Evaluation of potential PAT tools

Transcription

1 Evaluation of potential PAT tools Process Analytical Technology Siemens AG / NNE Pharmaplan GmbH Frankfurt / Main October 6 th 2010 Stefan Buziol Pharmaceutical Biotech Production & Development Roche Diagnostics GmbH Penzberg / Germany

2 Content Expected Benefit Basic Conditions Inline Monitoring (Near-Infrared Spectroscopy) Summary S. Buziol Evaluation of potential PAT-tools 2

3 Introduction Expected Benefit Source: Guidance for Industry Pharmaceutical CGMPs September 2004 S. Buziol Evaluation of potential PAT-tools 3

4 Basic Conditions Dedicated and appropriate Bioreactor & Operation stainless steel, stirred tank reactor operating volume = 10 liter low volume sampling valve SCADA system process control options dedicated to evaluation projects dedicated operator/engineer S. Buziol Evaluation of potential PAT-tools 4

5 Basic Conditions Strong & Comprehensive Reference Analytics Determination of concentrations of major cell culture analytes, cell related variables & parameters Chromatogram for quantification of glucose in cell culture medium containing complex raw material S. Buziol Evaluation of potential PAT-tools 5

6 Basic Conditions Clone & Process Clone: Medium: Process mode: CHO clone (Chinese Hamster Ovary cells, suspension culture) development medium, clone specific, containing complex raw materials batch / fed-batch, different process phases S. Buziol Evaluation of potential PAT-tools 6

7 NIRS for Process Monitoring Setup & Equipment Equipment FT-NIR spectrometer: Yokogawa NR800; transflectance probe Bioreactor: 14 L stainless steel stirred tank reactor Software Spectrometer control and spectra conversion: Spectland 2 (Yokogawa, Tokyo, Japan) Chemometric models: Solo (Eigenvector Research Inc., Wenatchee, WA, USA) Conversion (spectra + models): The Unscrambler 9.1.2a (Camo Software AS., Oslo, Norway) S. Buziol Evaluation of potential PAT-tools 7

8 NIRS for Process Monitoring Model Development / Calibration FT-NIR spectrometer bioreactor data acquisition sampling correlate preprocessed spectra with analyte concentration(s) sample ID time NIR spectra stamp (PLS) modeling model validaton analyte conc analytics model data of reference analytics S. Buziol Evaluation of potential PAT-tools 8

9 NIRS for Process Monitoring Results: Concentration of Glucose Glucose profile of the external, unseen validation batch is significantly different from that of the average batch used for calibration (intended altered feeding strategy to challenge the model). Prediction of glucose concentration (validation) in the course of a cell culture fermentation process (NIRS prediction vs Ion Chromatography) S. Buziol Evaluation of potential PAT-tools 9

10 NIRS for Process Monitoring Results: Concentration of Ammonia Offset of ammonia prediction in the final phase of the batch explainable by extrapolation of model: significant ammonia accumulation in validation batch in contrast to calibration batches due to intended altered feeding strategy. Prediction of ammonia concentration (validation) in the course of a cell culture fermentation process (NIRS prediction vs reference) S. Buziol Evaluation of potential PAT-tools 10

11 NIRS for Process Monitoring Results: Total Cell Density Cell density is directly proportional to the turbidity of the medium during initial growth phase. This relation is lost with increasing cell lysis at the start of the stationary phase. During the death phase the prediction seems adequate again. Thus, a more complex model is required to predict cell density. Prediction of total cell density (validation) in the course of a cell culture fermentation process (NIRS prediction vs reference) S. Buziol Evaluation of potential PAT-tools 11

12 Summary NIRS for Process Monitoring Monitoring of mammalian cell cultivations for monoclonal antibody production with inline near-infrared spectroscopy has potential since: accurate prediction of glucose concentrations on an external, unseen validation batch was possible prediction of ammonia in the external validation was adequate prediction of cell density was adequate but a more complex model was required prediction of lactate was adequate for the first phase of the fermentation process only Remarks: further data is required in order to confirm that the entire process variability is covered by the model before being used for closed loop process control robustness and transferability of chemometric models have to be verified S. Buziol Evaluation of potential PAT-tools 12

13 Acknowledgement Elena Stocker, Miriam Ahlert, Yvonne Lehmann, pilot plant team, Dr. Rainer Mueller, Dr. Josef Gabelsberger Roche Diagnostics GmbH, Pharma Biotech Production & Development, Penzberg, Germany João G. Henriques 4TUNE Engineering Ltd, Lisbon, Portugal Prof. José C. Menezes IBB-Institute for Biotechnology and Bioengineering, IST-Technical University of Lisbon, Lisbon, Portugal Dr. Arthur Voogd Yokogawa Europe BV, Amersfoort, The Netherlands S. Buziol Evaluation of potential PAT-tools 13

14 We Innovate Healthcare S. Buziol Evaluation of potential PAT-tools 14