Quality by Design Questions to Consider How can we maximize the benefits to the industry and other stakeholders? How can we ensure that this will speed

Quality by Design

Questions to Consider• How can we maximize the benefits to

the industry and other stakeholders?• How can we ensure that this will

speed up development and reduce the investment for process and product development?

• QbD may be implemented in parts or as part of a development philosophy. How can this be implemented during early development?

• What is the best way to ensure that smaller enterprises can benefit from the work going on with QbD and facilitate innovation?

Slide 1

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


Filtration

Filtration

# of Impellers

Vessel Design

Baffles

Control Parameters

Operations

Impeller Design

Sparger Design

Nominal Volumne

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio

Basal Strength (Dilution)

Feed Strength (Dilution)

Feed Neutralization

Osmo

Duration

Factor

35

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.644125

6.1354189

32.452376

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lved

CO

2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.951625

7.5034189

32.837639

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lve

d C

O2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

5.974125

9.5011447

30.980559

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

solv

ed C

O2

Productivity

aFucosylation

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

35

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

6.896625

6.1244052

30.595296

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lved

CO

2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

A-Mab: a Case Study in Bioprocess Development

CMC Biotech Working Group

Background and Goal• To create a publicly available case study that helps translate the

‘what’ of ICH guidelines into practical ‘how’ for a biological molecule with emphasis on Quality by Design

• Started in August 2008• 7 companies divided across the various sections into teams

– GlaxoSmithKline, Abbott, Lilly, Pfizer, Genentech, MedImmune, Amgen– John Berridge, Sam Venugopal, and Ken Seamon, co-facilitators

• Combination of regular telecon and in- person meetings • Relentless focus on science and risk-based approaches, not

traditional ways• Colleagues from regulatory authorities provided unique insights

to help stimulate our case study

Slide 3

Creating a Biotech Case Study:“A-Mab”

• Based on a monoclonal antibody drug substance and drug product– “A-Mab”– Humanized IgG1

– IV Administered Drug (liquid)– Expressed in Cho Cells– Treatment of NHL

• Publicly and freely available as a teaching tool for industry and agencies

Why Monoclonal Antibody? Represents a significant number

of products in development Good product and process

experience in development and manufacture

Slide 4

Outline and Intent of Case Study

Content• Structure• Introduction• Quality Attributes• Upstream• Downstream• Drug Product• Control Strategy• Regulatory

Intent• Contains pieces/ sections that

appear realistic and represent selected QbD principles

• Illustrates the benefits of a QbD development approach

• Information represents real data or appropriate fictitious data

• Not a mock CTD-Q• Not a Gold Standard

Slide 5

A-Mab is a Public Document• Publication and Sponsorship

– CASSS http://www.casss.org– ISPE http://www.ispe.org

• Maintain CMC Working Group interactions• Coordinate workshops• Develop training• Facilitate regulatory interactions

Slide 6

Background and Linkage to ICH


Quality Risk

Management

Q9

Product and Process

Understanding

Q8 (R1)Q9, Q10 Q11

Pharmaceutical Quality System

Q10

21st Century Quality Paradigm

Lower Risk OperationsInnovation and Continual Improvement

Optimized Change Management ProcessEnhanced Regulatory Approaches

The New Qs underwrite the Quality Paradigm

Slide 8

Historical PerspectiveHistorical Perspective• Companies have always used science and risk based processes to

develop new products and gain process understanding– But they often did not submit knowledge or information to regulators

• Focus on minimum controversy registration, launch and then compliance– Processes became fixed

Future Goal• Knowledge management and risk management processes more

extensively used, documented and submitted– Intention of clearer communication of product and process understanding

• Opportunities for flexibility and post-approval process optimisation– A challenge to do this well– Leads to opportunities

Slide 9

To illustrate options to achieve enhanced product and process understanding

Demonstrate Industry’s vision for QbD as applied to biotech product realisation

Overall Goals of the A-mAb Case Study

• Identification of CQAs– Examples of CQA risk ranking tools

• Use of prior knowledge and platform technologies• Risk based approaches• Use of DoEs and statistical approaches

– To identify CPPs and their linkage to CQAs• Approaches to define and describe Design Spaces• Upstream , Downstream and Drug Product • Rational approach to defining a Control Strategy that reflects product & process

understanding and risk • Risk-based, lifecycle approach to managing continual improvement

Slide 10

Our Focus is on the key differentiators of QbD (from ICH Q8R1)

• An enhanced, quality by design approach to product development would additionally include the following elements:

• A systematic evaluation, understanding and refining of the formulation and manufacturing process, including; – Identifying, through e.g., prior knowledge, experimentation, and risk

assessment, the material attributes and process parameters that can have an effect on product CQAs;

– Determining the functional relationships that link material attributes and process parameters to product CQAs;

• Using the enhanced product and process understanding in combination with quality risk management to establish an appropriate control strategy that includes proposals for a design space(s) and/or real-time release testing

Slide 11

Linking Product and Process Understanding

Product QualityAttributes

CriticalityAssessment

1.Quality attributes to beconsidered and/or controlled

by manufacturing process

2. Acceptable ranges forquality attributes to ensure

drug safety and efficacy

Attributes that do not need tobe considered or controlledby manufacturing process

Safety andEfficacy Data

Process Targetsfor QualityAttributes

ProcessDevelopment andCharacterization

Con

tinuo

us P

roce

ss V

erifi

catio

nProcedural Controls

Characterization &Comparability Testing

Process ParameterControls

Specifications

Input Material Controls

In-Process Testing

Process Monitoring

Con

trol

Str

ateg

y E

lem

ents

High CriticalityAttributes

Low CriticalityAttributes

Product Understanding Process Understanding

ClinicalStudies

AnimalStudies

In-VitroStudies

PriorKnowledge

DesignSpace

Process Controls

Testing

Slide 12

“Systematic Evaluation”1. Use of prior platform knowledge

and process risk assessments to identify CQAs and those steps that need additional experimentation.

2. Demonstration that laboratory scale models are representative of the full-scale operations.

3. DOE to determine CPPs & KPPs4. Linkage of process parameters to

product Quality Attributes to create a Design Spaces.

5. Final risk assessment and categorization of process parameters to develop control strategy.



HCPDNA

N-1 Bioreactor

FeedGlucose Feed


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


Gas Transfer

Airflow

Antifoam

Scale Effects

Amount Delivered

Number of Feeds


Osmolality

Concentration

ProceduresAge

Duration

Working Volume

[NaHCO3]


Storage Temperature

[Antifoam]

Procedures

Age

Storage Temperature


Filtration

Filtration

# of Impellers

Vessel Design

Baffles

Control Parameters

Operations

Impeller Design

Sparger Design

Nominal Volumne

Tox500L

PhI/PhII1,000L

Optimization DOE I - 2L

Optimization DOE II - 2L

PhIII5,000L

PlatformKnowledge

25

30

35

40

Gal

acto

syla

tion

32.0

2279

±0.9

3055

5

35

35.5 36

36.5 37

36

Temperature

30 40 50 60 70

50

DO

40 50 60 70 80 90 100

70

Dissolved

CO2

3.8 4

4.2

4.4

4.6

4.8

4.3

Split Ratio

90 95 100

105

110

100

Basal Strength

(Dilution)

400

420

440

460

480

440

Osmo

90 95 100

105

110

100

Feed Strength

(Dilution)

86 88 90 92 94

90

Feed

Neutralization

16.8

17.2

17.6 18

17.3778

Duration

Prediction Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

35

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.644125

6.1354189

32.452376

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lve

d C

O2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.951625

7.5034189

32.837639

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lve

d C

O2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

5.974125

9.5011447

30.980559

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

solv

ed C

O2

Productivity

aFucosylation

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

35

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

6.896625

6.1244052

30.595296

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lved

CO

2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Slide 13

“Prior knowledge”

• Extensive use of prior knowledge and platform technologies – Previous Mabs extensively leveraged to assist in risk

assessments• Seed Expansion from frozen WCB to N-1 Bioreactor not

critical and not dependent on process format

– Use engineering and process characterization to define design space for production bioreactor

• Demonstrate that Design Space is valid at multiple scales of operation

• Parametric control of selected critical quality attributes

Slide 14

Critical Quality Attributes (CQAs)

• One of the greatest challenges is identifying CQAs• In the case study, we focus on severity, not process capability

– Risk assessment is based on:• prior knowledge (encompasses laboratory to clinic)• nonclinical studies and biological characterization throughout clinical

development • clinical experience

– Key Decisions: • Assign a Criticality Level (continuum) instead of critical/non-critical• Criticality based on potential impact to safety and efficacy

– Key Issues that were discussed:• Is there a cutoff for critical?• What would make critical into non-critical?• Linkage of QA ranking to Control Strategy

Slide 15

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

Risk Assessment Approach used through A-MAb development lifecycle

Slide 16

CQA Risk Ranking & Filtering Approach

• Severity = risk that attribute impacts safety or efficacy• Assess relative safety and efficacy risks using two factors:

– Impact and Uncertainty

• Impact = impact on safety or efficacy, i.e. consequences– Determined by available knowledge for attribute in question– More severe impact = higher score

• Uncertainty = uncertainty that attribute has expected impact– Determined by relevance of knowledge for each attribute– High uncertainty = high score– Low uncertainty = low score

Severity = Impact x Uncertainty

Slide 17

Impact Definition & ScaleImpact

(Score)

Biological Activity or

Efficacya PK/PDa Immunogenicity Safety

Very High (20)

Very significant changeSignificant change

on PKATA detected and confers

limits on safetyIrreversible AEs

High(16)

Significant changeModerate change

with impact on PD

ATA detected and confers limits on efficacy

Reversible AEs

Moderate (12)

Moderate changeModerate change

with no impact on PD

ATA detected with in vivo effect that can be

managedManageable AEs

Low(4)

Acceptable changeAcceptable change

with no impact on PD

ATA detected with minimal in vivo effect

Minor, transient AEs

None(2)

No changeNo impact on PK or

PD

ATA not detected or ATA detected with no

relevant in vivo effectNo AEs

AE = adverse event; ATA = anti-therapeutic antibodyaQuantitative criteria should be established for biological activity/efficacy and PK/PD. Significance of the change is

assessed relative to assay variability.

Uncertainty Definition & Scale

Uncertainty (Score)

Description(Variants and Host Related Impurities)

Description(Process Raw

Material)a

7(Very High)

No information (new variant)No information (new

impurity)

5(High)

Published external literature for variant in related molecule.

---

3(Moderate)

Nonclinical or in vitro data with this molecule. Data (nonclinical, in vitro or clinical) from a similar class

of molecule.

Component used in previous processes

2(Low)

Variant has been present in material used in clinical trials.

---

1(Very Low)

Impact of specific variant established in Clinical Studies with this molecule.

GRAS or studied in clinical trials

GRAS = generally regarded as safe a Assesses the impact of a raw material as an impurity. Impact of the raw material on the product

during manufacturing is assessed during process development.

Attribute Criticality

Aggregation 48

Glycosylation 48

Deamidation 4

Oxidation 12

HCP 24

DNA 12

Protein A 12

C-terminal lysine variants (charge

variants)

4

High CriticalityImpacted by multiple steps in the process Exemplify linkage across multiple unit ops through Design Space and Control Strategy

High CriticalityPrimarily impacted by production BioRx ; no clearance or modification in DS or DPProvide example of Parametric Control

Low Criticality Impacted by multiple steps in the process Exemplify linkage to Control Strategy

Medium CriticalityImpacted by multiple steps in DS but not affected by DPExemplify linkage to Control Strategy

Only a Subset of Quality Attributes is Evaluated in the Case Study

Slide 20

A-Mab Case StudyUpstream Process Development


Seed Culture Expansionin 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

Leverage Prior Knowledge with platform process

Engineering and process characterization to define Design Space and Control

Strategy

Risk-based approach to demonstrate no impact to

product quality

Demonstrate that Design Space is applicable to

multiple scales of operation

Lifecycle validation approach that includes continued process verification

Upstream Process

Slide 22

A-Mab Batch History

Process Scale Batches DispositionClinical

Exposure

Process 1 500 L 2 Pre-clinical studies

Process 1 1,000 L 3Phase 1 & 2Product/process understanding.

Process 2 5,000 L 5Phase 3Confirm end-to-end process performance.

Process 2 15,000 L 2

Commercial launch supplies Confirm Design Space and Control Strategy at commercial scale

Slide 23

Upstream Process Steps 1 & 2: Seed expansionNon-Critical based on Risk Assessment

1. No product is accumulated during seed expansion steps. 2. Prior knowledge with platform process (X-Mab, Y-Mab, and Z-Mab) shows

that process performance is consistent and robust 3. Prior knowledge also demonstrates that process is flexible: successful use of

multiple formats and scales (shake flasks, cell bags, spinners, bioreactors)4. 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 Steps Product Accumulation Risk 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 24

N-1 Seed Impacts Process Performance but NOT Product Quality

P-Values

Process Parameters N-1 Seed Bioreactor Performance Parameters


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 25

Upstream Process: Production Bioreactor Approach to Define a Design Space

Leverage Prior Knowledge and A-Mab Development Experience

Tox500L

Ph 1/Ph 21,000L

ProcessDevelopment (2L)

Ph 35,000L

PlatformKnowledge

Tox500L

Ph 1/Ph 21,000L


Ph 35,000L

PlatformKnowledge

Tox500L

Ph 1/Ph 21,000L


Ph 35,000L

PlatformKnowledge

Tox500L

Ph 1/Ph 21,000L


Ph 35,000L

PlatformKnowledge

Process 1 Process 1

Process 2

Data from other MAbs A-Mab Data

Slide 26

Example of Risk Assessment Approach to Process Characterization



HCPDNA

N-1 Bioreactor

FeedGlucose Feed


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


Gas Transfer

Airflow

Antifoam

Scale Effects

Amount Delivered

Number of Feeds


Osmolality

Concentration

ProceduresAge

Duration

Working Volume

[NaHCO3]


Storage Temperature

[Antifoam]

Procedures

Age

Storage Temperature


Filtration

Filtration

# of Impellers

Vessel Design

Baffles

Control Parameters

Operations

Impeller Design

Sparger Design

Nominal Volumne

Step 1. Use a Fish-bone (Ishikawa ) diagram to identify parameters and attributes that might affect product quality and process performance

Slide 27

Example of 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

Step 2: Rank parameters and attributes from Step 1 based on severity of impact and control capability. Identify interactions to include in DOE studies

Slide 28

DOE Studies to Define Design Space: Identify CPPs and Interactions

Example of DOE Results

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

29

Classification of Process Parameters based on Risk Assessment

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

Within Design SpaceRegulatory-Sensitive

Not in Design SpaceManaged through QMS

Slide 30

Control Strategy for Upstream Production

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 31

Define Engineering Design Space for Production Bioreactor

Analogous to the design space defined by scale-independent parameters, the engineering design space is a multidimensional combination of bioreactor design characteristics and engineering parameters that provide assurance that the production bioreactor performance will be robust and consistent and will meet product quality targets

Slide 32

Engineering Design SpaceRan

dal Al

le n

Design Space for scale-independent parameters was developed using qualified scale-down models

Engineering Design Space includes bioreactors of multiple scales and designs (2L -25K L)

Based on keeping microenvironment experienced by cells equivalent between scales

Characterization of bioreactor design, operation parameters, control capabilities, product quality and cell culture process performance provide basis for scientific understanding of the impact of scale/design

Includes bioreactor design considerations and scale-dependent process parameters

linked to fluid dynamics and mass transfer

2L Scale

Design Space applicability to multiple operation scales demonstrated using PCA/MVA models

500 L – 25,000 L

Slide 33

Lifecycle Approach to Validation

1. Multivariate model based on process characterization (e.g. DOE) - Model 1A comprehensive Design Space based on 2-L characterization studies as well as 500-

L, and 5000L experience for A-Mab. Includes scale-independent operational parameters: iVCC, temp, pH, pCO2 etc

2. Design Space for Scale-up, based on BioRx engineering parameters- Model 2Based on engineering characterization and DOE studies. Establish 2L as a reliable model system by: a) Establishing hydrodynamic similarity and ensuring appropriate

equipment design and operation; b) Establishing scalability through demonstration of overlapping performance of either scale in a MVA model that includes process inputs,

outputs and product quality – for previous aMAb product (Model 3)

3. Demonstration of scalability and Design Space for A-Mab by execution of 2 batches at the intended commercial scale (15K)

4. Use process monitoring during routine manufacturing for continuous verification that process is in state of control

Build MVA model for A-Mab; define acceptance criteria

1. Multivariate model based on process characterization (e.g. DOE) - Model 1A comprehensive Design Space based on 2-L characterization studies as well as 500-

L, and 5000L experience for A-Mab. Includes scale-independent operational parameters: iVCC, temp, pH, pCO2 etc

2. Design Space for Scale-up, based on BioRx engineering parameters- Model 2Based on engineering characterization and DOE studies. Establish 2L as a reliable model system by: a) Establishing hydrodynamic similarity and ensuring appropriate

equipment design and operation; b) Establishing scalability through demonstration of overlapping performance of either scale in a MVA model that includes process inputs,

outputs and product quality – for previous aMAb product (Model 3)

3. Demonstration of scalability and Design Space for A-Mab by execution of 2 batches at the intended commercial scale (15K)

4. Use process monitoring during routine manufacturing for continuous verification that process is in state of control

Build MVA model for A-Mab; define acceptance criteria Slide 34

Case StudyDownstream Process and Drug Product


Leverages Prior Knowledge with platform process to

define Design Space

Leverages prior knowledge and A-Mab results to justify a

modular approach to viral clearance

Downstream Process

Protein A Affinity Chromatography

Clarified Bulk

Final Filtration, Fill and Freeze

Formulation: Ultrafiltration and

Diafiltraion

Small Virus Retentive Filtration

Anion Exchange Chromatography

Cation Exchange Chromatography

Low pH Incubation

A-mAb

Step 5

Step 11

Step 8

Step 6

Step 7

Step 10

Step 9

Design Space based on worst case scenario for A-Mab stability and worst case for viral inactivation

Slide 37

Justification of two process changes post-launch : 1. Change resin for Protein

A 2. Change from resin to membrane format for AEX

Design Space based on multivariate model that links all three purifications steps (Protein A, AEX and CEX)

Multi-step Design Space for Chromatography Columns

• Design Space is defined based on model that links performance of the 3 purification steps– HCP clearance example

• Model based on results of individual DOE studies• No extrapolation of parameters outside ranges tested allowed

in design space• No interaction of parameters from different steps assumed.

– Assumption was experimentally verified.• 99.5% prediction interval added to mean predicted HCP levels

– To reflect high level of assurance specifications will be met if process operated in design space.

Slide 38

Acceptable range for each step depends on acceptable ranges for other two steps

Case 1: If full range allowed in Protein A and CEX, AEX is constrained

Protein A

3.23.33.43.53.63.73.83.9

10 20 30 40 50

Protein Load

Elu

tion

pH

CEX

3

4

5

6

7

10 15 20 25 30

Protein Load

Wa

sh C

on

du

ctiv

ity

AEX

1.6

2.6

3.6

4.6

5.6

7.2 7.4 7.6 7.8

Load pH

Equil

Wash C

onductiv

ity

Protein A

3.23.33.43.53.63.73.83.9

10 20 30 40 50

Protein Load

Elu

tion

pH

CEX

3

4

5

6

7

10 15 20 25 30

Protein Load

Wa

sh C

on

du

ctiv

ity

AEX

1.6

2.6

3.6

4.6

5.6

7.2 7.4 7.6 7.8

Load pH

Equil

Wash C

onductiv

ity

Protein A

3.23.33.43.53.63.73.83.9

10 20 30 40 50

Protein Load

Elu

tion

pH

CEX

3

4

5

6

7

10 15 20 25 30

Protein Load

Wa

sh C

on

du

ctiv

ity

AEX

1.6

2.6

3.6

4.6

5.6

7.2 7.4 7.6 7.8

Load pH

Equil

Wash C

onductiv

ity

Acceptable RangeFull range on axis is range explored in DOE

Case 2: Constraining Protein A and CEX ranges allows full ranges for AEX

Case 3: If full range allowed in Protein A and AEX, CEX is constrained

Slide 39

Step 1

Step 2

Step 3

Step 4

A-Mab Drug Substance

Drug substance preparation/handling

Compounding

Sterile filtration

Filling, stoppering and Capping

Packaged A-Mab Drug Product

Design spaces

· Multiple or single lots/container

· Frozen or unfrozen· Unclassified or class

100,000

· 50-1500 L· Stir time· Hold time· Tank configuration

· 50-1500 L· Hold time· Filter configuration

· Reservoir pressure· Pumping configuration· Capper spring pressure

Risk Assessment

Design Space

Control Strategy

Drug product process steps exemplifying QbD supported by optimized formulation design

40

A- Mab Case StudyControl Strategy


Control Strategy: Linking Product and Process Understanding

Product QualityAttributes

CriticalityAssessment

1.Quality attributes to beconsidered and/or controlled

by manufacturing process

2. Acceptable ranges forquality attributes to ensure

drug safety and efficacy

Attributes that do not need tobe considered or controlledby manufacturing process

Safety andEfficacy Data

Process Targetsfor QualityAttributes

ProcessDevelopment andCharacterization

Con

tinuo

us P

roce

ss V

erifi

catio

nProcedural Controls

Characterization &Comparability Testing

Process ParameterControls

Specifications

Input Material Controls

In-Process Testing

Process Monitoring

Con

trol

Str

ateg

y E

lem

ents

High CriticalityAttributes

Low CriticalityAttributes


ClinicalStudies

AnimalStudies

In-VitroStudies

PriorKnowledge

DesignSpace

Process Controls

Testing

Slide 42

Control Strategy is based on a final Risk Assessment for each CQA

Overall CQA RiskAssessment

RPN

CQA CriticalityAssessment

Severity

ProcessCapability

Occurrence

TestingStrategy

Detectability

= X X

RiskAssessment

RPN = SxOxD

Categorizationof ProcessParameters

Design Space

In-ProcessControls

Specifications

Risk Assessment

Severity of Impactx Certainty

Slide 43

Example of Control Strategy for selected CQAs

CQA Criticality Process Capability Testing Spec

LimitsOther Control

Elements

Aggregate High (48) High Risk DS and DP release Yes Parametric Control of

DS/DP steps

aFucosylation High (48) Low Risk DS Process Monitoring Yes Parametric Control of

Production BioRx

Host Cell Protein High (24) Very Low Risk Charact.

Comparability YesParametric Control of Prod BioRx, ProA, pH inact, CEX , AEX steps

DNA High (24) Very Low Risk Charact.Comparability Yes

Parametric Control of Prod Biox and AEX

Steps

Deamidated Isoforms Low (12) Low Risk Charact.

Comparability No Parametric Control of Production BioRx

From A-Mab Case Study www.casss.org

Drug Substance & Product Release Testing is Only one Element of Control Strategy

Attribute Test Acceptance Criteria Release Stability

Identity CEX Consistent with Ref Std and No New Peaks Yes No

Monomer HPSEC NLT 97% Yes Yes

Aggregates HPSEC NMT 3% Yes Yes

Endotoxin (LAL) USP <85> NMT 12.5 EU/mL Yes No

Reduced testing in comparison with traditional approaches

Example: Drug Substance Release Testing

Slide 45

A-Mab Case StudyRegulatory Considerations


Regulatory Aspects of the Case Study

• Objectives of the Regulatory section of the case study:– Describe information that is provided in the filing to convey process &

product understanding -vs- license commitments

– Describe how elements not covered by license commitments will be addressed in the Quality System

– Describe how development and monitoring of process knowledge throughout the product’s lifecycle will differ from traditional process validation activities and lead to continued improvement

– Propose a general risk-based approach for managing post-approval changes within and outside the design space and provide specific examples

Slide 47

Linking Product and Process Understanding to Regulatory Commitments & Process Lifecycle

The regulatory filing presents a summary of the risk assessment methodology and accumulated process & product knowledge

Regulatory commitments are the critical elements of the overall control strategy developed based on the outcomes of the overall risk assessments

The overall approach to risk-based process management becomes the basis for lifecycle and change management

BLA/MAA

Design space controls

In-process tests

Lot release tests

Stability commitments

Prior Knowledge

Process Understanding

Product Understanding

ProcessDevelopment

RiskAssessment

ProcessCharacterization

RiskAssessment

RiskAssessment

ProcessPerformanceVerification

RiskAssessment

Life CycleManagemen t

Final ControlStrategy

ProcessParameters

QualityAttributes

Design Space

Draft ControlStrategy

Slide 48

Justification of the Design Space• The overall knowledge that justifies the Design Space is based on

– Product and process specific knowledge– Historical and platform data

• Summary of the knowledge that justifies the outcomes of the risk assessment and the limits for design space will be presented in the Process Development History section– Conclusions will be supported by process characterization reports available upon

request or inspection

• The design space may be applied across many scales, or pieces of equipment (different bioreactors, columns of different widths), provided data sufficient justification is provided in the application

• The design space is not “validated” at manufacturing scale in the traditional sense

Slide 49

Lifecycle Approach to Process Validation

• Begins during development and continues post-launch

• Builds on knowledge from multiple scales

• Departure from the traditional 3-batch validation approach prior to submission– Process validation encompasses cumulative knowledge– Includes continued process verification

• To demonstrate validity of Design Space• To maintain validity of models

Slide 50

Lifecycle Management of Process Improvements & Changes

• Movements within the design space are managed without regulatory notification

• Changes outside the design space will involve a regulatory action– From notification to pre-approval depending on risk assessment

• Specific examples addressed in case study– Scale-up of production culture– Replace new chromatography resin with similar from same vendor– Replace new chromatography resin with new technology (membrane)– Manufacturing Site Changes for DS and DP

Slide 51

Assessing Change: Scope of Change is Initially Assessed at the Unit Operation Level

Degree to which outputs overlap denotes risk associated with change

Movement w/in approved DS

Changes outside approved DS

Outputs from previous step & other material inputs

Same Minor Change

Major change

Major change

Design Space Parameters

Same Same, Data not in original filing

New New

Step Outputs Same Same Same New

Output from previous step

DS ParametersUnchanged

DS Parameters

Changed

Output Output Output

MATERIAL INPUTS (Vendor, Scale,

Technology)

Unchanged Changed

Risk

Changes which represent more risk drive more extensive data collection

Slide 52

Quality by Design

Questions to Consider• How can we maximize the benefits to

the industry and other stakeholders?• How can we ensure that this will

speed up development and reduce the investment for process and product development?

• QbD may be implemented in parts or as part of a development philosophy. How can this be implemented during early development?

• What is the best way to ensure that smaller enterprises can benefit from the work going on with QbD and facilitate innovation?

Slide 53



HCPDNA

N-1 Bioreactor

FeedGlucose Feed


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


Gas Transfer

Airflow

Antifoam

Scale Effects

Amount Delivered

Number of Feeds


Osmolality

Concentration

ProceduresAge

Duration

Working Volume

[NaHCO3]


Storage Temperature

[Antifoam]

Procedures

Age

Storage Temperature


Filtration

Filtration

# of Impellers

Vessel Design

Baffles

Control Parameters

Operations

Impeller Design

Sparger Design

Nominal Volumne

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

35

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.644125

6.1354189

32.452376

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lved

CO

2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

3.7

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

8.951625

7.5034189

32.837639

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lve

d C

O2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

37

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

5.974125

9.5011447

30.980559

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

solv

ed C

O2

Productivity

aFucosylation

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

Horiz Vert

Temperature

DO

Dissolved CO2

Split Ratio



Feed Neutralization

Osmo

Duration

Factor

35

50

70

4.9

100

100

90

440

17.5

Current X

Productivity

aFucosylation

Galactosylation

Response

3

11

25

Contour

6.896625

6.1244052

30.595296

Current Y

3

3

25

Lo Limit

.

11

55

Hi Limit

40

60

80

100

Dis

so

lved

CO

2

Galactosylation

400 420 440 460 480

Osmo

Contour Profiler

What are Biosimilars?

• Biosimilars – Are biological products that claim to be similar to

an innovator biological product– The innovator’s product is off-patent and no

regulatory data protection remains– Are manufactured by a second manufacturer with

new cell line, new process and new analytical methods

– Require original data for approval

EMEA Approach for Biosimilar Medicines:Guideline on Similar Biological Medicinal Products

(CHMP/437/04)

• Overall Approach– Similar biological medicinal products are not generic medicinal

products– Comparability studies need to demonstrate the similar nature in

terms of quality, safety, and efficacy

• Biosimilars will be different from the reference– It is not expected that the quality attributes in the biosimilar and

reference product will be identical– The biosimilar product may exhibit a different safety profile (in

terms of nature, seriousness, or incidence of adverse reactions)

US Definition of Biosimilarity

• Biosimilarity– The biological product is highly similar to the

reference product not withstanding minor differences in clinically inactive components

– There are no clinically meaningful differences between the biological product and the reference product in terms of the safety, purity, and potency of the product.

Criteria for Biosimilar

EU• Similar nature to reference product

based on:– Quality– Safety– Efficacy

• Should be similar in molecular and biological terms

• Pharmaceutical form, strength, and route should be the same or if different additional data should be provided

• Class specific guidelines are referenced

US – BPCA• Highly similar to reference product

based on:– Analytical studies– Animal studies– Clinical study or studies

• Utilizes same mechanism of action• Conditions of use have been approved• Route of administration, dosage form,

and strength are the same• Not all data elements may be

necessary• Allows for a determination of

interchangeability

US Definition of Interchangeability

• The biological product may be substituted for the reference product without the intervention of the health care provider

• Determination of Interchangeability– Finding of biosimilarity and expectation to produce the

same clinical result in any patient– For a product that is administered more than once

• The risk in terms of safety or diminished efficacy of alternating or switching between use of the biological product and the reference product is not greater than using the reference product alone

Specification Limits Vs. Control LimitsDifferentiate Specification Limits from Control Limits

Based on clinical relevance to provide assurance of

safety and efficacy

Based on process capability to provide assurance of

process consistency

Regulatory CommitmentDesign Space enabled

Process Improvements enabled

Managed through QMSProcess Monitoring

Continued Process Verification


Design Space

Control SpaceS

pe

cif

ica

tio

n L

imit

s

Co

ntr

ol

Lim

its

CQA 1 CQA 2 CQA 3

Specifications are linked to clinical relevance not process capability

Changes in specifications during product lifecycle reflect improved understanding of relationship between product and clinical relevance

From Ilse Blumentals, GSK

Step 2: Consider Impact to Other Unit Operations and Requirements for Extended Characterization

Movement in approved DS

Change outside approved DS

Outputs from previous step & other material inputs

Same Minor Change Major change Major change Major change

Design Space Parameters Same Same, Data not in original filing

New New New

Step Outputs Same Same Minor Changes New New

Other Unit Operations Affected

Single Single Single Multiple Multiple

Meets IP & Lot Release Criteria

Yes Yes Yes Lot release met, some IPCs changed

Lot release met, some IPCs changed

Comparability required__________________Results Observed

no no Yes,__________ No changes

Yes__________ minor changes

Yes__________ new peaks

Supportive non-clin/clin data

no no no maybe Yes

No Reporting Notification Pre-approvalReporting Requirement

Reporting requirements are based on the reassessment of risk posed by the change including results of new design and testing if necessary

Slide 66

Documents

Quality by Design Questions to Consider How can we maximize the benefits to the industry and other stakeholders? How can we ensure that this will speed