Chemical Imaging of Pharmaceutical Compacts

Carl Anderson, Ph.D.Assistant Professor of Pharmaceutical Sciences

Duquesne University

Areas of Interest

2

• 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

7

• 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

8

• 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

10

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