QbD and smart PD join forces Understanding the interplay between resin variability and process parameters

Peter Hagwall, GE Healthcare Life Sciences

Common post-launch problems for upstream and downstream manufacturing

Raw material consistency

Process robustness to variation

Supply chain continuity

HTPD = high-throughput process development

DoE = design of experiment

Our initiatives to help you overcome these problems

Resin development

and manufacturing

• Design for six sigma resin development

• Genealogy engine and extended variability

monitoring and control

• Brilliant factories

Process

development tools

• HTPD formats

• DoE in ÄKTA™ chromatography systems

• Support smart process development

• Process characterization kits

• Process characterization services

• Modeling tools

Manufacturing • Supply chain sustainability

• Business continuity management

• Communication

• Connecting digital platforms

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Drivers for deeper process understanding

Limit risks for

• QA investigations 20–100 kUSD

• Yield loss per batch 0.1 M USD

• Lost batches 1 M USD

• Commercial revenue impact

Biological production exposure to raw material variability Drivers for process understanding

Upstream cell culture

Media and feed components

Process parameters

Trace metals

Downstream

Resin attributes

Process parameters

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Traditional examples of CQA “Newer” CQAs

• Primary sequence

• Aggregates

• Fragments

• Overall HCP content

• Oxidated species

• Virus clearance

CQAs

• ADC

- Drug/antibody ratio

• Bispecifics

- Incorrect pairing

- Half antibodies

• Stability related

- HCP content affecting polysorbate

- Product fragments

- Charge variants

• New analytics

- Removal of specific HCP

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CQA = critical quality attributes

HCP = host cell proteins

ADC = antibody drug conjugate

CQA = critical quality attributes

HCP = host cell proteins

ADC = antibody drug conjugate

Molecular diversity creates purification challenges

What proportion of non-mAbs are in your pipeline? What are the current main pain points for non-mAbs?

0 to 10% non-mAbs

11 to 30% non-mAbs

31 to 50% non-mAbs

More than 50% non-mAbs

Capacity

of affinity

resins

Lack of

purification

platform

Product related

impurities

Alkaline

stability of

affinity

resin

0 %

5 %25 %

70 %

Answers collected from 20 attendees of the GE antibody development meeting 2018

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Voice of process developers about the interplay of process parameters with resin variability

”Resin variability is a blind spot to us””Develop understanding of resin variability

impacting product quality.

Required for BLA.”

“Several observations made

regarding impact of resin

variability on product quality”

“Most processes show no impact,

but this is molecule dependent”

BLA = biologic license application

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Voice of process developers about resin variability7 out of 9 had seen impact of resin properties on purification performance

• Risk assessment guides when to characterize the interplay between process parameters and resin properties.

• Resin variability is more likely to impact processes using:

– HIC and MMC chromatography techniques compared to IEX.

– Bind/elute mode compared to flow-through mode.

• Risk assessment indicates resin ligand density as the resin attribute most likely to impact process performance.

Feedback Trends in process robustness collaborations

IEX = ion exchange chromatography

MMC = multimodal chromatography

HIC = hydrophobic interaction chromatography

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AIEX = anion exchange chromatography, CIEX = cation exchange chromatography, HCP = host cell protein

Modality Type of separation Attribute Impact Reference

AIEX

Product-related species Unknown Elution peak appearance Biogen 2011

Product-related species Ligand density Product loss during wash Biogen 2009

Aggregate removal

Product-related speciesLigand density

Aggregate removal

Product Yield

Chromatogram appearance

Biogen 2013

CIEX

Aggregate removalLysozyme capacity

(particle size showed no impact)

Product yield

Aggregate clearanceAstraZeneca 2017

Product variants Multiple Product form distribution Roche 2016

Aggregate and HCP removal Ligand density No impact Genentech 2012

Aggregate and HCP removal Particle size Elution volume Genzyme 2008

Aggregate removalLigand density and porosity (particle size showed no impact)

Aggregate removal

Product yield

GE Healthcare Life Sciences

2014

It is not common, but resin properties might impact process performance—published examples (1/2)

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Modality Type of separation Attribute Impact Reference

HIC

Aggregate removal Ligand densityAggregate removal

Product yieldBiogen 2009

Product variants Binding strength Product form distribution Pfizer 2014

Product related species Multiple Impurity removal Genzyme 2017

HCP removal

Glycoform profileLysozyme retention

Product yield

Glycoform profileGenzyme 2009

HIC, flow-through mode Aggregate and HCP removal Lysozyme retention No impact within spec BMS 2010

HIC, flow-through mode,

low salt

Aggregate removal Lysozyme retention No impact Biogen 2017

Aggregate removal Three lots No impact Biogen 2013

Multimodal AIEX Aggregate removal Ligand densityProduct yield

Aggregate clearance

GE Healthcare Life Sciences

2013

Protein A affinity

chromatographymAb capture

Ligand density

Particle size

Accessible pore fraction

No impactGE Healthcare Life Sciences

2015

It is not common, but resin properties might impact process performance—published examples (2/2)

AIEX = anion exchange chromatography, HIC = hydrophobic interaction chromatography, HCP = host cell protein

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How are processes developed today?

Minimizing the impact of variability: smart process design based on QbD principles Find the balance between quality and performance

Critical raw material

attributes (CMA)

Critical process

parameters (CPP)

Critical quality attributes

product (CQA)

Toolkit for process design

HTPD

PAT Adaptive process control

Mechanistic modelingDoE

HTPD = high-throughput process development

PAT = process analytical technology

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Statistical tools

The interplay of process parameters and resin properties—what limits characterization?

This interplay may be a blind spot in risk assessment

Design for six sigma is applied to ensure resin supply and quality

• Variability samples unlikely to exist in inventory

• > 5 year sampling needed

• 5 year shelf life

Larger study using a design of experiments (DoE) approach would involve more time and experiments

Normal operating

range

LSL = lower specification limit

USL = upper specification limit

Ligand density

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A route to deeper process understanding and robustness

A route to deeper process understanding and robustness—how?

Support model-

assisted developmen

t

Enable easy access to subject matter experts

Make variability

samples available

Make risk assessment

input available

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Our recommendations for process characterization studies

Generic risk assessment—when should you study resin variability during process characterization?

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Process Characterization Kits (available in Nov 2019)

Resin kits intended for process characterization or platform studies together with process parameters

• Each lot in the kit has a defined ligand density level: low, average, or high.

• The three levels are designed to span the manufacturing envelope.

• The resin is specifically created to display variability, using a base matrix lot carefully selected to reflect typical process outcomes.

Process Characterization Kits Three ligand densities for a given resin

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Process Characterization Kits are useful for understanding bulk resin performance

Capto™ adhere ImpRes mAb retention Capacity study for Capto adhere ImpRes resin

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mAb elution conductivity in a gradient

0%–100% elution buffer in 20 column volumes

Starting condition: 20 mM citrate, 20 mM phosphate, pH 7.8

End condition/elution: 20 mM citrate, 20 mM phosphate, pH 4

Sample: mAb 3 mg/mL

Starting condition: 20 mM citrate, 20 mM phosphate, pH 7.8

Bed height: 10 cm

Residence time: 4 min

Process Characterization Kits

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Multimodal cation exchange resins

Capto™ adhere

Capto adhere ImpRes

Capto MMC ImpRes

Capto MMC

Hydrophobic interaction chromatography resins

Capto Phenyl (high sub)

Capto Phenyl ImpRes

Capto Butyl ImpRes

Ion exchange chromatography resins

Capto S ImpAct1

Capto SP ImpRes

1 Available during 2020

Low HighAverage

Factors that can impact robustness towards resin variability

Chromatography resin factors:

• Process development performed on one resin lot

• Resin intended use

Column packing factors:

• Column packing quality (HETP and asymmetry)

• Column packing compression factor proxy for resin amount and therefore binding capacity

Process parameters factors:

• Pooling criteria

• Wash step

• Buffer composition

• Load mass

• Sample variability

HETP = height equivalent to theoretical plate

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Studying ligand density impact on a mAb/Fab separation using Capto™ Phenyl ImpRes resin

0

20

40

60

80

100

DB

C a

t 4

min

RT

(m

g/m

L)

QB10 QB80

DBC on Capto Phenyl ImpRes at different ligand densitiesFrontal analysis on Capto Phenyl ImpRes at different ligand densities

A2

80

nm

(AU

)

Volume (mL)

Low ligand density

Average ligand density

High ligand density

Low Average High

Ligand densityQB10 = Capacity at 10 % breakthrough

QB80 = Capacity at 80 % breakthrough

RT = residence time

DBC = dynamic binding capacity

QB10 QB80

Insignificant difference in binding capacityKA10467111219PP

Studying ligand density impact on a mAb/Fab separation using Capto™ Phenyl ImpRes resin

1,5

1,6

1,7

1,8

20

25

30

35

40

45

50

Re

solu

tio

n (

Rs)

Re

ten

tio

n t

ime

(min

)

Retention time Fab (min) Retention time mAb (min)

Resolution (Rs)

Chromatogram on Capto Phenyl ImpRes at different ligand densities

Resolution on Capto Phenyl ImpRes at different ligand densities

Volume (mL)

A2

80

(mA

U)

Co

nd

uct

ivit

y(m

S/c

m)

Low Average High

Ligand density

Low ligand density

Average ligand density

High ligand density

Conductivity

Effect of ligand density on yield and retention | Minor impact on resolution

22% yield loss for high ligand density due to incomplete

elution.

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Shift process parameter values Probability assessment

Use process parameters for which process performance will not be impacted by resin variability.

Extended PD work

☺ Simple to implement

Adaptive control strategy Custom specification Lot mixing

Possible scenarios to prevent process variations due to resin variability

Use e.g. Monte Carlo simulation to understand likelihood of impact and accept some risk for process impact.

Risk for impact on process economy

☺ Data-driven and risk-based decision

Adjust process parameters settings to the properties of a given resin lot to best process outcome.

Might be seen complex by manufacturing operation teams

☺ Maximizes process performance and product quality

The supplier will supply a resin that has a specific ligand density (custom product).

Higher cost

Need close collaboration with supplier

☺ Maximizes process performance and product quality

The average value of any resin attribute for the mixture will tend towards the middle of the specification.

No guarantee that the performance of the mixture is the same as the average indicates

Driving storage cost and complexity

☺ Simple to implement

Preferred. Sustainable.

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Smart process development: study process parameters and resin interplay…early

1. Include in DoE

2. Include in DoE with CPPs only

3. Test at process edge of failure

4. Test at, or around, process target conditions

• Monte Carlo assisted simulations

• Mechanistic modeling

Alternative approaches Worst case/best case characterization

CPPs = critical process parameters

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Elution conductivity

Elu

tio

n p

H

Target

conditions

Low productivity

corner

Edge of failure for

product quality.

High productivity

corner

Ensure process robustness in

chromatographyMolecular diversity creates new purification challenges

Perform risk-based process characterization

Develop a solid control strategy

gelifesciences.com/QbD

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