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Recognition of the 'high quality’ forgeries among medicines

Semenov Institute of Chemical Physics, RAS Moscow. Russian Chemometric Society. Recognition of the 'high quality’ forgeries among medicines. Oxana Rodionova . Forgery types. Placebo. Low concentration/quality of active substance. Wrong active substance. Another excipient.

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Recognition of the 'high quality’ forgeries among medicines

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  1. Semenov Institute of Chemical Physics, RAS Moscow Russian Chemometric Society Recognition of the 'high quality’ forgeries among medicines Oxana Rodionova Kazan WSC-6

  2. Kazan WSC-6

  3. Forgery types Placebo Low concentration/quality of active substance Wrong active substance Another excipient Produced with violation of technological regulations Contain no declared substances Kazan WSC-6

  4. Why NIR - based approach ? Near- infrared (NIR) spectroscopy Multivariate data analysis (chemometrics) + Advantages Routine test time 5 min Sample preparation No Personal training level Low Trade-off Disadvantages NIR is not a stand-alone technology. Mathematical calibration for each type of medicine is required Kazan WSC-6

  5. Methods Kazan WSC-6

  6. Data Set Overview Kazan WSC-6

  7. Forgeries of different 'quality' 1. Easily detected without instruments Dietary supplements 2. Easily detected by a regular pharmacopeia test as well as by the NIR-based approach 3. Easily detected by NIR-based approach but are not detected by the pharmacopeia tests 4. Various intricate cases for NIR approach and are not detected by the pharmacopeia tests Kazan WSC-6

  8. Complex antibacterial drug (2 active substances) 25 original tablets from 5 batches 25 counterfeit tablets from 5 batches Kazan WSC-6

  9. Antispasmodic drug 10 original pills 9 counterfeitpills Kazan WSC-6

  10. Antibiotic drug 89 original tablets from 17 batches 20 counterfeit tablets from 2 batches Kazan WSC-6

  11. Main steps PCA model for genuine samples SIMCA classification with class limits for genuine samples Representative calibration set Pertinent external validation Proper variable selection Kazan WSC-6

  12. Variability of genuine drugs A digestive enzyme (Data sets 4 &5) Manufacture N1 Manufacture N2 20 original tablets from 4 batches 10 counterfeit tablets from 1 batch 55 original tablets from 11 batches Kazan WSC-6

  13. Genuine 11 batches (5 pills) Counterfeit 4 batches (5 pills) Test 75 Samples Calibration Counterfeit 4 batches (5 pills) Genuine Sample 45 pills from 9 batches Genuine Sample 10 pills from 2 batches Influence of spectral region.Data set 5. (A digestive enzyme) Kazan WSC-6

  14. outlier Calibration & Classification(previous lecture) outliers extreme A=2 Kazan WSC-6

  15. Unscrambler overview Kazan WSC-6

  16. NIR spectra Kazan WSC-6

  17. Classification on short spectral region Kazan WSC-6

  18. Step-by-step classification . Data set 3 Synthetic Antiprotozoal and Antibacterial Agent, 1-(β-hydroxy-ethyl)-2-methyl-5-nitroimidazole, Genuine 17 batches (5-10 pills) Counterfeit 2 batches (5 pills) Test 109 Samples Calibration Counterfeit 2 batches (5 pills) Genuine Sample 68 pills from 12 batches Genuine Sample 31 pills from 6 batches Kazan WSC-6

  19. G25 G25 MM IQR DoF_h 1.99 5.68 4 DoF_v 5.87 5.78 5 Initial PCA model 4 3 5 N=68, A=4 g=0.01 Kazan WSC-6

  20. Classification Kazan WSC-6

  21. Classification without batch G25 G25 Calibration Test N=64, A=3 Kazan WSC-6

  22. Peculiarity of G25 Kazan WSC-6

  23. NIR spectra for G25 Kazan WSC-6

  24. Conclusions Each medical product should be carefully tested for batch-to-batch variability The selection of a spectral region should be done for each type of medicine individually. The choice of the spectral region may essentially influence the final classification results. It is crucial not only to recognize forgeries but also to avoid misclassification of genuine samples. The application of reliable acceptance limits is of great importance Kazan WSC-6

  25. Thanks! Be Healthy! Do not apply any remedy! Kazan WSC-6

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