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© 2005, Genentech PAT Applications for Biochemical Processes Shih-Hsie Pan Interphex, March 19, 2009.

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Presentation on theme: "© 2005, Genentech PAT Applications for Biochemical Processes Shih-Hsie Pan Interphex, March 19, 2009."— Presentation transcript:

1 © 2005, Genentech PAT Applications for Biochemical Processes Shih-Hsie Pan Interphex, March 19, 2009

2 © 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

3 © 2005, Genentech PAT: Process Information Enabling QbD Laboratory Off-LineOn-LineIn-LineAt-Line Production Area Diverted Sample Inserted Probe Non Invasive No Product Contact Real-timerelease Predictive Modeling NIR Probe Transition Analysis Slide 3

4 © 2005, Genentech Benefits of PAT for Biologics Increase knowledge of product and process –Identify critical steps and parameters (CCPs and CPPs) 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

5 © 2005, Genentech 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

6 © 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 Slide 6

7 © 2005, Genentech Affinity Elution Chromatogram Chromatogram improved after lowering flow adapter Loss of Column Integrity Slide 7

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

9 © 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

10 © 2005, Genentech Oxygen Transfer Rate (OTR) -Definition -k L a = mass transfer coefficient,based on empirical data from each bioreactor family -C* = dissolved oxygen level at oxygen saturation point -C L = Dissolved Oxygen Concentration (should be a constant) -Pros- -OTR directly measures cell growth -OTR is a non-invasive method, per guidance definition Slide 10

11 © 2005, Genentech Case Study Results R 2 Value vs. Current (Off-Line) Method On-Line Method Non- Invasive Method N-3 Stage N-2 Stage N-1 Stage Using Technology…. To manage process performance Slide 11

12 © 2005, Genentech Prediction of protein titers with PLS model based on 1695 variables Data courtesy of Kirin Jamison Slide 12

13 © 2005, Genentech My colleagues at Genentech: Eric Fallon Robert Kiss Harry Lam Acknowledgement

14 © 2005, Genentech Back-up Slide 14

15 © 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

16 © 2005, Genentech QbD Model Slide 16

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