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Recognition of the 'high quality’ forgeries among medicines
Oxana Rodionova Semenov Institute of Chemical Physics, RAS Moscow Russian Chemometric Society Kazan WSC-6 August 23, 2019
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Kazan WSC-6 August 23, 2019
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Forgery types Low concentration/quality of active substance Placebo
Wrong active substance Another excipient Forgery types Produced with violation of technological regulations Contain no declared substances Kazan WSC-6 August 23, 2019
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Why NIR - based approach ?
Near- infrared (NIR) spectroscopy Multivariate data analysis (chemometrics) + Advantages Routine test time 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 August 23, 2019
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Methods Measurements NIR (800 – 2 500nm) reflectance spectra with integrating sphere Data pre-processing SNV/MSC + column-wise data centering Variable reduction PCA Supervised pattern recognition SIMCA+special critical limits Kazan WSC-6 August 23, 2019
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Data Set Overview N Description Genuine batches Forgery batches Total
1 Complex antibacterial drug (2 active substances) 5 (5) 50 2 Antispasmodic drug 1 (10) 1 (9) 19 3 Antibiotic drug 17 (5-10) 2 (5) 109 4 Digestive enzyme (Manufacture 1) 4 (5) 30 5 Digestive enzyme (Manufacture 2) 11 (5) 75 Kazan WSC-6 August 23, 2019
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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 August 23, 2019
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Complex antibacterial drug (2 active substances)
25 original tablets from 5 batches 25 counterfeit tablets from 5 batches Kazan WSC-6 August 23, 2019
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Antispasmodic drug 10 original pills 9 counterfeit pills Kazan WSC-6
August 23, 2019
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Antibiotic drug 89 original tablets from 17 batches
20 counterfeit tablets from 2 batches Kazan WSC-6 August 23, 2019
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Main steps SIMCA classification with class limits for genuine samples
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 August 23, 2019
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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 August 23, 2019
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Influence of spectral region. Data set 5. (A digestive enzyme)
Counterfeit 4 batches (5 pills) Genuine Sample 45 pills from 9 batches Genuine 11 batches (5 pills) 75 Samples Calibration Test 10 pills from 2 batches Kazan WSC-6 August 23, 2019
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Calibration & Classification (previous lecture)
outlier outliers extreme A=2 Kazan WSC-6 August 23, 2019
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Unscrambler overview Kazan WSC-6 August 23, 2019
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NIR spectra Kazan WSC-6 August 23, 2019
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Classification on short spectral region
Kazan WSC-6 August 23, 2019
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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 August 23, 2019
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Initial PCA model G25 G25 4 3 5 DoF_h 1.99 5.68 4 DoF_v 5.87 5.78 5 MM
IQR 4 3 5 N=68, A=4 g=0.01 Kazan WSC-6 August 23, 2019
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Classification . Kazan WSC-6 August 23, 2019
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Classification without batch G25
Calibration Test N=64, A=3 Kazan WSC-6 August 23, 2019
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Peculiarity of G25 Kazan WSC-6 August 23, 2019
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NIR spectra for G25 Kazan WSC-6 August 23, 2019
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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 August 23, 2019
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Thanks! Be Healthy! Do not apply any remedy!
Kazan WSC-6 August 23, 2019
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