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Process Validation for Biologics Manufacturing with Process Analytical Technology and Real-Time Release Testing

Presentation to 2015 CMC Strategy ForumGaithersburg, MD, July 20, 2015

Eliana Clark, Ph.D.

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Outline

• Biogen’s next generation DS manufacturing process

• Advanced Process Control (APC)• General Approach

• Enabling technologies • Analytical

• Upstream processing

• Downstream processing

• Informatics

• Process Validation approach

• The Future

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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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• 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

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Understanding Pharmaceutical Quality by DesignYu et al, The AAPS Journal, 2014

Current state

Next generation manufacturing process

QbD Control Strategies

Traditional Process Control

Control parameters at set point

Advanced Process ControlReal-time automatic control

PAT and RTRT enabled

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Advanced Process Control

Product Quality &

Yield

Process Input

Proc

ess

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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Test Goal Analytics

Raw Material (individual and media)

Media feed control (for consistency in Yield, Product Quality)

Raman spectroscopyFluorescenceUVNMRXRF

Product Quality in Process Intermediates

Process understanding Process monitoringFeed forward controlFeedback controlReal time release

LC-MS or 2D-LCCE WesternWith rapid purification:• Peptide mapping LC/MS

DNA/HCP in Process Intermediates

Column performancemonitoring

qPCR with automated extraction (DNA)High throughput immunoassays (HCP)

APC – In-Process Analytics

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APC – Upstream Control (fed-batch process)

Raw materials control - minimize raw material variability (input)

Feedback control - Feeding based on actual biomass present in reactor Feed automation using culture capacitance (Dielectric Spectroscopy) Ensures more consistent culture physiology and performance

consistent product quality

Feedback control - Glucose / Lactate control Feed automation using Raman Spectroscopy Maintain glucose at low levels to minimize

glycation and restrict lactate formation

Consistent Cell Growth = Consistent Titer and PQ

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DS

Cap

ture

Inte

rmed

iate

Polis

h

Bioreactor Harvest

Purification

APC – Downstream Control

Goals:

Real time release

Real time adaptive downstream control

Fast response to issues

Multi-attribute method for product quality

On-

line

sam

plin

g

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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 Validation

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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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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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According to the FDA PAT Guidance1, “With real time quality assurance, the desired quality attributes are ensured through continuous assessment during manufacture. Data from production batches can serve to validate the process and reflect the total system design concept, essentially supporting validation with each manufacturing batch.”

Continuous Process Verification: An alternative approach to process validation in which manufacturing process performance is continuously monitored and evaluated. (ICH Q8)2

Continuous Quality Verification (CQV):An approach to process validation where manufacturing process (or supporting utility system) performance is continuously monitored, evaluated and adjusted as necessary”. (ASTM)3

References: 1. FDA Guidance for Industry: PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing and

Quality Assurance, Sep 2004)2. ICH Q8- Pharmaceutical Development, also defined in the EMA PV guidances3. ASTM E2537-08: Standard Guide for the Application of Continuous Quality Verification to Pharmaceutical and

Biopharmaceutical Manufacturing;

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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• Scientific knowledge and risk assessment reduce the need for discrete

re-validation exercises

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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

Rohin Mhatre – Bioprocess DevelopmentThomas Ryll – Cell culture Development• John Paul SmelkoDoug Cecchini – Process BiochemistryPatrick Swann – Analytical Development• Kazumi KobayashiStacey Traviglia – Quality ControlAndre Walker – Manufacturing Sciences• Lilong Huang

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Data/Information Flow

Data housed in multiplesources is aggregated intoa centralized system

Centralized Data Management System

Raw Materials

Material Flow

ERP PI LIMS

Built-in analytics provide control charts, Capability analysis, etc.

System allows user to access data from any system

Batch

Interface

Inoculation Bioreactor Purification Drug Substance Drug Product

User

Data Management System(Discoverant)

Records

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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