13-Jun-14ASQ-FDC\FDA Conference Process Analytical Technology: What you need to know Frederick H. Long, Ph.D. President, Spectroscopic Solutions

13-Jun-14ASQ-FDC\FDA Conference Spectroscopic Solutions Consulting & Training –Process Analytical Technology –Spectroscopy –Statistics

13-Jun-14ASQ-FDC\FDA Conference Overview of PAT Design of Experiments/ Statistical Quality Control Process Analyzers Knowledge Management Multivariate Analysis

13-Jun-14ASQ-FDC\FDA Conference PAT Case Studies CSV of a Process Analyzer NIR Raw Material Library NIR In Process Control

13-Jun-14ASQ-FDC\FDA Conference CSV of a Process Analyzer Special issues –Field acceptance testing (FAT) –PAT Software –Training Issues GOOD NEWS Many vendors have compliant software !

13-Jun-14ASQ-FDC\FDA Conference 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

13-Jun-14ASQ-FDC\FDA Conference 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

13-Jun-14ASQ-FDC\FDA Conference Training Issues Operators find compliant software easy to use Password control issues Emergency procedures for a lost password

13-Jun-14ASQ-FDC\FDA Conference 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

13-Jun-14ASQ-FDC\FDA Conference 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

13-Jun-14ASQ-FDC\FDA Conference Investigate NIR Spectra Look for variation between vendors Two sources of pseudoephedrine Difference in particle size Moisture variation

13-Jun-14ASQ-FDC\FDA Conference Identification Method Development Use simplest (i.e. most robust) method Wavelength Correlation with 2 nd Derivative Treatment Normalized dot product of mean spectrum with test spectrum

13-Jun-14ASQ-FDC\FDA Conference Method Validation Strategy Internal Validation External Validation Challenge Samples Robustness Testing USP Chapter PASG, ICH. EMEA Guidelines

13-Jun-14ASQ-FDC\FDA Conference 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

13-Jun-14ASQ-FDC\FDA Conference NIR Spectra of Product

13-Jun-14ASQ-FDC\FDA Conference Calibration Development Collected NIR spectra and HPLC data from over the course of the previous year Samples collected to maximize range, approximately % of target 60 spectra used for Calibration equation For robustness, MLR model was desirable

13-Jun-14ASQ-FDC\FDA Conference Spectral Pre-Processing Use 2 nd derivative for pre-processing Minimize SEC for 1 term MLR by varying segment length SECSegment length (nm)

13-Jun-14ASQ-FDC\FDA Conference Calibration Models Both 3 and 4 term MLR models were constructed and gave good initial results

13-Jun-14ASQ-FDC\FDA Conference Pre-Validation Testing Used new product samples to validate equation Accuracy Precision Lot #3-term MLR accuracy 4-term MLR accuracy %99.9 % %98.8 % %101.7 % net %100.1 %

13-Jun-14ASQ-FDC\FDA Conference Engineering Study Examination of calibration robustness 5 Lots over 4 months

13-Jun-14ASQ-FDC\FDA Conference Equation Selection 3 term equation is more robust

13-Jun-14ASQ-FDC\FDA Conference Equation Validation Method Validation Criteria –Specificity –Range –Precision, Accuracy –Instrument Repeatability –Linearity –Robustness

13-Jun-14ASQ-FDC\FDA Conference Robustness Lot to Lot variation Operator variation

13-Jun-14ASQ-FDC\FDA Conference Multi-Vary Plot

13-Jun-14ASQ-FDC\FDA Conference Summary Clear plan, cross functional team Good validation strategy Detailed FAT and testing of chemometric models Need for sound understanding of chemometrics and statistics