Challenges in Establishing Risk Based, Phase Appropriate

Product Specifications in the Real World…

Chuck Smith

Seattle Genetics, Inc.

WCBP 2013

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Presentation Outline

Setting specifications - key considerations based on ICH

Challenges across the lifecycle

Systematic approach

Example scenarios

Life cycle considerations - early vs late

Conclusions

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Biotechnology company focused on innovative, empowered antibody-based therapies for the treatment of cancer

ADCETRIS® (brentuximab vedotin) granted accelerated approval for two indications by FDA

Robust ADC product pipeline designed to address unmet medical needs

Founded in 1998 Publicly traded (Nasdaq: SGEN) >525 employees Lead ADC programs

ADCETRIS (brentuximab vedotin) SGN-75 ASG-5ME ASG-22ME SGN-CD19A

Seattle Genetics Overview

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Antibody Drug Conjugate Structure

IgG1κ monoclonal antibody Linker: chemically stable MMAE: synthetic small molecule

Drug MMAE

cytotoxic agent

Linker Antibody Attachment

group Protease-

cleavage site

NHN

NO

O

OOCH3 O

N

OCH3

NH

HO

O

O

NH

HN

NH

NO

NH

O

OO

O

NH2O

S

Brentuximab Vedotin Mechanism of Action

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ADC Manufacture

mAb (Intermediate)

Drug-Linker (Intermediate)

Bulk Drug Substance

Drug Product

BDS Process Overview

Process Concept

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Setting Specifications and ICH

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Key Considerations for Setting Specifications From ICH Q6B

• This suggests a dialogue / negotiation

• Important to talk to HA early and often

Specifications: Critical quality standards that are proposed and justified by the manufacturer and approved by regulatory authorities as conditions of approval.

Specifications are one part of the total control strategy designed to ensure product quality and consistency.

• Product and process characterization during development • Process design and justification incorporating orthogonal

control elements • Adherence to Good Manufacturing Practices • Process Validation • Raw material testing, in-process testing, stability testing,

etc.

Other aspects of control strategy

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Key Considerations for Justifying Specs From ICH Q6B

• Necessary to establish product knowledge • Allows for relevant quality attributes to be chosen

Characterization

• Linked to preclinical and clinical studies • Prior knowledge (platform molecule, literature) • Linked to a process • Linked to analytical procedures • Characterization and stability

Justification of the specification

• Data from lots used in preclinical and/or clinical studies • Data from lots used for demo of mfg. consistency • Data from stability studies and other relevant development data.

Acceptance criteria should be established and justified based on:

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So, What Are the Challenges in General?

• Limited manufacturing experience (small n) • What is the true process capability? • Do we know the true process average and

process variability?

Limited Process Knowledge

• Extensive product characterization is good, but still leaves gaps

Limited Product Knowledge

• Clinical experience is usually with material representing the center targets

• Safety trials not conducted with material at the desired limits for impurities and variants

Limited Clinical & Nonclinical Experience

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Suggested Reading ICH Guideline “Q6A Specifications: Test Procedures and Acceptance

Criteria for New Drug Substances and New Drug Products: Chemical Substances”, www.ich.org

ICH Guideline “Q6B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products”, www.ich.org

ICH Guideline “Q8 Pharmaceutical Development”, www.ich.org

A-Mab: a Case Study in Bioprocess Development, CMC Biotech Working Group, Version 2.1, 30th October 2009. www.CASSS.org

Kozlowski, S. and Swann, P. (2009), Considerations for Biotechnology Product Quality By Design, Quality by Design for Biopharmaceuticals, Rathore, A.S. and Mhatre, R. (Eds.).

Schenerman, M. et. al. (2009), Using a Risk Assessment Process to Determine Criticality of Product Quality Attributes, Quality by Design for Biopharmaceuticals, Rathore, A.S. and Mhatre, R. (Eds.).

Baffi, R. (1997), The Role of Assay Validation in Specification Development, Development of Specifications for Biotechnology Pharmaceutical Products. Dev. Biol. Stand. Vol 91, Brown, F. and Fernandez, J. (Eds.).

Geigert, J. (1997), Appropriate Specifications at the IND Stage, Development of Specifications for Biotechnology Pharmaceutical Products. Dev. Biol. Stand. Vol 91, Brown, F. and Fernandez, J. (Eds.).

How To Approach The Problem? - A Systematic, Risk Based, Lifecycle Approach

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A Systematic Approach to Development

A-Mab: a Case Study in Bioprocess Development, CMC Biotech Working Group, Version 2.1, 30th October 2009. www.CASSS.org

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A Systematic Approach to CQA’s

• High ranked QA’s should have robust controls (i.e. they are critical)

• Low ranked are not CQA’s and may not need control, but will need strong justification

Ranking CQA’s identifies those that must be considered and controlled by the mfg. process

• Control strategies are based on the scientific understanding of the linkage between product quality attributes and the safety and efficacy of the product and process design/capability

Develop and justify the control strategy

Develop and justify the specification strategy

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Lifecycle Management

Set Acceptance

Criteria

Choose Release Tests

Establish the overall control strategy based on process knowledge and impact to

CQA’s

Establish Quality Attributes and their associated Criticality

A Systematic Approach to Setting Specifications

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Choice of Tests – The Easy Part

Selected tests for parameters/attributes necessary to confirm the relevant identity, safety, purity, potency and strength

Considers the appropriate analytical technology and inherent capability

Based on product knowledge and attribute criticality

Based on process knowledge and sensitivity to process control

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Example Quality Attributes and Control Points

Universal Quality Parameters

Associated Quality Attributes

mAb ADC BDS ADC DP Potency Binding Binding

Cytotoxicity Cytotoxicity Purity HCP

Free drug-related impurities

Free drug-related impurities

Size Variants Size Variants Size Variants Charge Variants Charge Variants Charge Variants

Safety Bioburden Bioburden Sterility Endotoxin Endotoxin Endotoxin

Identity mAb Specific ADC Specific ADC Specific

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Provides assurance of conformational integrity and binding for both mAb and ADC The mAb and ADC have similar binding properties The acceptance criteria for mAb and ADC may be the same or

very similar

Potency – Antigen Binding Ability Direct Binding ELISA

HRP-F(ab’)2 α-Human

F(ab’)2 Target Antigen

ADC or

mAb

Substrate

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Potency – Biological Activity Cell-based Cytotoxicity Assay

Cytotoxicity assay is specific for the ADC

Cytotoxicity assay confirms both binding and bioactivity

Applied to release of BDS and DP

Acceptance criteria for BDS and DP may be the same

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Setting and Justifying Acceptance Criteria – The Hard Part

ICH Q6B Application

Linked to Clinical / Non-clinical Experience

Demonstrated Safety Concept of “fit for use”

Linked to Manufacturing Process Process characterization and justification Concept of “fit for use”

Data from Manufacturing consistency runs

Statistical tolerance intervals Process capability estimates

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Size Variants by SEC – Process Knowledge

Conjugation Process can affect % HMW and % LMW

% HMW is a product related impurity considered to be a CQA and should be minimized Potential for immunogenicity and/or hyper-potency

A limit of ~5% is typical in industry for IND stage programs

mAb

ADC

Same assay, correlated result

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Setting Specifications for %HMW

Management of %HMW for ADCs requires applying appropriate Acceptance Criteria at each stage of production

Looking forward: Quality of the DP is primarily determined by the levels inherent in the mAb The BDS conjugation process and the DP processing may all impact and add to

the final %HMW level in DP. Therefore, the acceptance criteria established for the mAb must consider the

affects and contributions of further processing since ADC process provides no additional unit operations for the removal of HMW species.

Looking backward: Setting the limit for the DP can help dictate the maximum acceptable levels for

the BDS and intermediate

Attribute mAb BDS DP % HMW < x% < (x + y)% < (x+y+z)%

Attribute DP BDS mAb % HMW < x% < (x - y)% < (x-y-z)%

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Setting and Justifying Acceptance Criteria – The Hard Part

ICH Q6B Application

Linked to Clinical / Non-clinical Experience

Demonstrated Safety Concept of “fit for use”

Linked to Manufacturing Process Process characterization and justification Concept of “fit for use”

Data from Manufacturing consistency runs

Statistical tolerance intervals Process capability estimates

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Understanding Process Capability and Tolerance Intervals (TI)

• similar to process capability; i.e., they show the practical boundaries of process variability and therefore can be a valuable input in the determination of product acceptance limits.

Statistical tolerance limits

• defines the limits within which a specified percentage of the population is expected to lie with a given probability

The TI calculation

• K is a factor (obtained from statistical table or computation) based on the desired percentage of the population to be included, the probability of inclusion, and the number of measurement used to calculate mean and standard deviation.

• The factor k increases with decreasing n, i.e. larger n = smaller factor to multiply times the standard deviation.

TI = 𝑋 ± k𝑠

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Size Variants: mAb Lot History (%HMW) -Run Chart

Later lots are lower Process appears stable overall

0.0

Lot

% H

MW

% HMW Average USL

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Size Variants: %HMW mAb Tolerance Interval and Capability Assessment

Capability Analysis

One Sided TI Analysis

Main HMW LMW

n 18

Mean 98.2 0.5 1.3

Std Dev 0.2 0.2 0.1

k 3.3

1 sided TI

> 97.6 < 1.0 < 1.7

mAb: % HMW

0

1

2

3

4

5

6

7

8

9

10

0.0 % HMW

Num

ber

USL

Mean 0.5 Median 0.4 Mode 0.4

Cp 4.1 Cpk 4.1 CpkU 4.1 CpkL Cpm 2.1 Pp 2.1 Ppk 2.1 PpU 2.1 PpL Stdev 0.2 Min 0.3 Max 0.9 Z Bench 12.4 ZTarget 0.0 % Defects 0.0% PPM 0.0 Expected 0.0 Sigma 6.0

TI analysis provides guidance on setting potential process limits

Considered with other factors such as overall process design knowledge, attribute risk etc.

Proposed acceptance criteria can then be evaluated using process capability analysis

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Size Variants (%HMW): ADC Lot History

Pattern not normal

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BDS SEC (% HMW)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

GSM

001

GSM

002

GSM

003

GSM

004

GSM

005

GSM

006

GSM

007

GSM

008

GSM

009

GSM

010

GSM

011

GSM

012

GSM

013

GSM

014

GSM

015

GSM

016

GSM

017

GSM

018

GSM

019

GSM

020

GSM

021

GSM

022

GSM

023

GSM

024

GSM

025

GSM

026

GSM

027

GSM

028

GSM

029

GSM

030

GSM

031

GSM

032

GSM

033

GSM

034

GSM

035

GSM

036

GSM

037

GSM

038

GSM

039

GSM

040

GSM

041

GSM

042

Lot

% H

MW

% HMWAverage

Important trends may not be apparent until after significant mfg. experience is gained This demonstrates importance of continuous process verification and periodic reassessment

based on additional data.

Work with HA to establish expectation that specifications may be adjusted periodically based on data and justification. Additional data will help to confirm process capability for tighter specs Conversely, it may also be the case where an acceptance criteria can be loosened to

account for true long term process capability as long as it can be justified w.r.t. safety, efficacy, etc.

ADC %HMW Results Bimodal Distribution BDS: SEC (% HMW)

0

2

4

6

8

10

12

14

0.5 0.8 1.1 1.4 1.7 2.0 2.3 2.6 2.9 3.2 3.5 3.8 4.1

% HMW

Num

ber

USL 3.5Mean 2.1Median 2.3Mode 2.7

Cp 2.3Cpk 2.3CpkU 2.3CpkLCpm 0.8Pp 0.8Ppk 0.8PpU 0.8PpLStdev 0.6Min 1.2Max 2.8Z Bench 6.8ZTarget 0.0% Defects 0.0%PPM 0.0Expected 0.0Sigma 6.0

Investigation confirmed correlation to input variable

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Using Statistical Tolerance Intervals - Caveats

Tolerance interval tools can help to anticipate future mfg. capability, but may be unreliable due to small n

It may take time (significant mfg. experience) for important patterns to emerge

Assumptions: process in control, data independence, normal distribution

In some cases, there may be co-factors that affect the overall independence of the data

Use statistical tools as guidance only, along with other considerations, when determining and justifying acceptance criteria

Take your statistician to lunch!

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Phase Appropriate Specs – Early vs Late

Early Phase 1

n = 1 – 3

• Pending clinical safety and clinical proof of concept

• Broad specs based on target product profile and tox. lot profile

Late Phase 3

n = 7 – 15

• Product safety and efficacy demonstrated

• Clinical experience does not encompass full range of product at specification limits

• Process and product well characterized

• Process capability estimated with caveats

• Final commercial specs proposed and justified pending HA negotiation

Post Commercial and Ongoing Lifecycle

n > 50

• Ongoing PV • Safety profile experience

better understood • Both companies and HA’s

should expect and plan for periodic review

• Ultimate long-term process capability becomes revealed overtime

• May require specification re-assessment and follow up regulatory submissions

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Specifications Overall Strategic Considerations Establishment of specifications requires the consideration of many factors for which the degree of knowledge may be incomplete

Risk based approach considers many factors, i.e. indication, dosing, quality attribute criticality, process design and capability, clinical experience

Document justification in technical reports

Open dialogue with health authorities is essential

Early stage specifications should be focused on safety and leverage prior knowledge of similar products

Late stage specifications may be set based upon preliminary process capability estimates but will need to anticipate additional re-assessment post commercial launch (PMC)

Manage as a lifecycle

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Acknowledgements

•Seattle Genetics •Vaughn Himes •Shawn Novick •Nathan Ihle •Oscar Salas-Solano •Todd Coffey •Quality Control Dept. •Analytical Biochemistry Dept. •Bioprocess Development Dept.

•External •Claudia Jochheim •Lotte McNamara