Process Analytical Technology:
What you need to know
Frederick H. Long, Ph.D.
President, Spectroscopic Solutions
www.spectroscopicsolutions.com
22-Mar-16
ASQ-FDC\FDA Conference
Spectroscopic Solutions
• Consulting & Training
– Process Analytical Technology
– Spectroscopy
– Statistics
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ASQ-FDC\FDA Conference
Overview of PAT
•
•
•
•
Design of Experiments/ Statistical Quality Control
Process Analyzers
Knowledge Management
Multivariate Analysis
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PAT Case Studies
• CSV of a Process Analyzer
• NIR Raw Material Library
• NIR In Process Control
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CSV of a Process Analyzer
• Special issues
– Field acceptance testing (FAT)
– PAT Software
– Training Issues
• GOOD NEWS Many vendors have compliant
software !
22-Mar-16
ASQ-FDC\FDA Conference
Field Acceptance Testing
• Upgraded hardware and
software tested for improved
operation
Precision of 1143 nm peak
0.025
• Encoder was found to be
defective, was replaced
precision
0.02
specification limit
0.015
1
3
5
7
9
0.01
0.005
0
• Done as part of engineering
study
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warm up
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run #
11
13
15
17
old encoder
new encoder
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
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Training Issues
• Operators find compliant software easy to use
• Password control issues
• Emergency procedures for a lost password
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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
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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
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ASQ-FDC\FDA Conference
Investigate NIR Spectra
• Look for variation between
vendors
• Two sources of
pseudoephedrine
Pseudoephedrine
blue 10588502
red 23037602
0.6979
0.6281
0.5583
• Difference in particle size
Absorbance
0.4885
0.4187
0.3489
0.2791
0.2093
• Moisture variation
0.1394
0.0696
-0.0002
1100
1200
1300
1400
1500
1600
1700
1800
1900
Wavelength
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2000
2100
2200
2300
2400
2500
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
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ASQ-FDC\FDA Conference
Method Validation Strategy
•
•
•
•
Internal Validation
External Validation
Challenge Samples
Robustness Testing
• USP Chapter <1119>
• PASG, ICH. EMEA Guidelines
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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
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NIR Spectra of Product
1.3613
1.2340
1.1068
0.9795
Absorbance
0.8522
0.7249
0.5976
0.4703
0.3430
0.2157
0.0885
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Wavelength (nm)
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2100
2200
2300
2400
2500
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
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Spectral Pre-Processing
• Use 2nd derivative for pre-processing
• Minimize SEC for 1 term MLR by varying segment length
SEC
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Segment length (nm)
0.0908
10
0.0848
16
0.0835
20
0.0848
24
0.0856
30
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Calibration Models
• Both 3 and 4 term MLR models were constructed and
gave good initial results
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Pre-Validation Testing
•
•
Used new product samples to validate
equation
Accuracy
•
Precision
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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 %
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Engineering Study
• Examination of calibration robustness
• 5 Lots over 4 months
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Equation Selection
SUMMARY of Engineering Study Results
DATE
LOT #
D
D
F
F
G
G
H
H
I
I
EQ
3 term
4 term
3 term
4 term
3 term
4 term
3 term
4 term
3 term
4 term
NET ACCURACY
3 term
4 term
accuracy % precision % RSD
99.1
1.8
98.4
2
100.1
1.6
99.4
1.6
98
1.8
97.6
1.8
98.4
1.7
97.9
2.1
98.5
1.4
97.6
1.5
98.8
98.2
3 term equation is more robust
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Equation Validation
• Method Validation Criteria
– Specificity
– Range
– Precision, Accuracy
– Instrument Repeatability
– Linearity
– Robustness
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Robustness
• Lot to Lot variation
• Operator variation
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Multi-Vary Plot
Variability Chart for Difference
0.30
0.25
Difference
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
N
O
A
O
M
B
O
M
C
N
O
M
D
O
E
LOT w ithin Inspector
Std Dev
0.15
0.10
0.05
0.00
N
O
A
O
B
M
O
C
M
N
O
M
D
LOT w ithin Inspector
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O
E
Summary
• Clear plan, cross functional team
• Good validation strategy
• Detailed FAT and testing of chemometric models
• Need for sound understanding of chemometrics and
statistics
22-Mar-16
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