Multi-site Performance Monitoring in Batch Pharmaceutical Production

1. Multi-site Performance Monitoring in Batch Pharmaceutical Production Chris Wong Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle

2. Presentation Structure • Manufacturing challenges facing the chemical and pharmaceutical industries • Process description • Batch performance monitoring overview • Statistical data analysis • Individual Principal Component Analysis (PCA) model • Combined PCA model – Removal of global mean • Combined PCA model – Removal of local mean • Multi-group PCA model • Conclusions and potential industrial impact

3. Manufacturing Challenges • Reduce time between product development and full-scale production • Achieve right-first-time production • Manufacture consistently high quality product with minimal environmental impact • Transfer product from one vessel to another, i.e. fingerprinting • Transfer process between sites

4. Information Knowledge Data Universities Industries Government Technology Transfer The Ultimate Goal

5. Aims • Understand and identify differences in process behaviour where a product is manufactured at two different sites • Remove impact of differences between site operations such as those relative to operational scale • Focus on within process variability One Multi-site Model versus Many Single Site Models

6. Process From Previous Stage Process To Next Stage Input Material + Reactant Product Process Measurements Product Quality Measurements Process Description • The process is a single stage within a multi-stage synthetic route for the production of an active pharmaceutical ingredient

7. Process Variables Site A 57 batches 5 process variables • Agitation Rate • Level Site B 152 batches 4 process variables • Vapour Temperature • 3 common process variables • Reactor Temperature • Reactor Pressure • Reactant Addition Rate

8. Batch Performance Monitoring • Multi-way unfolding (Nomikos and MacGregor, 1994) Time (K) Batches (I) Variables (J) V(1) V(2) V(J) Batches Time

9. Multi-way PCA Model • Apply PCA to the unfolded equalised batch data • Extract the principal component score vectors • Batch performance can be investigated Score vector ~ examining batch variation V(1) V(2) V(J) Batches Time Loading vector ~ examining process behaviour over time for different variables

10. Batch Length Equalisation • To apply multi-way PCA, batch lengths are required to be of equal duration • Two methods were implemented: • Multivariate Dynamic Time Warping (DTW) • A method to match features in a data pattern, or profile, to a reference profile • Cutting to minimum length • To include only the important period of operation for analysis

11. Batch Length Equalisation Initial cleaned data After applying DTW Temperature Pressure

12. Individual PCA Model – Site A Bivariate scores plot PC1 vs PC2 Bivariate scores plot after removal of batch 15 PC1 vs PC2

13. Individual PCA Model – Site A Level Univariate loadings plot PC1

14. Individual PCA Model – Site B Reactant Addition Rate Bivariate scores plot PC1 vs PC2 Contribution plot for batch 127

15. Site A Site A Site A Standardisation Combining 2 data matrices Site B Site B Site B Mean trajectory removed through entire column Removal of Global Mean

16. Removal of Global Mean Site A Global Mean Site B

17. Combined PCA Model – Removal of Global Mean Blue: Site A Black: Site B Bivariate scores plots PC1 vs PC2 PC3 vs PC4

18. Combined PCA Model – Removal of Global Mean Reactor Temperature Reactor Pressure Reactant Addition Rate Differential contribution plot

19. Combined PCA Model – Removal of Local Mean Site A Site A Site A Combining 2 data matrices Standardisation Site B Site B Site B Mean trajectory removed from entire column at individual site

20. Removal of Local Mean Site A Local Mean for Site A Local Mean for Site B Site B

21. Combined PCA Model – Removal of Local Mean Reactant Addition Rate Bivariate scores plot PC1 vs PC2 Univariate loadings plot PC1

22. Multi-group PCA Model • An extension to traditional multi-way PCA • Based on the assumption that a common eigenvector subspace exists for the sample variance-covariance matrix of individual sites • Through the pooled sample variance-covariance matrix (S), the principal component loadings are calculated

23. Multi-group PCA Model Site A Site A Site A s1 S Pool the covariance matrices from the two sites Calculate the covariance matrix Standardisation Site B Site B Site B s2

24. Multi-group PCA Model Bivariate scores plot, PC1 vs PC2

25. Multi-group PCA Model Site A Site B Common variables Univariate loadings plot, PC1

26. Multi-group PCA Model • Developed a single model for monitoring two different sites • Enable an enhanced understanding of the subtle differences between two sites • Minimise loss of information (all variables were retained) • Eliminate problems caused by operational scale • Help facilitate the transfer of a process to a new site

27. Conclusions • Different approaches to multi-site monitoring have been demonstrated by their application to data from a drug intermediate process • The capabilities of multi-group models were shown to have acceptable detection and diagnostic properties

28. Potential Industrial Impact • Faster knowledge transfer from R&D, pilot plant to full scale manufacture • Provision of model portability and transferability across different plants • Provision of tighter product specification and minimising quality differences between plants

29. Acknowledgments • Professor Elaine Martin and Professor Julian Morris • Mr. Richard Escott, GSK • Dr. John O’Shea and Dr. Chris Killen, GSK • Centre for Process Analytics and Control Technology • EPSRC • GlaxoSmithKline • The UK Overseas Research Students (ORS) Scheme