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Continuous Process Verification of Next-

Generation Processes Utilizing Advanced

Process Control (APC)

CMC Forum Europe, Paris, 09May2016

Rohin Mhatre, Ph.D.

Vice-President, Regulatory Affairs

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• Multiple tools for analyzing raw materials

• Good understanding of critical components in the raw materials

affecting product quality

• Understanding of process levers that control product quality

• Linkage of the unit operations

• Feed back/forward adaptive control

• Extensive real time monitoring systems

• Sophisticated models for predicting product quality

• Significant process history for commercial products

State of our Processes…

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15kL3750L950L

Seed

train

Production

ReactorHarvest

Raw

MaterialsPurification Filling

DS

Rele

ase

Bioreactor: in-line

Raman, capacitance

In-line: protein concentration

At line: purity, aggregation,

charge variants

DS

• Endotoxin

• Bioburden

• Potency

UF/DF: in-line Protein

concentration

Biotech Drug Substance Advanced Process Controls

feedback feedback

Feed

forward

Raw Materials

• Geneology

• Spectroscopic tools

• Rapid ID testing

Multivariate discrete analysis for process monitoring and

disposition decisions, Predictive model for RTRT or FF control

APC

CQA Predictive

models for

feedforward control

Real-time multivariate analysis for trending, actions and disposition decisions

Bioreactor and/or harvest:

At line multi-attribute testing and/or CQA

Predictive models for feedforward control

Media/buffer

Prep control

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Advanced Process Control

Product Quality &

Yield

Process Input

Pro

cess

Process Control • Robust process design • Advanced process control

Raw Material

RM Control• Screening and/or • Control by Vendor• Learn from MFG experience

PAT PAT

4 4

4 Real time quality

PATActual or virtual

sensor

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• Develop a clear connection of process parameters that affect

product quality attribute/s

• Predictive models that can predict product quality

• Process signatures that are indicative of product quality (growth

curves, peak shapes, etc.)

• On-line measurement and control

Use these tools to assure process and

product quality consistency

General Approach -APC

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Test Goal Analytics

Raw Material

(individual and

media)

Media feed control (for

consistency in Yield,

Product Quality)

Raman spectroscopy

Fluorescence

UV

NMR

XRF

Product Quality in

Process

Intermediates

Process understanding

Process monitoring

Feed forward control

Feedback control

Real time release

LC-MS or 2D-LC

CE Western

With rapid purification:

• Peptide mapping LC/MS

DNA/HCP in Process

Intermediates

Column performance

monitoring

qPCR with automated

extraction (DNA)

High throughput

immunoassays (HCP)

APC – In-Process Analytics

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• Test for: %aglycosylated protein and specific glycoforms

• Collect time-course data during cell culture process characterization (PC)

• Simple, high throughput method can support the large number of PC samples

• Data supports predictive model building (and possible control levers, if any)

• Based on outcome of PC studies, determine if there are feed forward or feedback control opportunities

• Define testing point(s) and target range of the product quality attributes (including method/process variability)

Production Bioreactor Testing using

Reduced Intact Mass Spectrometry

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Reduced Intact Mass for mAb

DTT

HC-Agly LCMan5

G0F G1F

G2F

G0F-GlcNAc

LC-MS

%Agly

%Man 5

%Biantennary

Galactosylation

Agly

G0FG1F

G2FMan5

G0F-GlcNAc

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APC – Upstream Control

Feedback control - Glucose / Lactate control

Feed Automation using Raman Spectroscopy

Maintain glucose at low levels to minimize glycation and restrict lactate formation

Low glucose

feedback control

High glucose

feedback control

Consistent Cell Growth = Consistent Titer and PQ

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DS

Cap

ture

Inte

rme

dia

te

Po

lis

h

Bioreactor

HarvestPurification

APC – Downstream Control

Goals:

Real time release

Real time adaptive

downstream control

Fast response to issues

Multi-attribute method

for product quality

On

-lin

e s

am

plin

g

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Viral Inactivation

Capture Column

Polish Column #1

Polish Column #2

≤ “x” g/L Loading

Viral Filter

UF/DF

Harvest At Line

Rapidly

Measure %

Impurity in

Column Load

No FF

Load ≤ [x]

(n cycles)

≤2.5%

FF

Load ≤ [x – y]

(n + 1 cycles)

FF

Load ≤ [x – 2y]

(n + 2 cycles)

≥3.5%2.6-3.4%

APC Feed Forward (FF) Control Opportunity

Impurity controlled to ≤ 2.5%

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CQA Predictive Modeling for FF Control and RTRT

Purpose Traditional Future State

Product quality prediction in near real time

Releasebased on CQA test

Release based on Predictive Model

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Predictive Model Development Strategy

Data Acquisition Data Population

Data Pre-treatment

Exploratory Analysis Model Optimization Model Validation

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Identifying Product Attributes Possible for Predictive Model Development

Modelability? (Y)Release or FF control? (Y)

mAb CQA’s

CQA 1

CQA 2

CQA 3

CQA 4

CQA 5

CQA 9

CQA 10

PROCESS PERFORMANC

PARAMETER 1

CQA 6

CQA 7

CQA 8

CQA 15, CQA 16, CQA 17

CQA 11

CQA 12

CQA 13

CQA 14

Model

Considerations

• Modelability is assessed based on:

• CQA variability in development/mfg experience

• Assay variability

• Simple or complex source of variability

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APC – Informatics

Real Time Univariate and Multivariate

Statistical Process Monitoring

Advanced Analytics for Predictive

Modeling of Product Quality

Real Time Quality

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Process Design

• FDA

• Design

• EMA

• Evaluation

‘Qualification’ (PPQ)

• FDA

• Qualification

• EMA

• Verification

Verification (CPV)

• FDA

• Continued Process Verification

• EMA

• Ongoing Process Verification

PV for Traditional Process Control

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Process Design

• FDA

• Design

• EMA

• Evaluation

Continuous Verification

• FDA

• Combine Stages 2/3

• ICH/EMA

• Continuous Process Verification

PV for Advanced Process Control

• Performance qualification applied to all batches

• Employs continuous measurement on every batch produced

• Data trending is an integral part of the approach

• Validation based on development knowledge and on-going manufacturing

performance confirmed on every batch

• Combining stages 2/3 builds upon established examples (e.g. reprocessing)

• Scientific knowledge and risk assessment reduce the need for discrete re-

validation exercises

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APC process characteristics:

Raw materials control minimizes process input variability

Quick and decisive product quality analytical methods for intermediates

real time product quality at process intermediates for every batch

Feedback control loops in the bioreactor for nutrients (based on cell density)

and glucose (to maintain low glucose levels) consistent cell growth

Consistent cell growth = consistent titer and product quality

Real time adaptive downstream product quality control

Advanced informatics tools provide real time process data and product quality

predictions

Process Validation for APC Process

Continuous Process Verification (ICH Q8)/

Continuous Quality Verification (ASTM)

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What is Continuous Process Verification?

EU Annex 15, Mar 2015

Continuous process verification

5.23. For products developed by a quality by design approach, where it has been scientifically established

during development that the established control strategy provides a high degree of assurance of product quality,

then continuous process verification can be used as an alternative to traditional process validation.

5.24. The method by which the process will be verified should be defined. There should be a science based

control strategy for the required attributes for incoming materials, critical quality attributes and critical process

parameters to confirm product realisation. This should also include regular evaluation of the control

strategy. Process Analytical Technology and multivariate statistical process control may be used as tools.

Each manufacturer must determine and justify the number of batches necessary to demonstrate a high level

of assurance that the process is capable of consistently delivering quality product.

5.25. The general principles laid down in 5.1 – 5.14 above still apply.

Hybrid approach

5.26. A hybrid of the traditional approach and continuous process verification could be used where there is a

substantial amount of product and process knowledge and understanding which has been gained from

manufacturing experience and historical batch data.

5.27. This approach may also be used for any validation activities after changes or during ongoing process

verification even though the product was initially validated using a traditional approach.

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What is the future…

Much greater level of process understanding and control

Process signatures will help predict product quality

Adaptive control will be a critical element of process consistency

RTRT will be the new paradigm

Process validation through continuous process verification

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Acknowledgements

Eliana Clark – Manufacturing Sciences

• Sarah Yuan, Rob Guenard, Gregory Stromberg,

• Steve Doares, Canping Jiang

Valerie Tsang – Technical Development

Rashmi Kshirsagar - Cell Culture Development

• John Paul Smelko

• Alan Gilbert

John Pieracci – Process Biochemistry

• Brad Stanley

Li Zang – Analytical Development

Patrick Swann – Regulatory Affairs