Biopharmaceutical Committee Technology Working Committee … · 2019-05-06 · biopharmaceutical manufacturing process. •Propose an example and identify challenges from industry

Case Studies of Established Conditions in Manufacturing Process

Toshiyuki Suzawa, Ph.D.Kyowa Hakko Kirin Co., Ltd.

Biopharmaceutical CommitteeTechnology Working Committee

JPMA06, Dec., 2016

CMC Strategy Forum Japan 2016

1

Contents

• Objectives

• Evaluation Scheme and Terminology

• Anticipated Manufacturing Process and Assumptions for Case Study

• Procedure for Determination of ECs

– Cell Culture Process

– Purification Process

• Summary

2

Objectives

• Investigate “model” Approval Matters (Established Conditions (ECs)) and their determination scheme using a “model” biopharmaceutical manufacturing process.

• Propose an example and identify challenges from industry side (Biopharmaceutical Committee, JPMA) to leverage for establishing regulatory process associated with ECs.

3

Evaluation scheme

1. Anticipate a “model” manufacturing process and some detailed assumptions on manufacturing conditions of a “model” biopharmaceutical.

2. Determine procedure for prioritization of process parameters considering Critical Quality Attributes (CQA) of the “model” product.

3. Perform Risk Assessment and risk rating based on Process Characterization (PC) studies as well as historical manufacturing data (Those are also assumed).

4. Propose and determine ECs and their management level (classification) based on Risk Assessment.

4

Terminology: Current rule of classification in Marketing Approval Application Form M1.2

• Partial Change Application (PCA): prior approval for change

– Applied for changes in ingredients other than the active ingredient, their contents, dosage and administration, indication, manufacturing method, specification and test method, etc.

– The matters to be addressed in PCA shall be enclosed in << >> , and other matters than MCN described below shall be addressed in PCA.

• Minor Change Notification (MCN): within 30 days after implementation or shipping

– Applied if proposed change is minor (no effect to the quality, efficacy or safety of the product).

– The matters to be addressed in MCN shall be enclosed in 『』, and the MCN other than target values/set values shall be enclosed in “ “.

PCA MCN

1. Package form change / addition2. Site change / addition (DS, DP)3. Ingredients’ prescription change4. Manufacturing process change5. Testing method change

1. Site change / addition (test, storage, package)

2. Manufacturing process

Example for Biotech product

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Anticipated Manufacturing Process

Process and Materials Assumption

Cell line CHO/DG44

Product Monoclonal Antibody (IgG)

Medium for storing the drug substanceThe same buffer as the drug product formulation

Distinct culture scale(Volume of the bioreactor)

15,000 L

Medium Serum-free medium

Culture process yield Approx. 3 g/L

Purification process yield Approx. 50%

BioburdenEstablished in the specifications and test methods

Bacterial endotoxinsEstablished in the specifications and test methods

Ingredients Same ingredients as the drug product

6

Assumptions for Case Study (1)

• Production culture step was evaluated

– Cell proliferation and production of desired antibody

• Prerequisites of the production culture

– SUS bioreactor is used for cell culture

– Use Medium 3 and add the medium only once during incubation

– Add the specified medium for nutrient supplement (Glucose) on the specified day

• Premise of process development

– The manufacturing process development is performed with the approaches (QbD-based) given in ICH Q11

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Procedure for Prioritization of Process Parameter

1. Identification of Quality Attributes (QAs) in the production culture process

2. Linkage of the process parameters and the selected QAs

3. Identification of the process parameters having an impact on the selected QAs (based on the prior/existing knowledge and

experience) → Risk Assessment

4. Implementation of the PC studies for the selected process parameters

5. Identification of the process parameters having an impact on the selected QAs (based on the results of PC studies)

6. Determination of ECs based on the Risk Assessment (Risk Mitigation) considering control strategy

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Approach for Risk Assessment

• With reference to the cases in ICH Q11 and A-Mab study (The CMC Biotech Working Group, 2009), perform risk scoring in accordance with the following scoring method.

• Establish QAs and their weights in reference to A-Mab study.

– the processes for identification of the QAs and weighting are not described but assumed, since it varies depending on products.

• For the rating of each process parameter, the results of the PC studies, i.e., impact to the QAs, are taken into consideration.

• For the determination of ECs, not only actual impact to the QAs but also process control capability (including downstream process) and detectability of the changes have been considered.

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Scoring MethodRisk Scoring Category

Weight / Impact Score

Ranking criteria

Quality Attributes

10Established or expected direct relationship to product quality or safety (including mfg safety)

7Unsure. Impact to product quality or safety or key business drivers expected

5 Unlikely to impact product quality or safety

1 No product quality or safety impact expected

Process Performances

10Strong relationship known based on available data and experience

7 Strong relationship is expected

5Not-so-strong relationship expected or unknown

1 Known to not have a relationship

Cumulative score = sum of [(weight of QA) x (impact score of process parameter)]10

1. Identification of Quality Attributes (QAs) in the production

culture process

Quality Attributes Weight

High Molecular Weight species (HMW) 5

Charge Variant 7

Host Cell Protein (HCP) 7

Host Cell DNA (DNA) 10

Fragment 5

Galactosylated glycans 7

Afucosylated glycans 7

Sialic acid 5

High Mannose species 7

Non-glycosylated Heavy Chain HC (NGHC) 7

WCBExpansion Culture Production Culture Harvest

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2. Linkage of the process parameters and the selected QAs

Process Parameter

Quality Attributes

HMWCharge variant

HCP DNA Fragment Gal aFuc SAHi-

ManNGHC

Temperature ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

pH ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

Addition of medium(timing)

- - - - - ✔ ✔ - - -

Initial cell viability

- - - - - - - - - -

Initial cell culturevolume

- - - - - - - - - -

::

::

::

::

::

::

::

::

::

::

::

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3. Identification of the process parameters having an impact on

the selected QAs (based on the prior/existing knowledge and

experience) → Risk Assessment

Process Parameter

Quality AttributesPriority

HMWCharge variant

HCP DNA Fragment Gal aFuc SAHi-

ManNHGC

Temperature ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ High

pH ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ High


- - - - - ✔ ✔ - - - Medium


- - - - - - - - - - Low

Initial cell culturevolume

- - - - - - - - - - Low

::

::

::

::

::

::

::

::

::

::

::

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4. Implementation of the process characterization (PC)

studies for the selected process parameters

• The process parameters to be investigated will be determined by prioritization

• Experimental investigation on the impact of process parameters to QAs

Bioreactor

Pump

Feed medium

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5. Identification of process parameter impacting

on QAs

Process Parameter

Quality Attributes

ScoreHMW Charge variant

HCP DNA Fragment Gal aFuc SA Hi-Man

NGHC

5 7 7 10 5 7 7 5 7 7

Temperature 5 7 1 1 1 7 5 5 1 1 227

pH 7 7 1 1 1 10 7 5 1 5 292


1 1 1 1 1 5 5 1 1 1 193


1 1 1 1 1 1 1 1 1 1 67

Initial cell culture volume

1 1 1 1 1 1 1 1 1 1 67

: : : : : : : : : : :

• Scoring will be performed based on the PC study data as well as historical manufacturing data

• Critical process parameter (CPP) will be identified using a threshold score of 200 (High) and 150 (Middle)

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Example of Rating Outcome：Production Culture

Operation outline Process parameter Quality attributes Score

Weight 5 7 7 10 5 7 7 5 7 7

Parameter HMWCharge

VariantHCP DNA Fragment

Galactosy-

lated

glycans

Afucosy-

lated

glycans

Sialic acidHigh

Mannose

Non-

glycosylat

ed HC

Confirmation of cell viability of

expansion culture fluid

Initial cell viability 1 1 1 1 1 1 1 1 1 1 67

Addition of medium to culture

vessel

Initial cell culture volume 1 1 1 1 1 1 1 1 1 1 67

Seeding of expansion culture

fluid

Initial viable cell density 7 5 1 1 1 7 5 5 1 1 215

Setting of culture conditions Temperature 5 7 1 1 1 7 5 1 1 5 227

pH 7 7 1 1 1 10 7 5 1 5 292

Dissolved CO2 1 5 1 1 1 7 1 1 1 1 137

Dissolved oxygen 1 1 1 1 1 5 1 1 1 1 95

Osmolality 1 1 1 1 1 5 5 1 1 1 123

Agitation speed 5 1 1 1 5 1 1 1 1 1 107

Vessel internal pressure 1 1 1 1 1 1 1 1 1 1 67

Aeration rate 1 1 1 1 1 1 1 1 1 1 67

Addition of medium Medium, addition timing 1 1 1 1 1 7 5 1 5 5 193

Additive amount of medium 1 1 1 1 1 5 5 1 1 1 123

Feed flow rate 1 1 1 1 1 1 1 1 1 1 67

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Example of Rating Outcome: Production Culture (Continued)

Setting of culture conditions

after addition of medium

Temperature 5 7 1 1 1 7 5 1 1 5 227

pH 7 7 1 1 1 10 7 5 1 5 292

Dissolved CO2 1 5 1 1 1 7 1 1 1 1 137


Osmolality 1 1 1 1 1 5 5 1 1 1 123

Agitation speed 5 1 1 1 5 1 1 1 1 1 107


Aeration rate 1 1 1 1 1 1 1 1 1 1 67

Addition of supplemental

medium (glucose)

Supplemental medium, addition

timing

1 1 1 1 1 7 5 1 5 5 193

Concentrations of nutritive

ingredients

1 1 1 1 1 7 5 1 5 5 193

Culture duration (days) 5 1 5 5 1 5 5 7 5 5 297

Setting of culture conditions

after addition of supplemental

medium

Temperature 5 7 1 1 1 7 5 1 1 5 227

pH 7 7 1 1 1 10 7 5 1 5 292

Dissolved CO2 1 5 1 1 1 7 1 1 1 1 137


Osmolality 1 1 1 1 1 5 5 1 1 1 123

Agitation speed 5 1 1 1 5 1 1 1 1 1 107


Aeration rate 1 1 1 1 1 1 1 1 1 1 67

Operation outline Process parameter Quality attributes Score

Weight 5 7 7 10 5 7 7 5 7 7

Parameter HMWCharge

VariantHCP DNA Fragment

Galactosy-lated

glycans

Afucosy-lated

glycansSialic acid High Mannose

Non-

glycosylated

HC

In this presentation some typical parameters are selected for illustrative purpose. 17

EC

Decision Flowchart of ECsFocusing on Quality Attributes

Score Ranking of Process Parameter(Threshold: High, Middle or Low)

LowMiddleHigh

CPP

『MCN』<<PCA>>

Well-characterizedWell-controlledMonitored

GMP control under PQSRisk Assessment

Risk Assessment

Virus, microbes, etc.

CPP having HighlySevere impact if failed

Non-CPP(Non-KPP)

Not Considered

Quality, Safety & EfficacyRisk Assessment

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6. Determination of ECs based on the Risk Assessment

(Risk Mitigation) considering control strategy

Process Parameter

Score Risk AssessmentProposed EC

Severity (S) Occurrence (O) Detection (D)

Temperature 227 HighMiddle

(Well-controlled, but with narrow range)

Low(Monitored)

CPP → <<PCA>>

pH 292 High Low(Well-controlled)

Low(Monitored)

CPP → 『MCN』


193 Middle Low(Well-controlled)

Low(Monitored)

CPP → 『MCN』


67 Low - - Non-CPP

Initial cell culture volume

67 Low - - Non-CPP

: : : : : :

• Based on Risk Assessment, CPPs are classified into either <<PCA>> or 『MCN』.

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Determination of ECs

• Process parameters that have been judged, through the procedure mentioned above, to have an impact to the selected QAs are defined as CPPs, and shall be controlled ( included in the approval form).

• Temperature, pH and Addition of medium (timing)... have been identified as CPPs in this case study.

• These CPPs will be categorized in either <<PCA>> or 『MCN』 based on risk assessment.

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Additional Considerations for Determination of ECs

• The basic information of the process such as culture scale and the media information (critical raw material) will be included as ECs.

• Bioreactor size: 15000 L

• Media component and composition

Medium pH Ingredients

Medium 3 XXGlucose <<A g/L>>Fatty acid <<B g/L>>Synthetic IGF-1 <<C g/L>>

Glucose solution(supplemental medium)

- Glucose <<α g/L>>

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• The in-process control tests severely impacting on patient safety are included as ECs.

– Bioburden: < 〇〇 CFU/mL

– Adventitious virus: no evidence of viral contamination

– Mycoplasma: negative

• These outcomes are not detected by controlling process parameters only.

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Example of ECs in M1.2Culture Process: Production Culture

• The expansion culture fluid is used to inoculate a culture vessel (with a capacity of 15000 L) containing Medium 3 to a viable cell density of 『1 × 105 cells/mL』, and cultured at <<37℃>> and 『pH 』. After 『days』 of culture, the culture is fed with Medium 3 and further cultured at <<37℃>> and 『pH 』. After 『▲ days』 of culture, the culture is fed with glucose solution to a final concentration of 『 g/L』 and production culture is continued for a total of 『X days』.

– In-process control tests:• Bioburden: < CFU/mL• Adventitious virus: no evidence of viral contamination• Mycoplasma: negative

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Assumptions for Case Study (2)

• Anion Exchange Chromatography (AEX) Step was evaluated– Removal of impurities such as DNA, HCP and rProtein (leached ligand)

• Prerequisites of the AEX step– General antibody purification process with 3 chromatography steps

including Affinity capture step

– AEX step is operated by flow-through mode

• Premise of process development– The manufacturing process development is performed with the

approaches (QbD-based) given in ICH Q11.

• Same procedure with Production culture step for prioritization of process parameters

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1. Identification of Quality Attributes (QAs) in the Anion

Exchange Chromatography Step

Quality Attributes Weight

High Molecular Weight Species (HMW) 5

Charge Variant 7

Host Cell Protein (HCP) 7

DNA 10

Fragment 5

Leached rProtein (Protein A) 5

Endotoxin 10

Monomer 10

Virus Safety 10

AEXHarvest Affinity Capture Low pH Polish # NF UF/DF Filter Drug Substance

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5. Identification of process parameter impacting

on QAs

Process Parameter

Quality Attributes

ScoreFragment HMW Charge variant

HCP Leach rProtein

DNA Endotoxin Monomer Virus Safety

5 5 7 7 5 10 10 5 10

Column load capacity

1 1 1 5 5 10 10 1 10 332

Conductivity of Eq. buffer

1 1 1 5 5 10 7 1 10 302

pH of Eq. buffer 1 1 5 5 5 10 7 1 10 330

Linear flow rate 1 1 1 5 5 7 7 1 7 257

Volume of Eq. buffer

1 1 1 1 1 1 1 1 1 64

Conc .of Eq. buffer component

1 1 1 1 1 1 1 1 1 64

: : : : : : : : : :

• Critical process parameter (CPP) will be identifiedusing a threshold score of 300 (High) and 200 (Middle)

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Example of Rating Outcome:AEX Chromatography

Weight 5 5 7 7 5 10 10 5 10

ParameterFragme

nt

HMW

(%)

Charge

variantHCP

Leach

rProtein

A

DNAEndotox

in

Monom

er

Virus

Safety

Cumulat

ive score

Column Load capacity 1 1 1 5 5 10 10 1 10 332

Column size (bed height, diameter,

bed volume)1 1 1 5 5 7 5 1 7 237

Column reuse 1 1 1 5 5 7 5 1 7 237

Flow rate for column equilibration 1 1 1 5 5 7 5 1 1 177

Volume of buffer solution for

column equilibration (CV)1 1 1 1 1 1 1 1 1 64

Load volume 1 1 1 1 1 1 1 1 1 64

Solution and conditions for column

storage1 1 1 1 1 1 7 1 1 124

Buffer solution/eluate

Quantity needed for pre-

treatment/equilibration buffer, etc.1 1 1 1 1 1 1 1 1 64

Volume of equilibration buffer

(CV)1 1 1 1 1 1 1 1 1 64

Conductivity of equilibration

buffer 1 1 1 5 5 10 7 1 10 302

pH of equilibration buffer 1 1 5 5 5 10 7 1 10 330

Concentration of the components of

pre-treatment buffer1 1 1 1 1 1 1 1 1 64

Concentration of the components of

equilibration buffer 1 1 1 1 1 1 1 1 1 64

Chromatography Linear flow rate 1 1 1 5 5 7 7 1 7 257

Fractionation Range of peak collection 1 1 1 5 5 5 5 7 1 197

In this presentation some typical parameters are selected for illustrative purpose. 27

EC

Again, Decision Flowchart of ECsFocusing on Quality Attributes

Score Ranking of Process Parameter(Threshold: High or Middle)

LowMiddleHigh

CPP

『MCN』<<PCA>>


GMP control under PQSRisk Assessment

Risk Assessment



Non-CPP(Non-KPP)

Not Considered


28

6. Determination of ECs based on the Risk Assessment

(Risk Mitigation) considering control strategy

Process Parameter

ScoreRisk Assessment

Proposed ECSeverity(S) Occurrence (O) Detection (D)

Column load capacity

332 HighHigh

(Changeable)High

(Not confirmed)CPP → <<PCA>>

Conductivity of Eq. buffer

302 High Low(Well-controlled)

Low(Monitored)

CPP → 『MCN』

pH of Eq. buffer 330 High Low(Well-controlled)

Low(Monitored)

CPP → 『MCN』

Linear flow rate 257 Middle Low(Well-controlled)

Middle(Monitored but not

confirmed)CPP → 『MCN』

Volume of Eq. buffer

59 Low - - Non-CPP

Conc .of Eq. buffer component

59 Low - - Non-CPP

: : : : : :

• Based on Risk Assessment, CPPs are classified into <<PCA>> or 『MCN』

29

Determination of ECs

• Process parameters that have been judged, through the procedure mentioned above, to have an impact to the selected QAs are defined as CPPs, and shall be controlled (basically included in the approval form).

• Column load capacity, Conductivity , pH of Eq. buffer and Linear flow rate have been identified as CPPs.

• Column load capacity has been determined as EC, and it is categorized as <<PCA>> according to risk assessment.

• In addition to Linear flow rate, pH and Conductivity of Eq. buffer can be classified as 『MCN』, because they are well controlled and monitored, therefore, not likely to deviate.

30


• The basic information of the process such as column size and chromatography mode will be included as ECs

– Column size: X L

– Chromatography type: anion exchange (product name or equivalent)

– Chromatography mode: flow-through

31

Example of ECs in M1.2Purification Process: AEX Chromatography

• The entire volume of the treated eluate from the previous step is loaded onto “X L” (as bed volume) of anion exchange column (product name or equivalent) as at linear flow rate 『〇〇cm/h』. Conductivity andpH of the equilibration buffer is maintained to be 『〇〇mS/cm』 and 『pH 〇〇』. The fraction containing the target antibody is collected (flow-through fraction).

– In-process control tests:• Bioburden: < CFU/mL

• Endotoxin: < EU/mL

32

Additional Considerations for Determination of ECs in Purification Process

• Low pH viral inactivation is a robust step and considered to be well-controlled.

• However, once deviate the defined range, product quality may severely impact to patient safety (Severity).

• Viral inactivation can not be confirmed by monitoring process parameters only.

• Even though the step can be well-characterized and well-controlled, this kind of CPP should be defined as ECs independent of the result of Risk Assessment. (→See Decision Flowchart)

33

Summary

• Case study on determination of ECs for a typical manufacturing process of a model monoclonal antibody has been conducted.

• ECs have been determined by science and risk-based approaches, and their classification has also been proposed.

• Further discussions are still underway for this case study at JPMA, but it might be leveraged for establishing framework on determination of ECs.

34

Appendix

• Supporting Slides

35

EC

Decision Flowchart of ECsScore Ranking of Process Parameter

(Threshold: High, Middle or Low)

LowMiddleHigh

CPP

『MCN』<<PCA>>


GMP control under PQS

Non-CPP(KPP)

Titer, Yield, etc.

Risk AssessmentRisk Assessment



Non-CPP(Non-KPP)

Not Considered


36

Control Strategy Assumed for Production Culture Step

Production Culture Step

(15000L Bioreactor)- Bioburden: < CFU/mL

- Adventitious virus: no evidence

of viral contamination

- Mycoplasma: negative

viable cell density: 『1x105 cells/mL』Temp: <<37℃>>

pH: 『pH 』Addition of Medium 3:

at 『days』Temp: <<37℃>>

pH: 『pH 』Addition of glucose solution:

『 g/L』at 『▲ days』

Cell culture termination:『X days』

Input Output

Process Flow

(IPC)(CPP) (KPP/Non-KPP) (IPM)

37

Control Strategy Assumed for AEX Chromatography Step

AEX Chromatography

Product Name or equivalent

(“X L”) -Bioburden: < CFU/mL

-Endotoxin: < EU/mL

Load: Entire volumeLinear Flow Rate: 『 cm/h』Conductivity of Eq. Buffer:

『mS/cm』pH of Eq. Buffer: 『pH 』Fraction: Flow-through

Input Output

Process Flow

(IPC)(CPP) (KPP/Non-KPP) (IPM)

38

Additional considerations for determination of ECs in Manufacturing Process

• Output parameters are not necessarily monitored and should be adequately selected such that the process can be well-controlled, if process consistency has been confirmed through process validation activities (PPQ).

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Documents

Biopharmaceutical Committee Technology Working Committee … · 2019-05-06 · biopharmaceutical manufacturing process. •Propose an example and identify challenges from industry