Post on 21-Mar-2016
description
Chemical Imaging of Pharmaceutical Compacts
Carl Anderson, Ph.D.Assistant Professor of Pharmaceutical Sciences
Duquesne University
Areas of Interest
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• 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
3
Outline
• Implementation of technology in pharmaceutical industry
• Chemical Imaging• Chemical Imaging of Compacts• Preparation of a set of compacts• Development of a quantitative model
Implementation of Analytical Technology in Pharmaceutical Industry
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• 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.
Chemical Imaging - General
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• Data for a chemical image– Spatial information– Spectroscopic (chemical) information
• Chemical imaging by IR, RAMAN, EDX, NIR and other
Data Structure for a Chemical Image:Hyper-Spectral Data Cube
6
Frame 1Image at hν 1
hν 1
hν 2
hν 3
hν N
.
.
.
.
One pixel
Spectr
um at
Pixel 1
Dimensions yield:•Chemical information(spectra)•Spatial information
Chemical Imaging - General
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• Data evaluation and analysis– Data collection time – Image analysis (qualitative assessment)
• Feature contrast only
– Quantitative information• Spectral data translated to chemical information
Chemical Imaging - General
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• 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
NIR Chemical Imaging Equipment
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The Condor by Chemicon
InGaAs Camera
LCT Filter
Lens
Illumination
Stage
Sample
Light path
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Demonstration of Potential Applications of NIR Chemical Imaging
• Generation of a quantitative calibration to predict local concentrations in an image
Preparation of Compacts
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• 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
Preparation of Compacts (cont’d)
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• Two types of compacts were prepared– Compacts were prepared from each blend– Compacts were prepared as 50% composites of
two blendsSample Name
Content Side A
Content Side B
Net Concentration
Change in Concentration
Comp A Lactose
only
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
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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
Data Pre-Processing
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• Median filter• Pixel average
– 1 iteration• 2nd derivative
– 5 pt window, S-G, 2nd order polynomial
Averaging Across Pixels
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• Mask is passed through each frame (or image) N times
• Advantages– Reduces noise– Clarifies larger features
• Disadvantages– Blurs sharp features
1 1
1 1
1
1 1 1
1
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Original Image 1 Iteration Average
3 Iteration Average2 Iteration Average
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Original Image 1 Iteration Average
3 Iteration Average2 Iteration Average
Intensity = 0.2037St Dev = 0.0382
Intensity = 0.2035St Dev = 0.0142
Intensity = 0.2035St Dev = 0.0108
Intensity = 0.2037St Dev = 0.0095
Intensity @ 1660 nm
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PLS Prediction –Quantitative Model
• Using 10, 20, 30, 40 and 50% SAPredict 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
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Compacts Used for Developing a Model
10% SA 20% SA
30% SA 40% SA 50% SA
Spectra Used to Build SA Quantitative Model
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-0.02
-0.01
-0.01
0.00
0.01
0.01
1100 1200 1300 1400 1500 1600 1700
Wavelength (nm)
2nd
Der
Inte
nsity
0% SA – 10% SA – 20% SA –30% SA – 40% SA – 50% SA –
Model to Predict SA Concentration
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0
10
20
30
40
50
60
0 10 20 30 40 50 60
% Salicylic Acid Reference
% S
alic
ylic
Aci
d M
easu
red Slope =
Intercept = R =RMSEC = Bias =
0.9690.735
0.99980.2790.044
Model Residuals
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-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0 10 20 30 40 50 60
Nominal %SA
% S
A M
odel
Res
idua
l
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Testing the Model to Predict SA Concentration
0
10
20
30
40
50
0 10 20 30 40 50
% Salicylic Acid Reference
% S
alic
ylic
Aci
d M
easu
red
RMSEC = Bias =
1.960.38
RMSEP
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
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hν 1hν 2
hν 3
hν N
..
..
PLS Model
One imagePixel Intensity = SA Content
Many imagesPixel Intensity = NIR reading at a singe hυ
NIR Spectrum(Pixel 1) SA Content
(Pixel 1)
Sample Image After PLS Processing
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Acknowledgements
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• ChemImage (formerly, Chemicon)
– Matt Nelson, Ph.D.
– Laura Grudowski
• James K. Drennen III, Ph.D.
• Perkin-Elmer Instruments