PAT AND APPLICATIONS Richard G Brereton Centre for Chemometrics University of Bristol

1. PAT AND APPLICATIONS Richard G Brereton Centre for Chemometrics University of Bristol r.g.brereton@bris.ac.uk Phone +44-117-9287658

2. Chemometrics and PAT • PAT tools • Some potential applications and their solutions. • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

3. CHEMOMETRICS AND PAT Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance U.S. Department of Health and Human Services Food and Drug Administration Pharmaceutical CGMPs September 2004 http://www.fda.gov/cder/guidance/6419nl.pdf Other countries are following, for example European pharmaceutical companies.

4. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

5. PAT TOOLS • Multivariate tools for design, data acquisition and analysis • Process analyzers • Process control tools • Continuous improvement and knowledge management tools

6. 1. MULTIVARIATE TOOLS • Achieved through the use of multivariate mathematical approaches, such as • statistical design of experiments, • response surface methodologies, • process simulation, and • pattern recognition tools, • in conjunction with knowledge management systems. • The applicability and reliability of knowledge in the form of mathematical relationships and models can be assessed by statistical evaluation of model predictions.

7. When used appropriately, these tools enable the identification and evaluation of product and process variables that may be critical to product quality and performance. The tools may also identify potential failure modes and mechanisms and quantify their effects on product quality.

8. 2. PROCESS ANALYZERS Multivariate methodologies are often necessary to extract critical process knowledge for real time control and quality assurance. Comprehensive statistical and risk analyses of the process are generally necessary. Sensor-based measurements can provide a useful process signature.

9. 3. PROCESS CONTROL TOOLS Develop mathematical relationships between product quality attributes and measurements of critical material and process attributes 4. CONTINUOUS IMPROVEMENT AND KNOWLEDGE MANAGEMENT Continuous learning through data collection and analysis over the life cycle of a product is important. Scientific understanding of the relevant multi-factorial relationships (e.g., between formulation, process, and quality attributes).

10. Many areas of PAT where chemometrics methods can be useful. Important to have an overall grasp of the potential of chemometrics methods. Many levels. How can it help? Then turn to the specialist. Hierarchy of users and developers of chemometrics methods.

11. PAT applications This takes advantage of many well established areas of chemometrics, especially in process monitoring and control. Over 20 years “theoretical” development, e.g. CPAC in Washington. After many years FDA have recognised this area. Classical chemometrics is being used to advantage. On-line spectroscopy has an important role in catalysing the need for chemometrics methods.

12. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

13. SOME POTENTIAL APPLICATIONS AND THEIR SOLUTIONS • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

14. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

15. ORIGINS OF TABLETS AS DETERMINED BY PYROLYSIS GCMS • Pharmaceutical tablets • Can we distinguish origin? • Patent protection law – illegal manufacturing.

16. Pyrolysis GCMS

17. Identification of peaks • Determine peak areas • Alignment • Selection of common peaks • Pattern recognition to determine class of unknown • Validation to determine how well the method works Peaks Group Samples

18. 21 data sets with known preparation ( WG and DC) Preprocessing Automatic deconvolution Construct a matrix by comparing spectra of different samples (21×636) Delete specific variables (21×21) Outlier detection using PC plot (20×18) First k score vectors from PCA (20× k ) ( k =1,2,..6) Fisher discriminant analysis New feature matrix k := k +1 k := k +1 (20×1) Fuzzy c -means classification Mahalanobis distance and QDA

19. Classification by a method called “Mahalanobis distance” Cross-validation important Autoprediction Sample Process Cross - validated Predicted (4PC) Predicted (6PC) Predicted (4PC) Predicted (6PC) 1 DC DC DC DC DC 3 DC DC WG DC DC 4 DC DC DC DC WG 5 DC DC DC DC DC 6 DC DC DC DC DC 7 DC DC DC DC DC 8 DC DC DC DC DC 9 DC DC DC DC DC 10 DC DC DC DC DC 11 DC DC DC DC DC 12 WG DC WG DC DC 13 WG WG WG WG WG 14 WG DC DC DC DC 15 WG WG WG WG WG 16 WG WG WG WG WG 17 WG WG WG DC DC 18 WG WG WG WG WG 19 WG WG WG WG WG 20 WG DC DC DC DC 21 WG WG WG DC DC 22 WG WG WG WG WG 3 3 Misclassified - 5 7

20. Prediction of DC is good. • Prediction of WG slightly less good. • Cross-validation is like a “blind test”, two methods are compared, the method witrh 4 PCs gives 5 erroneous results, all in WG • Good as an exploratory method • Normally this is useful for checking rogue samples, then invest more time and money in a second confirmatory phase.

21. SUPPORT VECTOR MACHINES : A NEW APPROACH

22. These are methods that have been used a lot in biology and economics, but much less in chemistry. • The models are non-linear. This is common in many applications, one cannot necessarily darw a straight line between two classes. • Compare to conventional methods.

23. AUTOPREDICTION

24. % samples correctly classified 100 Auto-prediction Cross-validation 80 DATASET 1 60 40 20 0 Stepwise DA SVM dot SVM radial SVM Stepwise DA SVM dot SVM radial SVM product basis polynomial product basis polynomial 100 80 DATASET 2 60 40 20 0 COMPARISON OF METHODS

25. What does this mean? Samples can be classified from their Pyrolysis GCMS fingerprint. So there is a unique underlying “signal” that allows classification. Different methods can be compared.

26. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

27. Tablet samples Manufacturer & Distributor G Manufacturer & Distributor I Manufacturer & Distributor C Is it possible to distinguish between the three sources G, I and C ? Is it possible to characterise each source ? Different synthetic routes generate different impurities may be at very low level may be not persistent Characterisation of route specific impurities by LCMS. Characterise by diagnostic ions Distinguish by CWIA

28. The main peaks in the GCMS are from the drug and the excipient, but the peaks that distinguish routes are very minor. • A consequence of the manufacturing route. • Can we distinguish the routes? • Can we find the ions that are significant?

29. Remove main peaks Select best masses

30. M/z = 955 MCQ = 0.231 M/z = 252 MCQ = 0.437 M/z = 207 MCQ = 0.844 The Component Detection Algorithm (CODA) The CODA algorithm assigns a quality index to each m/z value. Why necessary? Because only certain mass ions are useful. MCQ is higher when the profile is similar to its smoothed and mean-subtracted version. Spikes dissimilarly to smoothed version Background dissimilarity to mean-substracted version

31. The Component Detection Weighted Index of Analogy Flaws: • A different number and type of chromatograms can be selected for each sample, if many samples this can be problematic • It may be to difficult to find an optimal cut-off • Relevant information can still reside in the portion left out. Alternative: Alternative: Take all M/z into account but with an exponential Take all M/z into account but with an exponential weight. weight. Hence all masses in the chromatogram are used, but some are better than others.

32. CWIA for clustering q index CWIA Similarity of pairs Similarity matrix Clustering Improvements of CWIA • Tablets cluster according to origin • Replicates cluster at the earliest stages • Some heterogeneity still present (e.g. I1-I2).

33. CWIA for determining characteristic ions CWIA can be applied on the entire dataset considering a single ion each time Ions can be ranked according on how much they resemble a target similarity matrix

34. This application is slightly different to the first one. • When products of different quality (in this case forgeries) are manufactured, very small differences in manufacturing process. • This are indicated by very minor peaks in LCMS, the main peaks are the excipient and the drug. • Use this information to detect samples that come from different origins. • Find information about which m/z values are diagnostic for samples from different sources.

35. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

36. ON-LINE REACTION MONITORING Obtaining information in real-time. Important aspect of PAT: on-line processes and probes. Can obtain spectra as the reaction progresses, need to develop software and then can study processes, e.g. drying, when reactions reach end-points etc.

37. BORIS – reaction monitoring software • Developed for Glaxo Smith Kline • Aims : To develop software that can: - • Read in data from a variety of sources • Pre-process this data • Apply various chemometric methods to the data • Be extended or expanded at a later date and… • Do all this in real-time

38. MIR Data source Variable selection row-scaling standardisation Join datasets Curve resolution PCA MLR Graphical output Graphical output Save results to file Graphical output UV Data source

43. Very sophisticated control. Multi-instrument.

45. 0.6 0.4 0.2 0.0 - 0.2 - 0.4 - 0.6 - 0.8 - 1.0 0 20 40 60 80 100 time / min Scores plot shows build up of product and then crystallisation, can monitor process in real-time.

46. On-line software allows real-time process monitoring using spectroscopic probes. • When has a reaction gone to completion? Is a process being run for too long? • Monitoring of drying. • Monitoring crystallisation. • Are impurities or side reactions building up?

47. Best to obtain results in “real-time”, i.e. when the reaction is running, rather than later. • Costs of destroying batches. • Problems of validation and compliancy. • Problems of factory operators who do not understand chemometrics.

48. Chemometrics and PAT • PAT tools • Some potential applications and their solutions • Origins of tablets as determined by pyrolysis GCMS. • Characterisation of route specific impurities by LCMS. • On-line reaction monitoring. • Material Analysis. • Chromatographic Pattern Recognition.

49. MATERIALS ANALYSIS • Co-operation Triton Technology Ltd • Develop a low-cost polymer test and identification instrument – the Plastics Analyser. • Test and analyse commercial samples results using chemometrics techniques. • Build a material library with all the data acquired.

50. Change in phase as heated. • Thermal analyser. • Different patterns for different plastics. • Cost effective mass market product. £5,000 total kit. • Chemometrics has been slow to take off in rheology and materials analysis – new application area.