“PAT” Applications for Biochemical Processes

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

8. Column Repacked Lowered Flow Adapter Transition Analysis Identifies Changes • 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. Case Study Results Using Technology…. To manage process performance

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

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

14. Back-up

15. 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