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Application of NIR for counterfeit drug detection Another proof that chemometrics is usable: NIR confirmed by HPLC-DAD-M

Application of NIR for counterfeit drug detection Another proof that chemometrics is usable: NIR confirmed by HPLC-DAD-MS and CE-UV . O. Rodionova , A. Pomerantsev, L. Houmøller, A. V. Shpak, O. Shpigun. Institute of Chemical Physics Moscow Arla Foods amba, Videbæk, Denmark

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Application of NIR for counterfeit drug detection Another proof that chemometrics is usable: NIR confirmed by HPLC-DAD-M

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  1. Application of NIR for counterfeit drug detectionAnother proof that chemometrics is usable: NIR confirmed by HPLC-DAD-MS and CE-UV O. Rodionova, A. Pomerantsev, L. Houmøller, A. V. Shpak, O. Shpigun Institute of Chemical Physics Moscow Arla Foods amba, Videbæk, Denmark Moscow State University, Moscow

  2. The Pros and Cons of the 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 Seeming simplicity

  3. An understanding of the algorithm is required . • Any pre-processing of the data carried out by the software by default should be stated. The selection of the appropriate method depends on the scope of the spectral library

  4. t3    t2 t1 SIMCA (Soft Independent Modeling of Class Analogy) Disjoint PCA class-modeling New object is compared with each class S. Wold 1976

  5. Score distance (SD), hi hi

  6. Orthogonal distance (OD), vi vi

  7. Calculated by the PCA decomposition Set by a researcher Estimated DoF Nv , Nh Acceptance areas J. Chemometrics 2008; 22; 601-609 A. Pomerantsev Acceptance areas for multivariate classification derived by projection methods

  8. g=0.2 22 points are out g=0.4 43 points are out g=0.01 1 point is out g=0.05 5 points are out g=0.1 11 points are out Type I error a. I=100 a=0.01 a=0.05 a=0.1 a=0.2 a=0.4 OUT 43 object OUT 11 object OUT 1 object OUT 22 object OUT 5 object Type II error, b ?

  9. Case Study Object: 4% aqueous solution of dexamethasone 21-phosphate in closed transparent glass ampoules (glucocorticosteroid remedy)

  10. Data Set Genuine objects Batch G1: 15 ampoules Batch G2 : 15 ampoules Counterfeit objects Batch F2: 15 ampoules G1 G2 F2

  11. Laboratory 1 Spectrum 100N %T Through ampoule • No strong evidence of NIR distinction on basis of sample constituents • Possible difference in glass container contributions 11

  12. Laboratory 2. (Nicolet Antaris) After MSC correction PCA Raw spectra PCA 12

  13. Laboratory 3 Bomem 160 FT NIR spectral range 5500-10000 cm-1 , resolution8cm-1 8 mm vial holder T= 30ºC

  14. Spectral range -log(T) 30 genuine samples (blue lines) 15 fake samples (red lines)

  15. Explorative analysis SNV pre-processing PCA Selected spectral ranges Scores plot

  16. SIMCA classification G1 calibration set G2 calibration set 16

  17. HPLC-DAD Chromatograms for G1 Micro-imputities in G1

  18. Peak areas of the impurities The genuine G1 sample is used as the reference (HPLC-DAD, UV detection at 254 nm)

  19. HPLC- DAD Chromatograms of Fake (F2) and Genuine (G1, G2) Samples Conclusion 1.Peak positions for samples G1 and G2 are identical, but for impurities 2-4 peak areas differed notably. Conclusion2.: Large peak, corresponding to impurity 10, for the fake sample, which, together with the absence of impurities 2 and 3, makes it possible to detect forgery

  20. CE – UV results Electropherograms for the genuine (G1) and fake (F2) samples

  21. April 2009 Listenon Suxamethonium Mildronate Trimethylhydrazinium propionate dihydrate Remedy for the treatment of heart and blood vessel diseases Applied for muscle relaxation in anaesthesia and intensive care 22

  22. Conclusions • Micro-impurity analysis is important for disclosure of "high quality forgeries" • NIR analysis cannot reveal the sources of disagreement between the tested samples • A general approach is to consider a remedy as a whole object, taking into account a complex composition of active ingredients, excipients, as well as manufacturing conditions

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