Application of NIR for counterfeit drug detection Another proof that chemometrics is usable: NIR confirmed by HPLC-DAD-MS and CE-UV

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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
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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
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SIMCA (Soft Independent Modeling of Class Analogy)
Disjoint PCA class-modeling
New object is compared with each class
S. Wold 1976
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Score distance (SD), hi
hi
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Orthogonal distance (OD), vi
vi
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Acceptance areas
J. Chemometrics 2008 22 601-609 A.
Pomerantsev Acceptance areas for multivariate
classification derived by projection methods
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Type I error a. I100
a0.01
a0.05
a0.1
a0.2
a0.4
OUT 1 object
OUT 5 object
OUT 11 object
OUT 22 object
OUT 43 object
Type II error, b ?
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Case Study
Object 4 aqueous solution of dexamethasone
21-phosphate in closed transparent glass
ampoules (glucocorticosteroid remedy)
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Data Set
Genuine objects Batch G1 15 ampoules Batch G2
15 ampoules
Counterfeit objects Batch F2 15 ampoules
G1
G2
F2
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Laboratory 1
T Through ampoule
Spectrum 100N

• No strong evidence of NIR distinction on basis of
  sample constituents
• Possible difference in glass container
  contributions

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Laboratory 2. (Nicolet Antaris)
Raw spectra
PCA
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Laboratory 3
Bomem 160 FT NIR spectral range 5500-10000 cm-1
, resolution 8 cm-1
8 mm vial holder T 30ºC
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Spectral range
-log(T)
30 genuine samples (blue lines) 15 fake
samples (red lines)
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Explorative analysis
PCA
SNV pre-processing
Scores plot
Selected spectral ranges
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SIMCA classification
G1 calibration set
G2 calibration set
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HPLC-DAD Chromatograms for G1
Micro-imputities in G1
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Peak areas of the impurities
The genuine G1 sample is used as the reference
(HPLC-DAD, UV detection at 254 nm)
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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
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CE UV results
Electropherograms for the genuine (G1) and fake
(F2) samples
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April 2009
Mildronate Trimethylhydrazinium propionate
dihydrate
Listenon Suxamethonium
Remedy for the treatment of heart and blood
vessel diseases
Applied for muscle relaxation in anaesthesia and
intensive care
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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