1. “PAT” Applications for Biochemical Processes
Shih-Hsie Pan
Interphex, March 19, 2009

2. PAT Framework 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 Analysis
Process Monitoring
Process Control
Process Design
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

3. PAT: Process Information Enabling QbD
Laboratory
Production
Area
Diverted
Sample
Inserted
Probe
No Product
Contact
Off-Line
At-Line
On-Line
In-Line
Non
Invasive
Real-time
release
Transition Analysis
Predictive Modeling
NIR Probe

4. 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.

5. Significant Reduction in RSD  Improved Consistency in
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

6. Chromatographic Transition Analysis
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.

7. Affinity Elution Chromatogram
Loss of Column Integrity
Affinity Elution Chromatogram
Chromatogram improved after lowering flow adapter

8. Transition Analysis Identifies Changes
Column Repacked
Lowered Flow Adapter
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.

9. 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.

10. 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

11. Using Technology…. To manage process performance
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
Using Technology…. To manage process performance

12. Data courtesy of Kirin Jamison
Prediction of protein titers with PLS model
based on 1695 variables
Data courtesy of Kirin Jamison

13. Acknowledgement My colleagues at Genentech: Eric Fallon Robert Kiss
Harry Lam

14. Back-up

15. Amino acid analysis by on-line HPLC
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)

16. QbD Model