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

Active Ingredient Excipients

Packaging Source

Counterfeit Medicine

API Assay in literature (1)

Chromatographic

Hyphenated

Spectroscopic

Near-infrared spectroscopy (NIRS)

Chemometrics: Partial Least Square

Regression (PLSR, MLR)

Quick, simple, accurate, non-

destructive

API Assay in literature (2)

Paracetamol

Ibuprofen

Macrolides

PLSR and MLR used in NIR

Quantified proprietary

Used more than one brand to

quantify generics

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:

Experimental (1)

MethodSample Preparation

• Tablets • Powdered mixtures • Pellets

NIR measurement

Spectra TreatmentPre-treatment (SNV-D2)

Quantitative model development

Experimental (2)

Experimental (3)

Quantitative Model Development

Results and Discussion (1)

Calibration and validation sets

Number of factors

Model quality (correlation coefficient, SEC, SEP)

Model selection (1)

Results and Discussion (2)

Model selection (2)

PLSR Loadings

Results and Discussion (3)

Results and Discussion (4)

Model Prediction (1)

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

Results and Discussion (6)Effect of adding 0 and 100 % to the models (2)

Effect of adding 0 and 100 % to the models (3)

Results and Discussion (7)

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.

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

Thank You For Thank You For Your AttentionYour Attention

sulaf.assi@pharmacy.ac.uk