© 2005, Genentech

“PAT” Applications for Biochemical Processes

Shih-Hsie Pan

Interphex, March 19, 2009

© 2005, Genentech

PAT Framework

Process Monitoring

Process Analysis

Process Control

Process Design

Multivariate data acquisition and analysis

tools• Process chemometrics

• Intelligent use of process data

Modern process analyzers• Process analytical

chemistry tools• In-process monitoring

techniques

Process and endpoint monitoring and control

tools • Process supervisory

control• High level multivariate

control strategies

Design for Quality• Continuous improvement

and knowledge management tools

• FMECA• DOE

Slide 2

© 2005, Genentech

PAT: Process Information Enabling QbD

Laboratory

Off-Line On-Line In-LineAt-Line

ProductionArea

DivertedSample

InsertedProbe

Non Invasive

No ProductContact

Real-Real-timetime

releaserelease

Predictive Modeling NIR Probe

Transition Analysis

Slide 3

© 2005, Genentech

Benefits of PAT for Biologics• Increase knowledge of product and process

– Identify critical steps and parameters (CCP’s and CPP’s) that impact quality– Lower the cost of process improvement to increase yield, quality & robustness– Minimize process validation cost – direct, real-time process control– Facilitate reduction of batch-to-batch variability for better quality and

predictability

• Allow near real time critical parameter conformance monitoring and comparisons – continuous quality assurance and validation

– Assist validation efforts for characterization and documentation of process changes

• Reduce testing requirements at end of process• Assess deviation impact in real time

– Avoid costs of processing unreleasable batches– Data justification of batch release

• Provide an ability to quickly identify shifts, trends, or outliers in the data, so that investigations can be conducted and decisions made on lot release quickly to reduce manufacturing risk.

Slide 4

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Automated (At-Line) Cell Count and Viability Determination By Image Analysis

Significant Reduction in RSD Improved Consistency in Mfg Operations based on Cell Count or %Viability

Courtesy of Polina Rapoport

Slide 5

© 2005, Genentech

• Real time method developed for monitoring column packing quality.

• Calculates plate number directly from transition curve.

• No off-line pulse injection tests required; uses process data.

• Predictive of column performance.

Chromatographic Transition Analysis

Filtered

0

0.2

0.4

0.6

0.8

1

40 90 140 190 240 290 340 390 440 490 540

VolumeNo

rmali

zed

C

Volume0

1

Cno

rmal

Cmax= Ci

ni=1

ni=1

σ2 (Vi-Vr )2ΔCi

Vr Vi Ci)ni=1

ni=1ni=1

ni=1

Vr

N = Vr2 / σ2

HETP = L / N

Slide 6

© 2005, Genentech

Affinity Elution Chromatogram

Normalized CV vs Elution OD

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3

Normalized CV

OD

R52

R53

R54

R55

R56

R57

R58

Chromatogram improved after lowering flow

adapter

Loss of Column Integrity Slide 7

© 2005, Genentech

• HETP data clearly identifies changes in column integrity.

• Values increase with time after column packing.

• Original HETP value is restored after lowering the top flow adapter.

• Increased measurement variability is observed when column integrity decreases.

0.2

0.3

0.4

0.5

0.6

0.7

0.8

HE

TP

(cm

)

40 80 120 160 200 240 280 320 360 400 440 480 520 560

Cycles

Avg=0.330

LCL=0.266

UCL=0.393

Individual Measurement of HETP (cm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Mov

ing

Ran

ge o

f HE

TP

(cm

)

40 80 120 160 200 240 280 320 360 400 440 480 520 560

Cycles

Avg=0.024

LCL=0.000

UCL=0.078

Moving Range of HETP (cm)

Control Chart

Column Repacke

d

Lowered Flow

Adapter

Transition Analysis Identifies Changes Slide 8

© 2005, Genentech

Packed Cell Volume

PCV is an accurate measurement of biomass, but it also lends itself to many inconsistencies…

1) Manual operation that is variable from operator to operator.

2) Measurement is performed visually which can also be very subjective.

Drivers to evaluate alternative methods of determining biomass to ensure a more robust and informative estimate of inoculum transfer time.

Slide 9

© 2005, Genentech

Oxygen Transfer Rate (OTR)

-Definition

-kLa = mass transfer coefficient ,based on empirical data from each bioreactor family-C* = dissolved oxygen level at oxygen saturation point -CL = Dissolved Oxygen Concentration (should be a constant)

-Pros--OTR directly measures cell growth-OTR is a non-invasive method, per guidance definition

)( *LL CCakOTR

Slide 10

© 2005, Genentech

Case Study Results

R2 Value vs. Current

(Off-Line) Method

On-Line

Method

Non-Invasive Method

N-3 Stage 0.92 0.91

N-2 Stage 0.97 0.95

N-1 Stage 0.84 0.92

Using Technology…. To manage process performance

Slide 11

© 2005, Genentech

Prediction of protein titers with PLS model based on 1695 variables

Data courtesy of Kirin Jamison

Slide 12

© 2005, Genentech

My colleagues at Genentech:

Eric Fallon

Robert Kiss

Harry Lam

Acknowledgement

© 2005, Genentech

Back-up

Slide 14

© 2005, Genentech

Additional At-Line Analyses Have Increased Measurable Parameters

• Blood gas analyzers– Enable measurement of glucose, lactate, pCO2, pH, pO2,

ammonium, sodium, potassium and other metabolites

• Amino acid analysis by on-line HPLC– Amino acids along with glucose can be measured every hour

with automated HPLC– Can enable more comprehensive view of how metabolism

shifts over the course of a culture– Can also be used for medium development & optimization

• Automated image analysis for cell count, viability, cell size (example)

Slide 15

© 2005, Genentech

QbD ModelSTRUCTURE-

FUNCTION PLATFORM

KNOWLEDGE

MOLECULE MECHANISM OF

ACTION

CRITICAL QUALITY

ATTRIBUTES

REGULATORY RELIEF

SAFE & EFFICACIOUS

PRODUCT

DESIGN SPACE

CONTROL SPACE

SAFE & EFFICACIOUS

PRODUCT

OPTIMIZED CONTROL SPACE

GOVERNED BY INTERNAL QUALITY SYSTEMS

PROCESS DEVELOPMENT

CLINICAL AND/OR COMMERCIAL

MANUFACTURING

PROCESS PLATFORM

KNOWLEDGE

PROCESS VALIDATION

RISKEVALUATION

DOE

SCALE-DOWNMODEL

VERIFICATION

PROCESS CONTROL &

MONITORING

CONTINUOUS PROCESS

IMPROVEMENT

RISKEVALUATION

STATISTICAL PROCESS CONTROL

Slide 16