Chemical Imaging of Pharmaceutical Compacts
Carl Anderson, Ph.D. Assistant Professor of Pharmaceutical Sciences Duquesne University
Areas of Interest • Monitoring and Understanding the Chemistry and Physics of Pharmaceutical Manufacturing – Process analytical technology • • • • •
Chemical synthesis Blending Drying Encapsulation and Tableting Packaging (i.e. Blister packs)
• Efficient analytical methods • Validation of non-traditional analytical methods
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Outline • Implementation of technology in pharmaceutical industry • Chemical Imaging • Chemical Imaging of Compacts • Preparation of a set of compacts • Development of a quantitative model
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Implementation of Analytical Technology in Pharmaceutical Industry • • • • • • •
Need for a new or better measurement Identification of appropriate technology Proof of concept Acquisition of $$, equipment and knowledge Qualification of instrumentation Method development and validation Implementation of technology, procedures and documentation – Calibration, model verification, 21 CFR 11 compliance, etc.
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Chemical Imaging - General • Data for a chemical image – Spatial information – Spectroscopic (chemical) information
• Chemical imaging by IR, RAMAN, EDX, NIR and other
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Data Structure for a Chemical Image: Hyper-Spectral Data Cube Dimensions yield: •Chemical information (spectra) •Spatial information
S
m ru t c pe
at
el x Pi
1
One pixel hν N . . .
Frame 1 Image at hν 1
. hν 3 hν 2 hν 1
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Chemical Imaging - General • Data evaluation and analysis – Data collection time – Image analysis (qualitative assessment) • Feature contrast only
– Quantitative information • Spectral data translated to chemical information
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Chemical Imaging - General • Technology of choice for collection of spectral data – NIR – Fast (compared to IR or RAMAN imaging) – Rich in chemical and physical information – Demonstrated potential for reliable quantitative calibration
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NIR Chemical Imaging Equipment InGaAs Camera
The Condor by Chemicon LCT Filter Lens Light path
Illumination
Sample Stage 9
Demonstration of Potential Applications of NIR Chemical Imaging • Generation of a quantitative calibration to predict local concentrations in an image
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Preparation of Compacts • 11 blends of salicylic acid (SA) and lactose monohydrate – 0, 5, 10, . . . 50% SA
• Blend uniformity was verified by NIR prior to compact formation • Compacts – 13 mm diameter, cylindrical (~3 mm deep), flat upper and lower surface – 500 mg blend/compact – Carver Autopellet • 4000 pounds force, 10 seconds 11
Preparation of Compacts (cont’d) • Two types of compacts were prepared – Compacts were prepared from each blend – Compacts were prepared as 50% composites of two blends Sample Name
Content Side A
Comp A Lactose only
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Content Side B
Net Concentration
Change in Concentration
50% SA 50% Lac
25% SA
50% SA
Comp B
10% SA 90% Lac
35% SA 65% Lac
22.5% SA
25% SA
Comp C
15% SA 85% Lac
25% SA 75% Lac
20% SA
10% SA
Experimental Parameters • Image 320 X 240 pixels • Spectal information – 121 points – 1100 nm - 1700 nm (9091 cm-1 - 5882 cm-1) – Spacing = 5 nm
• Data collection time ca 1 minute
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Data Pre-Processing • Median filter • Pixel average – 1 iteration
• 2nd derivative – 5 pt window, S-G, 2nd order polynomial
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Averaging Across Pixels • Mask is passed through each frame (or image) N times • Advantages – Reduces noise – Clarifies larger features
• Disadvantages – Blurs sharp features
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1
1
1
1
1
1
1
1
1
Original Image
1 Iteration Average
2 Iteration Average
3 Iteration Average
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Original Image
1 Iteration Average
Intensity = 0.2037 St Dev = 0.0382
Intensity = 0.2035 St Dev = 0.0142
Intensity @ 1660 nm 2 Iteration Average
3 Iteration Average
Intensity = 0.2035 St Dev = 0.0108
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Intensity = 0.2037 St Dev = 0.0095
PLS Prediction – Quantitative Model • Using 10, 20, 30, 40 and 50% SA Predict SA image for 0, 5, 15, 25, 35, 45% compacts • Model uses 2 factors • Generated from mean spectra of processed images – 7 samples per image – ~100 pixels/sample 18
Compacts Used for Developing a Model
10% SA
30% SA 19
20% SA
40% SA
50% SA
Spectra Used to Build SA Quantitative Model 0.01
2nd Der Intensity
0.01
0.00
-0.01
0% SA – 10% SA – 20% SA – 30% SA – 40% SA – 50% SA –
-0.01
-0.02 1100
1200
1300
1400 Wavelength (nm)
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1500
1600
1700
Model to Predict SA Concentration % Salicylic Acid Measured
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0.969 Slope = Intercept = 0.735 0.9998 R= RMSEC = 0.279 0.044 Bias =
50 40 30 20 10 0 0
10
20
30
40
50
% Salicylic Acid Reference 21
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Model Residuals 0.4
% SA Model Residual
0.3 0.2 0.1 0 -0.1
0
10
20
30
-0.2 -0.3 -0.4
Nominal % SA
22
40
50
60
Testing the Model to Predict SA Concentration % Salicylic Acid Measured
50
40
30
RMSEC = RMSEP Bias =
20
10
0 0
10
20
30
40
% Salicylic Acid Reference 23
50
1.96 0.38
Pixel Image
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Sample Image – 1375 nm
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Sample Image – 1660 nm
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Data Reduction Illustrated NIR Spectrum (Pixel 1)
SA (P Con ixe ten l1 t )
PLS Model hν N . . . . hν 3 hν 2 hν 1
Many images Pixel Intensity = NIR reading at a singe hυ 27
One image Pixel Intensity = SA Content
Sample Image After PLS Processing
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Acknowledgements • ChemImage (formerly, Chemicon) – Matt Nelson, Ph.D. – Laura Grudowski
• James K. Drennen III, Ph.D. • Perkin-Elmer Instruments
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