Perspectives in Chemometrics

1. Perspectives in Chemometrics Experience form GlaxoSmithKline

2. Where are we starting from • Fortunately, not from scratch • ASTM E1655-97 • “Standard Practices for Infrared, Multivariate, Quantitative Analysis” • USP Chapter on the use of NIR • scheduled for the Second Supplement • issue data June 2002

3. “When provided with identical information, statistical procedures achieve greater empirical accuracy than do professional. This remains true when one provides professionals with information not available to the statistical procedure, …” Dawes, Faust and Meehl, Science, 243:1668-167, 1989

4. First things: • We need a clear definition of what chemometrics encompasses • Does MLR constitute chemometrics? • Is this strictly for higher order techniques such as PLS, PCR, ….? • Are we approaching this as a data independent study? • Do we need to consider the source of data?

5. General classes of chemometric methods • On-line determination of composition • Pattern recognition and classification techniques • Multivariate statistical process control

6. ASTM E1655-97 • Arises from the petrochemical industry • Specifically addresses issues around IR though does mention NIR • Defines the term “multivariate mathematical technique” to be all inclusive • Defines many terms that we can reference

7. What separates the ASTM document from the needs of the pharma industry? • ASTN document describes methods for processes that run continuously • Pharma companies typically run in batch mode • Pharma companies often do not have the volume of batches to meet this requirement

8. What separates the ASTM document from the needs of the pharma industry? • A large sample set is required that spans between 3 to 5 SD of all constituents • Generating these OOS samples is difficult as they should be prepared using the same equipment as used in the process. For the pharma companies this represents large $$$ • If PAT is to be used upon product launch, the amount of active ingredient required may exceed the ROI

9. <1119> USP chapter:Near-infrared Spectrophotometry • In process of revision for a large number of years • Defines terms for both reflectance and transmittance • Defines PQ/IQ frequency • Wavelength standard (NIST SRM 1920) for reflectance only • Only refers to MSC, no mention of chemometric techniques for data analysis

10. What technologies have been or may be used for PAT? • Focus on spectroscopic techniques • Offers the advantage of bring the measurement system to the sample • UV/vis • Well understood technology, USP guidance • Spectra tend to be highly overlapped due to the broad nature of the absorbance - low specificity • UV/vis will rely heavily on chemometrics • Commercial and validatable hardware/software available

11. IR • Well understood • Spectra have high specificity • Difficulties making truly 0n-line measurements • Commercial hardware available but software not written to be validated • Raman • Not well understood by manufacturing groups • Safety concerns • Spectra have high specificity • Commercial hardware available but software not written to be validated

12. NIR • Reasonably well understood technology, USP guidance soon • Spectra tend to be somewhat overlapped • NIR will rely on chemometrics • Commercial and validatable hardware/software available • Technology over-sold to the industry, still facing “bad taste”

13. So, what steps do we need to take to ensure success? • First, and foremost, we must ensure that we are doing good science • This will require that any candidate processes for PAT/chemometrics be well understood • This in turn will require a rigorous calibration effort with real process samples and generation of data from referee methods • This will take a considerable amount of time and effort - does the ROI exist?

14. ... success? (cont.) • Are we targeting existing processes or new processes/product? • The former has the advantage of being an established, validated process • The latter may be easier to generate required sample sets

15. On-line or at-line determination of composition issues • Calibration • Maintenance of calibration • Sampling issues • Software issues • Process control

16. Calibration • Will require a large number of batches • These will need to include out of specification (OOS) batches to properly span the desired range • Who will generate these? • Cost, especially if a new product? • Will they be generated on the actual production equipment? If not, are they valid? • What group within the company performs the validation?

17. Maintenance of calibration • How often must the calibration be checked? • Daily suitability performed with some reference material? • Does it depend on what type of measurement? • If the method is fiber optic based, does the probe need to be removed for this test? • For example: NIR for octane in motor fuel - need daily check with verification from lab testing also

18. Maintenance of calibration (cont.) • What if the check reveals an OOS result? • Does this shut the process down? • Does it bring into question the previous results? • Again, who is responsible for the check?

19. Pattern recognition / classification techniques • Identify & assess quality of raw materials & products • Develop a library of spectra for acceptable lots • Develop a multivariate statistical model of the library • Compare future samples to predict identity & quality • Demonstrate sensitivity to known / expected impurities, degradation products and foreign materials. • Up-front investment in calibration is avoided • On-going calibration maintenance costs are avoided.

20. Multivariate statistical process control • Develop a statistical model of an existing process • Use rapid, low-cost on-line or in-situ spectroscopic measurements. • Uses multivariate statistics / chemometrics to characterize the processes from relevant, sensitive measurements. • Control limits derived from sound statistical practice and historical database of measurements. • Up-front investment in calibration is avoided • On-going calibration maintenance costs are avoided.

21. Multivariate statistical process control (cont) • Statistical characterization of a process is superior to unaided human judgment • Multivarite statistics are extremely effective tools for detecting correlation amidst significant noise. • Probabilistic relationships are more readily obtained and verifiable than causal understanding • Methodical “mechanical” approach is more thorough compared to heuristics and intuition • Potential issues • still in a research state • volume: does pharma have the number of batches to do this

22. Sampling issues • How is the sample measured? • Is the process sample collected the same way the validation data was collected • If it is a fiber based measurement, what if the probe/fiber break? • Are there issues of probe fouling? • Are there issues of sample presentation • This could be an issue for solids or turbid samples • Is particle size an issue

23. Sampling issues (cont.) • Are there environmental issues that need to be considered? • Summer/winter? Dry/humid? • Source of raw materials?

24. Software issues • Who does the burden of validation fall on? • Vendor: can provide a validation package but is this enough • End user: What degree of testing is required? • Do we need to ensure 21CFR 11 compliance • Vendors are aware of these issue and some have begun to address it • Bomem: process FT-NIR software Enablir • SpectrAllaince: process UV/vis software NovaPack

25. Software issues (cont.) • What of some current software packages • GRAMS/IQ: expecting release 8 • Matlab: doubtful that it could be validated but useful for development • LabView: doubtful that it could be validated but useful for development

26. Process control • Now that we have all of these tools in place, what can we do with the information? • Can we make process variations based on the data from the PAT? • These are validated processes • If a change is warranted, does this imply that the process was out of control? • Or, do we use this information to trigger a manual sampling?

27. For example • Dryer monitoring • Measuring the effluent from an oven • Looking at solvent vapors coming off product • Reasonable clean sample stream • Saw slight deposition of material on optics • Using PLS to model multiple gasses when appropriate • Data used to signal manual sampling and off-line testing

28. What was learned? • Not going to be used as final release of material • Manufacturing is conservative • Using chemometrics requires training local staff • Manufacturing sites often do not have the technical expertise • Anything beyond linear regression was initially confusing • Calibrations generated off-site were not accepted • Assurance at local site of validity of calibration • Methodology for generating calibration was used

29. What was learned? (cont.) • Need to access instrument manufacturer support world-wide • This includes software support • Validation not required as not used for release

30. What can ease this in the future? • Advanced training of staff • Internal/external options • Easier to use software • Reliance on vendors to provide this • Validation of software (vendor) • 21CFR11 compliance • Guideline for chemometrics • What analysis technique is appropriate • How to chose the correct number of factors

31. What are other issues/approaches? • Can pattern recognition be used • Based on historical data can the process be monitored • Need enough history to account for all possible conditions • Can consortia help with some of these issues • CPAC, MCEC, CPACT • Regulatory approval of new approaches • current is causal - understand every aspect via conventional mean/techniques • probabilistic - compare good batches to in-situ measurements to develop history