1 / 33

Impurity Monitoring in a Pharmaceutical Process

Impurity Monitoring in a Pharmaceutical Process. Dr Elaine Martin and Professor Julian Morris Centre for Process Analytics and Control Technology (CPACT) University of Newcastle. Overview of Presentation. Initial Analysis of Data from a Drug Intermediate Objectives Process Description

ingrid
Download Presentation

Impurity Monitoring in a Pharmaceutical Process

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Impurity Monitoring in a Pharmaceutical Process Dr Elaine Martin and Professor Julian Morris Centre for Process Analytics and Control Technology (CPACT) University of Newcastle

  2. Overview of Presentation • Initial Analysis of Data from a Drug Intermediate • Objectives • Process Description • Multivariate Statistical Process Performance Monitoring • Multi-way Principal Component Analysis • Generic Models • Conclusions

  3. Objectives of the Study • To understand the factors that have the greatest effect on impurity formation thereby enabling the Company to only analyse those batches that are close to the recommended operating levels. • To demonstrate the value of multivariate statistical techniques for data interrogation and process performance monitoring in batch pharmaceutical operations.

  4. Agitator Speed Reactants Jacket Outlet Temp Coolant Jacket Inlet Temp Refrigerant Flow Reaction Temp Schematic of Reactor Vessel

  5. Time Series Profiles Addition 2 Addition 1 Addition 2

  6. Process Description • Two additions are made during the batch. • Reactant A + slurry of ReactantB in ethyl acetate • ReactantC + above mixture • The reaction is exothermic.

  7. The Data • Twenty nine batches were made available. • Nine batches produced in a Stainless Steel reactor. • Twenty batches produced in a Hastelloy reactor. • The rationale for changing to the Hastelloy reactor was to increase the cooling capacity and reduce batch duration and impurity levels. • The final set of 10 Hastelloy batches were produced with a larger batch size, i.e. larger volumes of reactant A were added to larger volumes of reactant B. • The larger cooling capacity of the vessel however meant that these runs would not necessarily be longer.

  8. The Data • Five process variables - reaction temperature, refrigerant flow, jacket inlet temperature, jacket outlet temperature, agitator speed, are recorded on a 30 second basis. • Two quality parameters - yield and amount of impurity are monitored at the end of the batch. • According to the specifications, failed batches were identified as those that contained impurities greater than 0.14%. • However in reality a good batch is one that contains less than 0.1% impurity. • Longer addition times for stainless steel batches may contribute to higher impurity levels

  9. Impurity Levels Hastelloy Batches

  10. Limitations of Univariate Process Performance Monitoring 99% Action Limits 7 7 3 3 2 Univariate in-spec zone 2 Acceptable range for variable 1 5 5 6 6 1 1 4 4 Variable 1 1 2 3 Multivariate in-spec zone 4 5 6 Acceptable range for variable 2 7 Variable 2

  11. Multivariate Statistical Process Performance Monitoring • One possible approach to achieving a deeper understanding of the process has been through the application of multivariate statistical techniques. • Multivariate Statistical Process Performance Monitoring is based on the statistical projection techniques of Principal Component Analysis (PCA) and Projection to Latent Structures (PLS). • PCA monitors the process through a single block of information - the process or quality variables. • PLS monitors the process through a model of the quality variables / chemical information developed from the process information.

  12. K Time The Multiway approach 1 2J 3J J 1 JK Batches . . . . . . . I 1 J I Variables Multi-way Principal Component Analysis

  13. B Hastelloy Hastelloy A C StainlessSteel Results of Multi-way Principal Component Analysis

  14. Impurity Levels Batches 27, 28, 29

  15. Batch 29 - Variables 1 to 5 • Variable 3 : the main cause of separation of Hastelloy batches.

  16. Batches27, 28, 29 Variable 3 - Stirrer Speed • Higher stirrer speeds observed for batches 27-29.

  17. 14 25 Principal Component Analysis • PCA was applied to the 20 Hastelloy batches.

  18. Impurity Levels 25 (0.11%) 14 (0.09%)

  19. Variable contributions to PC2 Normal Batch Batch 14 Variable contributions to PC2 Abnormal Batch Batch 25 Scores Contribution Plot

  20. Hastelloy Batches 25 and 14 25 (0.11%) 25 (0.11%) 14 (0.09%) 14 (0.09%) Jacket Outlet Temperature Jacket Inlet Temperature Batch 25 (Solid line) Batch 14 ( Dotted line)

  21. Hastelloy Batches 25 and 14 25 (0.11%) 25 (0.11%) 14 (0.09%) 14 (0.09%) Jacket Coolant Flow Reactor Temperature Batch 25 (Solid line) Batch 14 (Dotted line)

  22. Interpretation of the Change in Process Operation • The jacket inlet temperature and jacket outlet temperature of batch 25 (0.11% impurity) starts to deviate from the trajectory of batch 14 during the first addition. • The reactor temperature for batch 25 is seen to be much less well controlled during the first addition. • For batch 14 (0.09% impurity), the reactor temperature is better controlled with a more constant jacket inlet temperature.

  23. Interpretation of the Change in Process Operation • A characteristic of batch operation is the closing of the coolant control valve at this time, allowing the reactor temperature to rise rapidly to its desired 21ºC. • Stopping the coolant flow results in the jacket coolant temperature measurements being unreliable during this period. • Following a period of erratic coolant flow conditions, good control is regained in batch 14 in contrast to that in batch 25. • This results in a clear difference in the responses of the jacket temperatures between the two batches in the subsequent four hour stir.

  24. Interpretation of the Change in Process Operation • During the stir, the reactor temperature of batch 25 is higher than the required desired 21ºC. • Discussions with plant personnel highlighted issues relating to other plant coolant demands and coolant control issues as possible causes for increased impurity levels.

  25. Multi-Group (Generic) Model • Products manufactured infrequently / small amounts. • Impractical to generate a model for every product type. • Method for monitoring a number of different processes using a single model. • Different product types. • Different ways of manufacturing a product.

  26. Generic Modelling • Weighted average of individual covariance matrices. • Covariance matrices of each group. • Pooled covariance matrix. • Common principal component loadings.

  27. Generic Modelling Batch 25 • Model generated from 20 Hastelloy batches (2 group model). • ‘Movement’ of high speed batches towards the centre. Batch 26

  28. Impurity Levels 25 (0.11%) 26 (0.07%)

  29. Scores Contribution Plots • Tracing the cause of ‘abnormal’ batches 25 (impurity level 0.11%) and 26 (impurity level 0.07%) from the contributions to PC2. • Variables 1 and 2 have the highest contributions.

  30. Cause of Abnormality Batch 25 - impurity level 0.11% Batch 26 - impurity level 0.07% Batch 25 Batch 26 Batch 25 Batch 26 Jacket Inlet Temperature Jacket Outlet Temperature

  31. Interpretation of the Change in Process Operation • A deviation in the jacket inlet temperature, for batch 25, can be seen during the first addition. • Also despite the inlet temperature being reasonably well controlled until the second addition, the jacket outlet temperature for batch 25 drifts upwards. • Following the second addition, the jacket temperatures are kept much lower for the low impurity batch (batch 26). • The multi-group study confirmed the previous findings using standard multi-way PCA and data augmentation.

  32. Conclusions • Multi-way principal component analysis was capable of identifying batches with ‘high’ and ‘low’ levels of impurity. • Poor reactor temperature control during the first addition appears to lead to increased impurity levels. In addition poor regulation at the end of the batch also results in temperature offsets. • Generic modelling helped remove differences caused by different reactor speeds. • The applicability and power of multivariate statistical techniques have been clearly demonstrated.

  33. Acknowledgements • Lane, S., Martin, E. B., Kooijmans, R. and Morris, A. J. (2001) “Performance Monitoring of a Multi-product Semi-Batch Process” Journal of Process Control, 11, 1-11. • Conlin, A., Martin, E. B. and Morris, A. J. (1998), “Data Augmentation: An Alternative Approach to the Analysis of Spectroscopic Data”, Chemometrics and Intelligent Lab. Systems, 44, 161-173. • J. Murtagh - Background process information and data. • Dr. Y. Bissesseur, CPACT Research Associate and Ms. C. Mueller, MEng Student

More Related