45

USL

20

30

40

50

60

70

80

90

100

110

120

130

Normalized Titer Distribution

Acidic Variant Distribution

25

30

35

40

45

USL

Slide 25



• 3 parameters with the largest effects on the most affected CQA were determined from ANOVA (main effect model fit) of Study 1 data

• Constricting temperature shift timing allows for wider pH & temperature ranges for an improved acidic variant response (<36% was targeted)

• Run duration also has a significant effect on acidic variant response as well

Temp Shift Time = -1 0 +1

-1

-0.5

0

0.5

1

pH c

oded

26

28

30

32

34 36

-1 -0.5 0 0.5 1

Temp coded

-1

-0.5

0

0.5

1

pH c

oded

28

30

32

34

36

-1 -0.5 0 0.5 1

Temp coded

-1

-0.5

0

0.5

1

pH c

oded

30

32

34

36

38

-1 -0.5 0 0.5 1

Temp coded

Temp Shift Time = -1 0 +1

-1

-0.5

0

0.5

1

pH c

oded

26

28

30

32

34 36

-1 -0.5 0 0.5 1

Temp coded

-1

-0.5

0

0.5

1

pH c

oded

28

30

32

34

36

-1 -0.5 0 0.5 1

Temp coded

-1

-0.5

0

0.5

1

pH c

oded

30

32

34

36

38

-1 -0.5 0 0.5 1

Temp coded

Slide 26



• The three most significant factors from Study 1 were re-examined in Study 2 (a resolution V central composite design)• Run duration removed from the design and was re-introduced in the Worst-Case tests

6.8

6.9

7.0

7.1

7.2

7.3

7.4

7.5

Initi

al p

H

36.0 36.5 37.0 37.5 38.0

Initial Temp

Contour Plot for Y:105 IEC %Acidic, proA

Y:105 IEC %Acidic, proA

28

29

30

31

32

33

34

Proposed MAR

Proposed PAR6.8

6.9

7.0

7.1

7.2

7.3

7.4

7.5

Initi

al p

H

36.0 36.5 37.0 37.5 38.0

Initial Temp



28

29

30

31

32

33

34

6.8

6.9

7.0

7.1

7.2

7.3

7.4

7.5

Initi

al p

H

36.0 36.5 37.0 37.5 38.0

Initial Temp



28

29

30

31

32

33

34

6.8

6.9

7.0

7.1

7.2

7.3

7.4

7.5

Initi

al p

H

36.0 36.5 37.0 37.5 38.0

Initial Temp



28

29

30

31

32

33

34

Proposed MAR

Proposed PAR

Proposed MAR

Proposed PAR

% Acidic Variants

Design Space

Acidic variants were influenced by three parameters (pH, temp, run duration)

Slide 27



ParameterHigh Acidic

VariantsHigh

HMWSHigh G0 Low G0 High G0-F Low G0-F

High NGHC

High CHOP

High DNA High LpA

Temperature + + − + − + − + + +

pH + + − + − + + + + +

Temperature Shift Timing + + − + − + − + + −

pH Shift Timing + + − + − + + − + +

Seed Density + + − + − + − + + −

Media Concentration + + + − + − + − + −

Batch Feed Level − + + − + − + − − +

Batch Feed Timing + − − + − + − − + −

Met acceptable range for respective CQA?

No Yes Yes Yes Yes Yes Yes Yes Yes Yes

Met acceptable range for Acidic Variants?

No No No No No No No No No Yes

Model-Predicted Worst-Case Conditions

• Rigorous testing of Design Space claims for all CQAs led to multiple acidic variant level responses that were undesirable

• Worst-cases predicted from ANOVA models• Direction of parameter estimate used to set level of each parameter

Slide 28


MAb1: Design Space Uncertainty

• Narrowed CQA Acceptance Criteria used to establish a design space – Accounts for uncertainty due to scale-down model and design space model

uncertainties– A simple and practical approach to build process robustness and ensure

product quality– Results in a meaningful tightening of process parameter ranges

Design Space

Corresponds to CQA Acceptance Criteria

CQA Target Range

Builds robustness into processCQA Acceptance

Criteria

CQA Target Range

Slide 29


MAb1: Process Parameter Classification

• Two categories: Non-CPPs and CPPs• A CPP is a parameter which has both a statistically significant and a practical (non-trivial) impact on the CQA.• CPPs are related to Design Space-limiting CQAs• Restriction relationship for parameters associated with CQAs impacted by multiple steps & that fail worst-case

linkage studies

CQA

Unit Operation

G0‑F Acidic VariantsLeached Protein

ACHOP

ViralSafety

N‑1 Inoculum Culture

None Seed density culture duration

None None NA

N Production Culture

Seed densitymedia concentration batch feed level

pHculture duration temperature

None None NA

Note: Seed train, N-3 and N-2 inoculum cultures, centrifugation/depth filtration do not have CPPs.

Slide 30


MAb1: Adaptive Overall Design Space

Control Space management allows for maximum design spaces for both steps

Run duration

Bioreactor

Elution pH

CEX

• Implement an overall design space for MAb1 via linkage studies

• Implement a restriction relationship allows for the broadening or constraining of the rangess for an individual unit operation while constraining or broadening the rangess of other unit operations impacting the same CQA.

• Restriction relationship ensures that the combination of acceptable ranges of the unit operations impacting a given CQA are constrained so that all allowable combinations result in all of the CQAs existing comfortably within the CQA target ranges.

pH

Elu

tion

co

nd

(Control Space Management)

Slide 31


A-MAb: DOE Studies to Define Design Space

Example of DOE Results from Screening Study (N=20)

3

4

5

Tite

r (g

/L)

3.74

3131

±0.0

7605

2

4

6

8

aFuc

osyl

atio

n

6.43

9933

±0.2

2694

8

24

28

32

Gal

acto

syla

tion

(%)

29.2

8939

±0.6

7458

2

4e+5

6e+5

8e+5

1e+6

HC

P (

ppm

)

6955

38

±165

18.3

1500

2000

2500

DN

A (

ppm

)

1935

.343

±89.

5590

8

24

28

32

CE

X %

Aci

dic

Var

iant

s

27.6

6898

±0.4

8081

4

1.8

2.2

2.6

3.0

Agg

rega

tes

(%)

2.51

5119

±0.0

3524

34

34.5 35

35.5 36

35

Temperature

(C)

30 40 50 60 70

50

DO (%)

40 60 80 100

120

140

160

100

CO2 (%)

6.6

6.7

6.8

6.9 7

7.1

6.85

pH

.8 1

1.2

1.4

1.6

1.2

[Medium]

(X)

360

380

400

420

440

400

Osmo (mOsm)

9 10 11 12 13 14 15

12

Feed (X)

.7 .8 .9 11.

11.

21.

3

1

IVCC (e6

cells/mL)

15 16 17 18 19

17

Duration

(d)

-0.1 .1 .3 .5 .7 .9 1.1

0.21

Curvature

Prediction Profiler

Slide 32


A-Mab: Develop Multivariate Models to define Design Space

• Augment Screening Design with Central Composite Design to develop full response surface

• One model for each CQA: describes relationships with CPPs

• Intersection of all CQA models define the Design Space

• For the production bioreactor the limits of Design Space are defined by a subset of CQAs:• Galactosylation• aFucosylation

• All other CQAs did not exceed Quality Limits when process operated within Knowledge Space & Design Space

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)

Culture Duration (days)

Factor

34.999293

50

100

6.85

1.2

440

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)

CEX % Acidic Variants

Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.2

5.7

30.3

490873.2

1498.3

26.7

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

CO2

Osmolality< 2%

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

11

40

15000000

19000

40

3

Contour

4.8

8.3

33.4

513494.5

1471.7

28.2

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

>11%

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

11

40

15000000

19000

40

3

Contour

5.2

9.8

36.8

469303.1

1465.4

33.1

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

4.7

7.8

33.7

495754.0

1552.2

30.2

1.2

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

360mOsm 440mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

4.5

6.6

34.4

458789.8

1479.0

31.0

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

160 mmHg

40 mmHg

100 mmHg

400mOsm

>40% >40%

>40%

<20%

<20%

>11%

>11%

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

34.999293

50

100

6.85

1.2

440

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.2

5.7

30.3

490873.2

1498.3

26.7

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

CO2

Osmolality< 2%

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

11

40

15000000

19000

40

3

Contour

4.8

8.3

33.4

513494.5

1471.7

28.2

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

>11%

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

11

40

15000000

19000

40

3

Contour

5.2

9.8

36.8

469303.1

1465.4

33.1

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

4.7

7.8

33.7

495754.0

1552.2

30.2

1.2

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

360mOsm 440mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

4.5

6.6

34.4

458789.8

1479.0

31.0

1.3

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

160 mmHg

40 mmHg

100 mmHg

400mOsm

>40% >40%

>40%

<20%

<20%

>11%

>11%

Design Space for Culture Duration 15 Days

CO2

Osmolality360mOsm 440mOsm

160 mmHg

40 mmHg

100 mmHg

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.0

6.5

29.8

697946.1

2040.3

30.2

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.6

5.2

25.7

694855.9

1966.2

26.9

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylationGalactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

400mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.1

6.3

29.1

702394.3

1965.8

28.4

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.7

7.5

33.5

669715.6

1973.9

32.9

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.9

5.9

30.9

674274.3

1961.3

30.7

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

>40%

<20%

< 2%<20%


CO2


160 mmHg

40 mmHg

100 mmHg

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.0

6.5

29.8

697946.1

2040.3

30.2

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.6

5.2

25.7

694855.9

1966.2

26.9

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH


34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

400mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.1

6.3

29.1

702394.3

1965.8

28.4

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.7

7.5

33.5

669715.6

1973.9

32.9

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.9

5.9

30.9

674274.3

1961.3

30.7

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

>40%

<20%

< 2%<20%

CO2


160 mmHg

40 mmHg

100 mmHg

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.0

6.5

29.8

697946.1

2040.3

30.2

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.6

5.2

25.7

694855.9

1966.2

26.9

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH


34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

400mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.1

6.3

29.1

702394.3

1965.8

28.4

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

40

15000000

19000

40

3

Contour

5.7

7.5

33.5

669715.6

1973.9

32.9

1.5

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

17

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.9

5.9

30.9

674274.3

1961.3

30.7

1.6

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

>40%

<20%

< 2%<20%



CO2


160 mmHg

40 mmHg

100 mmHg

400mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

5.2

27.3

889758.9

2443.6

30.5

1.9

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour ProfilerHoriz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

6.1

5.3

30.2

870128.1

2482.5

32.8

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

4.3

24.8

891294.0

2459.8

28.6

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)


Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

440

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.9

4.7

21.1

898827.7

2434.0

27.0

1.9

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

5.1

25.9

900138.3

2528.5

30.1

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

<20%

<20%

< 2%

<20%


CO2


160 mmHg

40 mmHg

100 mmHg

400mOsm

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

440

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

5.2

27.3

889758.9

2443.6

30.5

1.9

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)


Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

40

6.85

1.2

360

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

6.1

5.3

30.2

870128.1

2482.5

32.8

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

360

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

4.3

24.8

891294.0

2459.8

28.6

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)


Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

100

6.85

1.2

440

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

4.9

4.7

21.1

898827.7

2434.0

27.0

1.9

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

Horiz Vert

Temperature (C)

DO (%)

CO2 (%)

pH

[Medium] (X)

Osmolality (mOsm)

Feed (X)

IVCC (e6 cells/mL)


Factor

35

50

70

6.85

1.2

400

12

1

19

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Aggregates (%)

Response

0

2

20

15000000

19000

40

3

Contour

5.4

5.1

25.9

900138.3

2528.5

30.1

1.8

Current Y

.

2

20

.

.

20

.

Lo Limit

.

11

40

15000000

.

40

3

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

34 34.5 35 35.5 36

Temperature (C)

Contour Profiler

<20%

<20%

< 2%

<20%

Slide 33


Horiz Vert

Temperature (C)

DO (%)

CO2 (mmHg)

pH

[Medium] (X)

Osmo (mOsm)

Feed (X)

IVCC (e6 cells/mL)

Duration (d)

Factor

35

50

40

6.85

1.2

360

12

1

15

Current X

Titer (g/L)

aFucosylation

Galactosylation (%)

HCP (ppm)

DNA (ppm)


Response

3

11

40

675000

2250

40

Contour

5.3408326

9.1879682

38.227972

466955.66

1382.1644

34.420095

Current Y

3

.

.

.

.

.

Lo Limit

.

11

40

.

.

.

Hi Limit

6.6

6.7

6.8

6.9

7

7.1

pH

aFucosylation

Galactosylation (%)

34 34.5 35 35.5 36

Temperature (C)

Contour ProfilerA-MAb: Design Space Based on Process Capability & Bayesian Reliability

33

Galact >40%

aFucos >11%

34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6

6.65

6.7

6.75

6.8

6.85

6.9

6.95

7

7.05

0.5

Example: Day 15, Osmo=360 mOsm and pCO2=40 mmHg

>99% confidence of satisfying all CQAs50% contour approximates

“white” region” in contour plot

pH pH

Temperature (C) Temperature (C)

Slide 34


ProcessParameter

RiskAssessment

Does variability inparameter significantly

impact CQAsRisk

AssessmentSeverity of Impact,

ability to controlwithin Design

Space

Critical ProcessParameter

(CPP)

Well Controlled CriticalProcess Parameter

(WC-CPP)

RiskAssessment

Does variability inparameter impact process

performance orconsistency?

Key ProcessParameter

(KPP)

General ProcessParameter

(GPP)

Yes No

Yes

NoHighRisk

LowRisk

RiskAssessment

Severity of Impact,ability to control within

acceptableranges

HighRisk

LowRisk

A-MAb: Classification of Process Parameters

Within Design Space

Regulatory-Sensitive Not in Design Space

Managed through QMS

Slide 35


A-MAb: Control Strategy for Production Culture

Slide 35

Step 2Seed Culture Expansion

in Fixed Stirred TankBioreactors

Step 3Production Culture

Step 4Centrifugation and Depth

Filtration

Working Cell Bank

Clarified Bulk

Step 1Seed Culture Expansion

in Disposable ShakeFlasks and/or bags

In-ProcessQuality Attributes

BioburdenMMVMycoplamaAdventitious Virus

Product YieldTurbidity

Viable Cell ConcentrationViability

Product YieldViability at HarvestTurbity at Harvest


Key ProcessAttributes


Quality-linkedProcess Parameters

(WC-CPPs)

TemperaturepH

Dissolved CO2Culture Duration

OsmolalityRemnant Glucose

TemperaturepH

Dissolved OxygenCulture Duration

Initial VCC/Split Ratio

Antifoam ConcentrationTime of Nutrient Feed

Volume of Nutrient FeedTime of Glucose Feed

Volume of Glucose FeedDissolved Oxygen

Flow RatePressure

TemperatureCulture Duration

Initial VCC/Split Ratio

Key ProcessParameters

(KPPs)

TemperatureTime

Controlled within theDesign Space to

ensure consistentproduct quality and

process performance

Controlled within acceptablelimits to ensure consistent

process performance

Assay results partof batch releasespecifications

Slide 36


Design Space: A-MAb versus MAb1

Characteristic A-MAb MAb1

Steps with Design Space Production Bioreactor N-1 Inoculum BioreactorProduction Bioreactor

Parameters Included in Design Space WC-CPPs & CPPs(only WC-CPPs)

CPPs

Design Space Parameters pH & temperatureCulture durationDissolved CO2

OsmolalityRemaining Glucose

pH & temperatureCulture durationSeed density

Medium concentration Batch feed volume

Design Space Limiting CQAs AfucosylationGalactosylation

AfucosylationAcidic Variants

Address Uncertainty Bayesian Statistics Reduce the acceptable range for limiting CQAs

Description of Design Space in License

Mathematical Models Ranges and “Restriction Relationships”

Scale Increased Scale included via Engineering DSp

Small scale model to Full Commercial Scale Covered

Representation in Batch Records Ranges Ranges

Demonstration of Validity at Full Scale

1-2 runs + Continued Process Verification

3-5 runs + TBD

Slide 37


Lessons Learned from QbD Pilot Program

• BLA will need to include justification of parameter scoring (i.e., platform knowledge, prior knowledge, literature data) for risk assessments supporting design of characterization experiments

• Scientific literature is used to inform the risk ranking, but needs to be assessed for applicability to the sponsor’s process and product. Sponsor’s data takes precedence over scientific literature

• Qualification of small scale models needs to be demonstrated & raw material variation being incorporated in the characterization studies

• It is not clear what parameters are included in a Design Space (DSp)

• It is not clear how to interpret the ICH definition of Critical Process Parameters (CPPs); i.e., is a CPP a parameter which has both a statistically significant and a practical (non-trivial) impact on the CQA?

Slide 38


• Design Space only applies to steps where a CQA(s) is impacted (e.g., protein expression, impurity removal)

• Important to link individual steps through the process to ensure CQAs are maintained

• Overall Design Space allows operational flexibility as one or more steps can be constrained to provide more flexibility for another step

• Steps with Design Space are part of license claims with parameter ranges or mathematical model included

• Steps without Design Space are not part of license claims (other than claiming that they are controlled) and their ranges are managed within the Quality System (HA notification)

Lessons Learned from QbD Pilot Program (cont’d)

Slide 39


Opportunities and Challenges

Design Space• How best to communicate design space in License and Quality System –

data summaries, graphical representations, mathematical equations, etc.• What is included in the Design Space? How to define a CPP?• Demonstrate true capability of unit operations – measurement uncertainty

of probes, equipment functionality, and linkage to small-scale models

Continuous Verification• Identify best practices and approaches – statistical modeling and

engineering• Assure that Quality Systems can manage knowledge and change

especially for non-critical parameters• How to evolve design space as data and knowledge increase during

commercial scale manufacturing?

39

Slide 40


Acknowledgements

Genentech/RocheGreg BlankMary CromwellReed HarrisBernd HilgerKathy HsiaBrian KelleyChristoph LuedinLynne KrummenSherry Martin-Moe Nathan McKnightPaul Motchnik Wassim Nashabeh Mary SliwkowskiVassia Tegoulia Nathalie Yanze

A-MAb Cell Culture GroupIlse Blumentals, GSKGuillermo Miroquesada, MedImmuneKripa Ram, MedImmuneRon Taticek, GenentechVictor Vinci, Eli Lilly

Genentech/Roche MAb1 Brian HorvathMike Laird

Documents

© 2009 Genentech, Inc. Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for