Use of QbD Principles for the Development of an … · Use of QbD Principles for the Development of an Integrated Control Strategy. CMC Strategy Forum Europe. May 22-24, 2017. Girish

Use of QbD Principles for the Development of an Integrated Control Strategy

CMC Strategy Forum EuropeMay 22-24, 2017Girish J. Pendse Ph.D.

Background

• Integrated control strategy includes analytical, upstream, downstream and facility (e.g. micro) control strategies

• Control strategy was developed following ICHQ11 (i.e. Development Manufacture of Drug Substances) and incorporates aspects from several other ICH guidance documents

• Concepts and methodology presented were successfully implemented to support the approval of a multiple monoclonal antibody processes in the US, EU and ROW countries

6/26/2017 Company Confidential © 2012 Eli Lilly and Company 2

Control Strategy Components

1. Overview of how control strategy was developed

2. Process Description Example

3. List an example of Critical Quality Attributes (CQAs) for the process

4. Control Points Matrix

5. CQA acceptance criteria for key process intermediates

6. Methodology of parameter classification and PAR establishment

7. Summary of CPPs, critical limits and Proven Acceptable Ranges (PARs)

8. Summary of operational process parameters (OPPs) and corresponding operating ranges


Perform Risk Assessment of Potential Impact of Process Parameters on CQAs

Perform Empirical Studies of Selected Parameters Informed by CQAs

Determination of Process Parameter Criticality

Process Parameter Classification and Determination of Proven Acceptable Range

1. Conceptual Flow of Control Strategy Evolution


2. Process Description Example

Centrifugation & Depth Filtration

Protein A Capture

Low pH Viral Inactivation

TFF #1

Polishing Step

Viral Filtration

TFF #2

API Filtration and Dispense

Production Bioreactor

Vial Thaw & Flask Expansion

Scale-Up Bioreactor Expansion

Unit Op 1

Unit Op 2

Unit Op 3

Unit Op 4

Unit Op 5a

Unit Op 5b

Unit Op 6

Unit Op 7

Unit Op 8

Unit Op 9

Unit Op 10

Cell Culture (Upstream)

Purification (Downstream)


3. Description of CQAs for the Process

• A molecule specific assessment is needed to identify CQAs which present a potential risk to alter biological activity, efficacy and/or safety

• The more you understand about the molecule, the better the risk assessment

• Platform knowledge, prior knowledge obtained during early phase characterization and/or literature in the public domain should be leveraged for the risk assessment


3. Example of CQA’s for the Process

• Purity• Monomer, High Molecular Weight Species, Low Molecular Weight Species (SEC)• (ce) SDS-PAGE Reduced and Non-Reduced

• Potency• Potency Binding ELISA• Cell Based Potency

• Post Translational Modification (Product-Related Impurities)• Distribution of major glycans• α-Gal• Sialic Acid

• Process Related Impurities• DNA, HCP, rProA

• Microbial and Viral Safety• Bioburden, Endotoxin, Mycoplasma• Viral Safety• Genetic Stability


4. Control Points Matrix Example Residual HCP Plot

• Control Points Matrix is a summary of relationships identified between unit operations and their potential impact on CQA’s.

• Control Points Matrix is developed based on available small-scale and large-scale process intermediate data.


Residual HCP is cleared by Protein A and Polishing Step unit ops

Harvest ProA/Low pH TFF#1 Polishing Step VF Bulk

Seed Expansion

Prod Bioreactor

Primary Recovery

Pro A Capture

Low pH TFF#1Polishing

StepViral Filter TFF#2 Bulk Fill

Monomer SEC O ↓ ↓HMWS (Aggregate) SEC O ↑ ↑LMWS (Degradent) SEC OCharge Heterogeneity (% Acidic Variants)

IEC O

Charge Heterogeneity (% Neutral Variants)

IEC O

Charge Heterogeneity (% Basic Variants)

IEC O

Glycosylation N-Link LC OSialic Acid N-Link LC Oα-Gal N-Link LC O

Bioassay OBinding O

Non-Red OReduced O

DNA qPCR O ↓ ↓HCP ELISA O ↓ ↓rProtein A ELISA O ↓

BioburdenColony Count

O ↓ ↓

Endotoxin k QCL O ↓ ↓Mycoplasma Infectivity O

xMuLVInfectivity

qPCRO ↓ ↓ ↓ ↓

MMV Infectivity O ↓ ↓ ↓PRV Infectivity O ↓ ↓ ↓ ↓

Mic

robi

al a

nd V

iral

Safe

ty

Critical Quality Attribute

Analytical Method

Unit Operations Affecting CQAs

Mol

ecul

ar C

hara

cter

istic

s and

Pro

duct

Rel

ated

Im

purit

y

Potency

Purity by SDS-PAGE

Proc

ess

Rela

ted

Impu

rity


O = Point of Origin, ↑ - Observed Increase, ↓ = Observed Decrease

Product Quality Key Intermediates

4. Control Points MatrixExample Matrix Table

6. Methodology of Parameter Classification and Establishment of PARs

Conceptual Flow of Control Strategy Development

Early Stage Molecule Specific/ Platform Knowledge

Risk Assessment

Preliminary DoE Studies

Confirmatory DoE Studies

Finalize Control Strategy

Identify Potential Critical Parameters

Verify CPP/CIPC & PARs

Identify Critical Parameters (CPP/CIPC) & Preliminary PARs

Non-Critical(OPP/IPC)

Non-Critical(OPP/IPC)

CPP = Critical Process Parameter; CIPC =Critical In-Process Control; OPP = Operational Process Parameter; IPC = In-Process Control;PARs = Proven Acceptable Ranges


6. Representative Fishbone Diagrams and FMEA (Example : Unit Operations 1, 3 and 5b)


6. Focus on Select Unit Operations (based on Control Points Matrix)

Centrifugation & Depth Filtration

Protein A Capture

Low pH Viral Inactivation

TFF #1

Polishing Step

Viral Filtration

TFF #2

API Filtration and Dispense

Production Bioreactor

Vial Thaw & Flask Expansion

Scale-Up Bioreactor Expansion

Unit Op 1

Unit Op 2

Unit Op 3

Unit Op 4

Unit Op 5a

Unit Op 5b

Unit Op 6

Unit Op 7

Unit Op 8

Unit Op 9

Unit Op 10

Vial T-FlaskErlenmeyer

Flasks

Seed Bioreactor

Seed Bioreactor

N

N - 1N - 2N - 3

11,000L Production Bioreactor

Seed Bioreactor


6. Method of Parameter Classification and PAR Establishment

Upstream vs. Downstream Methodology

Model Qualification & Components of Variance Study

Model Qualification

Parameter Assessment(Ishikawa/Fishbone Diagram)

Parameter Assessment(FMEA)

Screening DoE and pCPPSelection

Screening DoE and pCPPSelection

Univariate Design

Augmented Design and Response Surface Model Confirmatory DoE and

Augmented Design (if necessary)

CQA Models Worst-case Simulations for Establishment

of Theoretical PARs

Experimental Runs to Confirm PARs

CPP Verification and PAR Establishment using CQA Models and VC Studies

Viral Clearance Studies

Finalize Control Strategy Finalize Control Strategy

Upstream (Unit Op 3) Downstream


6. Small-Scale Model Development

3L Bioreactor Scale-down Model

To Simulate 11K Bioreactors

11K Commercial Scale Runs

To Qualify 3L Scale-down Model

• Comparable cell growth, metabolites, process parameters and titer profiles

• Comparable product quality attributes

• Equivalence of Process Performance Indicators (PPIs)• Equivalence of Critical Quality Attributes (CQAs)


6. Small Scale Model Qualification

3L Bioreactor Components of Variance (CVS) Study (N=18)

Small-Scale Model Qualification(PPIs and CQAs)

Equivalence between 3L and 11K Bioreactors

CVS Inputs:Raw MaterialsMedia and Feed PrepsWCB VialsExperiment Block (Time)

Equivalence Test

Small scale (3L) data runat target conditions

Large scale (11K) data

CVS variance was used for establishment of CQA acceptance criteria used for Screening and Confirmatory DoE


6. Methodology of Parameter Classification and PAR Establishment

Approach to Classification of Process Parameters


Process Parameter

CQA

-1.0 -0.5 0.0 0.5 1.0

-6-4

-20

24

6

Process Parameter

CQA

-1.0 -0.5 0.0 0.5 1.0

-6-4

-20

24

6P< 0.0004 P< 0.00001

6. Considerations for Practical Significance

Both are highly “statistically” significant effects

Only this effect is ofpractical significance

A statistically significant effect of a parameter against a CQA is informative but is not enough

Need to compare the parameter effects against a meaningful scale to determine its practical significance.


7. Summary of CPPs, Critical Limits and PARsTypes of Control Limits and Establishing Batch Record Ranges from PARs

– One-Sided Maximum Critical Limit

– Two-Sided Critical

Critical for certain CQAs

Critical for certain CQAs Critical for certain CQAs

Company Confidential ©2015 Eli Lilly and Company 18

Batch Record Range

Proven Acceptable Range

Batch Record Range

Proven Acceptable Range

8. Summary of OPP’s and Corresponding Operating Ranges

• For each unit op, OPP’s were determined after the risk assessment or after the range evaluation study

• Any process parameter not evaluated in a range study (i.e. not a pCPPbased on risk assessment) is classified as an OPP and assigned an operating range equivalent to development and/or manufacturing platform experience

• Any process parameter which did not demonstrate either statistically or practically significant impact on CQAs is classified as an OPP and assigned an operating range equivalent to the range studied for Screening DoE

• A subset of OPP’s were selected to be monitored for process consistency, e.g. bioreactor transfer viable cell density, downstream unit op yields, etc.


Health Authority Feedback on Control StrategyHighlights

• Predominant theme of questions for earlier filings from US/EU regarding control strategy was “provide more detail,” i.e. not enough information in filing to provide a complete understanding of all rationale• Provide list of all process parameters assessed for each unit op

• Provide justification for non-critical parameters for all unit ops

• Provide non-critical parameters that are monitored for process consistency and include in S.2.2 and S.2.4 of Module 3

• Provide fish bone diagrams and FMEAs for all unit operations

• Provide DoE designs, results and analyses for unit ops


Health Authority Feedback on Control StrategyHighlights

• Cell bank related questions• Provide acceptance criteria for replacement working cell banks (WCB);

provide justification for # of DS lots to support full scale qualification of replacement WCBs

• Increased focus on cell bank monitoring program• Provide more details for cell bank clonality

• Unit Operation 3 (Production Bioreactor) related questions• Provide additional details to support the assertion that the scale down model

is representative of the 11K bioreactor in terms of CQAs• Provide more details on assessment of practical significance• Increased focus on raw material lot-to-lot variability


• Provide more details for how control strategy was developed in subsequent BLA/MAA filings

• Proactively, maintain a list of filed OPP’s (for future BLA/MAAs) that will be monitored for process consistency

• For future late phase molecules, integrate DoE studies into early/intermediate stage development to improve process characterization knowledge prior to BLA/MAA filings

• Continue to track health authority feedback and incorporate into subsequent filings – continuous learning

Future Considerations


Acknowledgements


Cell CultureJose Santiago Monique Person Timothy CavadasOmobolade Ogbuewu Matthew Schwartz Lori BrandtElizabeth PiotrowskiRoseanna ShimanskySagar DesaiKatarzyna CaseDenise CunninghamAngel Arrubla

PurificationRichard ChenAnupama NalluriAmy HuebnerThomas TahanJessica Norton

FormulationJun GaoJoseph LiuJoel Goldstein

Bioanalytical SciencesTim BlancMing-Ching HsiehTun LiuBabita Parekh

Bioproduct Research and Development (BR&D)

BR&D NJMichael Barry

BR&D IndianapolisTongtong WangMichael De Felippis

Global Statistical Sciences Anthony LonardoAlan RichterYing ZhangPatrick Gaffney

TS/MSDane DorundoBrian KearnsChristopher Swanson Alex ButtkeRobert KlimchakVictor Goetz

ManufacturingRajeew GuptaJoseph TroianoAnthony GonzalezLorraine O’Shea

RegulatoryPetra CavallaroEdward SaltusLawrence Starke

Thanks/Questions!

Things alter for the worse spontaneously, if they be not altered for the better designedly.Francis Bacon (1561 -1626)


Documents

Use of QbD Principles for the Development of an … · Use of QbD Principles for the Development of an Integrated Control Strategy. CMC Strategy Forum Europe. May 22-24, 2017. Girish