Bpc 2008 Presentation Sulaf3
Transcript of Bpc 2008 Presentation Sulaf3
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S. Assi, R. Watt, A.C. Moffat
Assay of Ciprofloxacin in Intact and Powdered Tablets by Near-infrared
Spectroscopy
The School of Pharmacy- University of London
29/39 Brunswick Square
London-United Kingdom
WC1N 1AX
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Active Ingredient Excipients
Packaging Source
Counterfeit Medicine
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API Assay in literature (1)
Chromatographic
Hyphenated
Spectroscopic
Near-infrared spectroscopy (NIRS)
Chemometrics: Partial Least Square
Regression (PLSR, MLR)
Quick, simple, accurate, non-
destructive
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API Assay in literature (2)
Paracetamol
Ibuprofen
Macrolides
PLSR and MLR used in NIR
Quantified proprietary
Used more than one brand to
quantify generics
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Quantify ciprofloxacin in generics
Use one brand only (Bayer’s Ciproxin)
Minimum or no sample treatment
Use the minimum amount of active or excipients
Use a range of active from 0 up to 100 %
Aim of the work:
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Experimental (1)
MethodSample Preparation
• Tablets • Powdered mixtures • Pellets
NIR measurement
Spectra TreatmentPre-treatment (SNV-D2)
Quantitative model development
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Experimental (2)
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Experimental (3)
Quantitative Model Development
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Results and Discussion (1)
Calibration and validation sets
Number of factors
Model quality (correlation coefficient, SEC, SEP)
Model selection (1)
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Results and Discussion (2)
Model selection (2)
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PLSR Loadings
Results and Discussion (3)
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Results and Discussion (4)
Model Prediction (1)
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Results and Discussion (5)
Effect of adding 0 and 100 % to the models (1)
Better prediction for intact tablets
The best type of zero used was erythromycin tablet
rather than excipient
The optimum number of zeros to be used is one
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Results and Discussion (6)Effect of adding 0 and 100 % to the models (2)
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Effect of adding 0 and 100 % to the models (3)
Results and Discussion (7)
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CONCLUSION
NIR measurement with PLSR could quantify active in one
generics by diluting one proprietary type only with active or
excipient(s).
No or minimal sample treatment is required .
Addition of 0 and 100 % to Ciproxin standard addition model
improved the model so it could predict intact tablets.
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Acknowledgement
The School of Pharmacy-University of London
FOSS COMPANY for the FOSS NIR 6500
Lebanese National Council for Scientific Research
Tanzanian Food and Drug Adminstration