Process Analytical Technology: What you need to know

1. Process Analytical Technology: What you need to know Frederick H. Long, Ph.D. President, Spectroscopic Solutions www.spectroscopicsolutions.com ASQ-FDC\FDA Conference

2. Spectroscopic Solutions • Consulting & Training • Process Analytical Technology • Spectroscopy • Statistics ASQ-FDC\FDA Conference

3. Overview of PAT • Design of Experiments/ Statistical Quality Control • Process Analyzers • Knowledge Management • Multivariate Analysis ASQ-FDC\FDA Conference

4. PAT Case Studies • CSV of a Process Analyzer • NIR Raw Material Library • NIR In Process Control ASQ-FDC\FDA Conference

5. CSV of a Process Analyzer • Special issues • Field acceptance testing (FAT) • PAT Software • Training Issues • GOOD NEWS Many vendors have compliant software ! ASQ-FDC\FDA Conference

6. Field Acceptance Testing • Upgraded hardware and software tested for improved operation • Encoder was found to be defective, was replaced • Done as part of engineering study ASQ-FDC\FDA Conference

7. PAT Software • Process Analyzer and PAT software often has statistical analysis capabilities such as control charts • It is good practice to document the accuracy of these calculations • Some NIST certified statistical data sets are available to further test calculations ASQ-FDC\FDA Conference

8. Training Issues • Operators find compliant software easy to use • Password control issues • Emergency procedures for a lost password ASQ-FDC\FDA Conference

9. NIR Raw Material Library • Seven Materials • Active 1, pseudoephedrine sulfate, monohydrate lactose, HPMC, corn product, sugar 1, sugar 2 • Selection criteria • Highest volume raw materials • Maximize impact ASQ-FDC\FDA Conference

10. Sample & Spectra Collection • Gather both file and recent samples • Collect samples from all vendors used • Use same sample presentation • 1” diameter scintillation vial • Collect spectra over different days • DOCUMENT, DOCUMENT, DOCUMENT ASQ-FDC\FDA Conference

11. Investigate NIR Spectra • Look for variation between vendors • Two sources of pseudoephedrine • Difference in particle size • Moisture variation ASQ-FDC\FDA Conference

12. Identification Method Development • Use simplest (i.e. most robust) method • Wavelength Correlation with 2nd Derivative Treatment • Normalized dot product of mean spectrum with test spectrum ASQ-FDC\FDA Conference

13. Method Validation Strategy • Internal Validation • External Validation • Challenge Samples • Robustness Testing • USP Chapter <1119> • PASG, ICH. EMEA Guidelines ASQ-FDC\FDA Conference

14. At-Line Process Control • Near IR used to measure active ingredient in pharmaceutical product • Results used to control process • Control Chart displayed in front of production machine • Used by all three production shifts ASQ-FDC\FDA Conference

15. NIR Spectra of Product ASQ-FDC\FDA Conference

16. Calibration Development • Collected NIR spectra and HPLC data from over the course of the previous year • Samples collected to maximize range, approximately 95 -105 % of target • 60 spectra used for Calibration equation • For robustness, MLR model was desirable ASQ-FDC\FDA Conference

17. Spectral Pre-Processing • Use 2nd derivative for pre-processing • Minimize SEC for 1 term MLR by varying segment length ASQ-FDC\FDA Conference

18. Calibration Models • Both 3 and 4 term MLR models were constructed and gave good initial results ASQ-FDC\FDA Conference

19. Pre-Validation Testing • Used new product samples to validate equation • Accuracy • Precision ASQ-FDC\FDA Conference

20. Engineering Study • Examination of calibration robustness • 5 Lots over 4 months ASQ-FDC\FDA Conference

21. Equation Selection 3 term equation is more robust ASQ-FDC\FDA Conference

22. Equation Validation • Method Validation Criteria • Specificity • Range • Precision, Accuracy • Instrument Repeatability • Linearity • Robustness ASQ-FDC\FDA Conference

23. Robustness • Lot to Lot variation • Operator variation ASQ-FDC\FDA Conference

24. Multi-Vary Plot ASQ-FDC\FDA Conference

25. Summary • Clear plan, cross functional team • Good validation strategy • Detailed FAT and testing of chemometric models • Need for sound understanding of chemometrics and statistics ASQ-FDC\FDA Conference