PAT/QbD: Getting direction from other industries

Transcription

1 PAT/QbD: Getting direction from other industries IFPAC Cortona 2010

2 Poll of Process Users 1. Analytical failure prediction 2. Result validation 3. More process-specific information (timely, higher quality, more focused) 4. Simplification of procedures 5. Elimination of analytical discrepancies 6. Reduction in the lifecycle cost (cost of ownership) Driven in part by dwindling manpower, skill sets and capabilities! 2

3 The key is multivariate instrumentation You can t control what you don't measure All measurements are inferentials Multiple measurements allow tighter specifications multivariate instruments Timeliness is key Turnaround speed of the instrument On-line versus near-line versus off-line Elimination of the analyst Real-Time Release real time data analysis and automated, application-specific interpretation 3

4 Chemometrics for instrumentation: the value proposition Anything you can do to improve precision of the multivariate measurements collected by the instrument will allow you to tighten the control essentially for free. One way is to construct an application-specific, objective evaluation system: Experimental design Exploratory data analysis Leading to Multivariate modeling (qualitative and quantitative analysis) Just as key is the signal processing aspect of chemometrics to reduce instrument-derived variability Within an instrument (e.g., noise reduction) Between instruments (i.e., transfer of calibration) 4

5 Chemometrics for instrumentation: the value proposition All this results in the ability to make the most of the data you are collecting and enables Continuous validation of the instrument, possibly the entire process A vast improvement in the ability to automatically interpret the stream of data, leading to better feed-back and feed-forward control A better ability to maintain the process, the instrument and the multivariate model. Software Goal: continuous validation of the system performance and guarantee the quality of the data. 5

6 Chemometrics: The Role in Separation Science Brian Rohrback September 22, 2010 IFPAC Cortona Italy

7 Interpretation Data, data and more data Human qualities Good at seeing small differences Bad at quantifying small differences Fair at recollection Poor at seeing patterns in tables of numbers Objectives Quantify a property or attribute? Characterize the sample? 7

8 Delivering Information Just having the measurements does not translate into control Remember, there are not enough skilled technicians to handle even the current workload. Chemometrics solves the information processing problem with 2 technologies: Alignment enables us to sell instruments that have vastly-lower calibration requirements. Interpretation algorithms automates the generation and the qualification of the information derived from the raw data. 8

9 Raw process chromatograms Full Data;2 0.2 Response Time Index (E +03) 9

10 The same chromatograms after alignment Full Data;2 0.2 Response Time Index (E +03) 10

11 Gating Problem 11

12 Gating Problem Solved 12

13 NMR Spectra 1 H NMR in the Aromatic Region 13

14 NMR Spectra - Aligned 1 H NMR in the Aromatic Region 14

15 The data to information transition 15

16 QC of x-ray contrast agents Original HPLC data Aligned HPLC data 16

17 QC of x-ray contrast agents 17

18 QC of x-ray contrast agents 18

19 Amino acid analysis 19

20 PCA as a basis for interpretation PC2 PC3 PC1 20

21 PCA as a basis for interpretation PC2 PC3 PC1 21

22 Chromatographic Alignment 3 instruments Time (seconds) Raw data 22

23 Chromatographic Alignment 3 instruments Time (seconds) Auto-Aligned 23

24 Automated alignment works 5 year period, 6 GCs 24

25 Automated alignment works 5 year period, 6 GCs aligned 25

26 On-line Simulated Distillation 400 Samples Un-Aligned Siemens Maxum II Same Samples Aligned 26

27 Comparison of PCA scores Before alignment After alignment 85% of all of the variation in the raw data is due to the misaligned peaks. Correcting for this shows us that there are three different production regimes in these data. 27

28 Distillation Chromatograms Unaligned Chromatograms % of Signal RetentionTime, min Aligned Chromatograms 28

29 Overlay of Yield Curves 100 Average Yield Curve Comparison BP Aligned Yield, wt % Boiling Point, deg. F 29

30 Chemometric modeling of results tables 95% confidence interval Composite report Total by group type & carbon number (in volume Composite percent) report Total by group n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: C1: (in volume Composite percent) report Total by group C2: n-paraffins: type & carbon 0 i-paraffins: number 0 Olefins: 0 Naphtenes: 0 Aromatics: 0 OxygenatesTotal: 0 0 C3: C1: (in volume Composite percent) report Total by group C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: 0 Aromatics: 0 0 OxygenatesTotal: C5: C3: C1: (in volume Composite percent) report Total by group C6: C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: C7: C5: C3: C1: (in volume Composite percent) report Total by group C8: C6: C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: C9: C7: C5: C3: C1: (in volume Composite percent) report Total by group C10: C8: C6: C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: C11: C9: C7: C5: C3: C1: (in volume Composite percent) report Total by group C12: C10: C8: C6: C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: C13: C11: C9: C7: C5: C3: C1: (in volume Composite percent) report Total by group C14: C12: C10: C8: C6: C4: C2: n-paraffins: type & carbon i-paraffins: number Olefins: Naphtenes: Aromatics: OxygenatesTotal: Total: C13: C11: C9: C7: C5: C3: C1: (in volume percent) Total C14+: C14: C12: C10: C8: C6: C4: C2: n-paraffins: i-paraffins: Olefins: Naphtenes: Aromatics: OxygenatesTotal: Total unknowns: Total: C13: C11: C9: C7: C5: C3: C1: Grand Total total C14+: C14: C12: C10: C8: C6: C4: C2: Total unknowns: Total: C13: C11: C9: C7: C5: C3: Grand Total total C14+: C14: C12: C10: C8: C6: C4: Total unknowns: Total: C13: C11: C9: C7: C5: Grand Total total C14+: C14: C12: C10: C8: C6: Total unknowns: Total: C13: C11: C9: C7: Grand Total total C14+: C14: C12: C10: C8: Total unknowns: Total: C13: C11: C9: Grand Total total C14+: C14: C12: C10: Total unknowns: Total: C13: C11: Grand Total total C14+: C14: C12: Total unknowns: Total: C13: Grand Total total C14+: C14: Total unknowns: Total: Grand Total total C14+: Total unknowns: Grand total

31 Chemometric evaluation of the results tables for new samples Outliers, because of: Instrument problem? Process upset? 31

32 54 samples of M. intracellulare (1 lab) 32

33 38 samples of M. simiae (5 labs) 33

34 Variation in M. asiaticum 34

35 PCA Scores Plot After Alignment Samples of the same species cluster, some species to a greater extent than others. Also, species known to be similar express similar chromatographic profiles and cluster near each other in this factor space. 35

36 Use of decision points, hierarchical models Journal of Chromatographic Science vol

37 Continuous validation of a multivariate instrument We can correct retention times to match an applicationspecific relevant sample This eliminates the transfer of calibration problem in chromatography Common regression and classification algorithms can be applied automatically to infer physical properties or characteristics This allows us to bring more complex analyses into on-line use 37