13-Jun-14ASQ-FDC\FDA Conference Process Analytical Technology: What you need to know Frederick H....

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Apr 11, 2023 ASQ-FDC\FDA Conference

Process Analytical Technology: What you need to know

Frederick H. Long, Ph.D.

President, Spectroscopic Solutionswww.spectroscopicsolutions.com

Spectroscopic Solutions

• Consulting & Training– Process Analytical Technology– Spectroscopy– Statistics

Overview of PAT

• Design of Experiments/ Statistical Quality Control• Process Analyzers• Knowledge Management• Multivariate Analysis

PAT Case Studies

• CSV of a Process Analyzer• NIR Raw Material Library• NIR In Process Control

CSV of a Process Analyzer

• Special issues– Field acceptance testing (FAT)– PAT Software– Training Issues

• GOOD NEWS Many vendors have compliant software !

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

Precision of 1143 nm peak

1 3 5 7 9 11 13 15 17

run #p

old encoder

new encoder

specification limit

warm up

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

Training Issues

• Operators find compliant software easy to use

• Password control issues

• Emergency procedures for a lost password

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

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

Investigate NIR Spectra

• Look for variation between

vendors

• Two sources of pseudoephedrine

• Difference in particle size

• Moisture variation1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500

-0.0002

0.0696

0.1394

0.2093

0.2791

0.3489

0.4187

0.4885

0.5583

0.6281

0.6979

Pseudoephedrineblue 10588502red 23037602

Wavelength

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

Method Validation Strategy

• Internal Validation• External Validation• Challenge Samples• Robustness Testing

• USP Chapter <1119>• PASG, ICH. EMEA Guidelines

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

NIR Spectra of Product

1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 25000.0885

0.2157

0.3430

0.4703

0.5976

0.7249

0.8522

0.9795

1.1068

1.2340

1.3613

Wavelength (nm)

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

Spectral Pre-Processing

• Use 2nd derivative for pre-processing• Minimize SEC for 1 term MLR by varying segment length

SEC Segment length (nm)

0.0908 10

0.0848 16

0.0835 20

0.0848 24

0.0856 30

Calibration Models

• Both 3 and 4 term MLR models were constructed and gave good initial results

Pre-Validation Testing

• Used new product samples to validate equation

• Accuracy

• Precision

Lot # 3-term MLR accuracy

4-term MLR accuracy

1 100.1 % 99.9 %

2 99.2 % 98.8 %

3 102.2 % 101.7 %

net 100.5 % 100.1 %

Engineering Study

• Examination of calibration robustness

• 5 Lots over 4 months

Equation Selection

SUMMARY of Engineering Study Results

DATE LOT # EQ accuracy % precision % RSDD 3 term 99.1 1.8D 4 term 98.4 2F 3 term 100.1 1.6F 4 term 99.4 1.6G 3 term 98 1.8G 4 term 97.6 1.8H 3 term 98.4 1.7H 4 term 97.9 2.1I 3 term 98.5 1.4I 4 term 97.6 1.5

NET ACCURACY 3 term 98.84 term 98.2

3 term equation is more robust

Equation Validation

• Method Validation Criteria– Specificity– Range– Precision, Accuracy– Instrument Repeatability– Linearity– Robustness

Robustness

• Lot to Lot variation

• Operator variation

Multi-Vary Plot

ce-0.15

N O O M O M N O M O

A B C D E

LOT within Inspector

N O O M O M N O M O

A B C D E

LOT within Inspector

Variability Chart for Difference

Summary

• Clear plan, cross functional team

• Good validation strategy

• Detailed FAT and testing of chemometric models

• Need for sound understanding of chemometrics and statistics

13-Jun-14ASQ-FDC\FDA Conference Process Analytical Technology: What you need to know Frederick H....

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