Date post: | 31-Mar-2015 |
Category: |
Documents |
Upload: | bryan-fulwood |
View: | 214 times |
Download: | 0 times |
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
Apr 11, 2023 ASQ-FDC\FDA Conference
Spectroscopic Solutions
• Consulting & Training– Process Analytical Technology– Spectroscopy– Statistics
Apr 11, 2023 ASQ-FDC\FDA Conference
Overview of PAT
• Design of Experiments/ Statistical Quality Control• Process Analyzers• Knowledge Management• Multivariate Analysis
Apr 11, 2023 ASQ-FDC\FDA Conference
PAT Case Studies
• CSV of a Process Analyzer• NIR Raw Material Library• NIR In Process Control
Apr 11, 2023 ASQ-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 !
Apr 11, 2023 ASQ-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
Precision of 1143 nm peak
0
0.005
0.01
0.015
0.02
0.025
1 3 5 7 9 11 13 15 17
run #p
reci
sio
n
old encoder
new encoder
specification limit
warm up
Apr 11, 2023 ASQ-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
Apr 11, 2023 ASQ-FDC\FDA Conference
Training Issues
• Operators find compliant software easy to use
• Password control issues
• Emergency procedures for a lost password
Apr 11, 2023 ASQ-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
Apr 11, 2023 ASQ-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
Apr 11, 2023 ASQ-FDC\FDA Conference
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
Ab
so
rba
nc
e
Apr 11, 2023 ASQ-FDC\FDA Conference
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
Apr 11, 2023 ASQ-FDC\FDA Conference
Method Validation Strategy
• Internal Validation• External Validation• Challenge Samples• Robustness Testing
• USP Chapter <1119>• PASG, ICH. EMEA Guidelines
Apr 11, 2023 ASQ-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
Apr 11, 2023 ASQ-FDC\FDA Conference
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)
Ab
so
rba
nc
e
Apr 11, 2023 ASQ-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 95 -105 % of target
• 60 spectra used for Calibration equation
• For robustness, MLR model was desirable
Apr 11, 2023 ASQ-FDC\FDA Conference
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
Apr 11, 2023 ASQ-FDC\FDA Conference
Calibration Models
• Both 3 and 4 term MLR models were constructed and gave good initial results
Apr 11, 2023 ASQ-FDC\FDA Conference
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 %
Apr 11, 2023 ASQ-FDC\FDA Conference
Engineering Study
• Examination of calibration robustness
• 5 Lots over 4 months
Apr 11, 2023 ASQ-FDC\FDA Conference
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
Apr 11, 2023 ASQ-FDC\FDA Conference
Equation Validation
• Method Validation Criteria– Specificity– Range– Precision, Accuracy– Instrument Repeatability– Linearity– Robustness
Apr 11, 2023 ASQ-FDC\FDA Conference
Robustness
• Lot to Lot variation
• Operator variation
Apr 11, 2023 ASQ-FDC\FDA Conference
Multi-Vary Plot
Diff
eren
ce-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
N O O M O M N O M O
A B C D E
LOT within Inspector
Std
Dev
0.00
0.05
0.10
0.15
N O O M O M N O M O
A B C D E
LOT within Inspector
Variability Chart for Difference
Apr 11, 2023 ASQ-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