application of nir for counterfeit drug detection another proof that chemometrics is usable: nir...
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Application of NIR for counterfeit drug detection
Another proof that chemometrics is usable: NIR confirmed by HPLC-DAD-MS and CE-UV
Institute of Chemical Physics Moscow Arla Foods amba, Videbæk, Denmark
Moscow State University, Moscow
O. Rodionova, A. Pomerantsev, L. Houmøller, A. V. Shpak,
O. Shpigun
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
Disadvantages
NIR is not a stand-alone technology. Mathematical calibration for each type of medicine is required
Trad
e-offT
rade-off
Seeming simplicitySeeming simplicity
Spectral range
Pre-treatmentNumber of samples
Method Thresholds
1000-2500 nm 2-nd derivative3spectra from 3 batches
WC + MWD 0.95 for WC
should be described and justified
smoothing, baseline correction, derivatisation,etc.
3spectra from 6 batches
PCA, LDA, QDA, k-NN, SIMCA, SVM
?
2003 (in force now)
2009 (draft, in preparation)
•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
SIMCA (Soft Independent Modeling of SIMCA (Soft Independent Modeling of Class Analogy)Class Analogy)
S. Wold 1976S. Wold 1976
New object is compared with each classNew object is compared with each class
Disjoint PCA class-modeling Disjoint PCA class-modeling
t1
t2
t3
Score distance (SD), Score distance (SD), hhii
Ii
th
A
a a
iaiAAii ,,1,)(
1
21tt
tTTt
I
ii I
Ah
Ih
10
1
hi
Orthogonal distance (OD), Orthogonal distance (OD), vvii
I
ii AR
I
Lv
Iv
1
00 )(1
1
vi
A
aia
K
Aaia
J
jiji tLtev
1
20
1
2
1
2
Acceptance areasAcceptance areas
Nv , Nh
Calculated by the PCA decomposition Estimated DoF Set by a researcher
J. Chemometrics 2008; 22; 601-609 A. Pomerantsev
Acceptance areas for multivariate classification derived by projection methods
J. Chemometrics 2008; 22; 601-609 A. Pomerantsev
Acceptance areas for multivariate classification derived by projection methods
Type I error I=100=0.01
1 point is out
=0.05
5 points are out
=0.1
11 points are out
=0.2
22 points are out
=0.4
43 points are out=0.01
OUT 1 object
=0.05
OUT 5 object
=0.1
OUT 11 object
=0.2
OUT 22 object
=0.4
OUT 43 object
Type II error, ?
Case Study
Object: 4% aqueous solution of dexamethasone 21-phosphate in closed transparent glass ampoules (glucocorticosteroid remedy)
Data Set
Genuine objects
Batch G1: 15 ampoules
Batch G2 : 15 ampoules
Counterfeit objects
Batch F2: 15 ampoules
11
Laboratory 1Laboratory 1
12000.0 8000 5000.0
0.0
5
10
15
20
25
30
35
40.1
cm-1
%T
%T Through ampoule
• No strong evidence of NIR distinction on basis of sample constituents
• Possible difference in glass container contributions
Spectrum 100N
12
Laboratory 2. (Nicolet Antaris)Laboratory 2. (Nicolet Antaris)
1.5
2
2.5
3
3.5
4
4.5
5500 6000 6500
cm-1
ab
so
rba
nc
e
G1G2G3G4
G5G6 G7
G8 G9
G10G11 G12
G13G14 G15
F1
F2
F3 F4
F5
F6
F7
F8F9
F11
F12
F13
F14F15F16
F17F18F19
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PC1
PC2
G1G2
G3
G4
G5
G6G7
G8
G9
G10
G11 G12
G13
G14
G15
F1
F2
F3F4
F5F6
F7F8
F9
F11
F12
F13
F14F15
F16
F17
F18F19
-0.5
0
0.5
-2 -1 0 1 2
PC1
PC2
Raw spectra
PCA
1.5
2
2.5
3
3.5
4
4.5
5500 5600 5700 5800 5900 6000 6100 6200 6300 6400 6500
cm-1
ab
so
rba
nc
e
After MSC correction
PCA
13
Laboratory 3Laboratory 3
Bomem 160 FT NIR
spectral range 5500-10000 cm-1 ,
resolution 8 cm-1
8 mm vial holder T= 30ºC
14
Spectral rangeSpectral range
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5500 6000 6500 7000 7500 8000 8500 9000 9500 10000
cm-1
ab
so
rba
nc
e-log(T)
30 genuine samples (blue lines)
15 fake samples (red lines)
15
Explorative analysisExplorative analysis
PCA
Scores plotSelected spectral ranges
SNV pre-processing
16
SIMCA classificationSIMCA classification
G1 calibration set G2 calibration set
0
1
2
3
0 1 2 3
(h /h 0)1/4
(v /v 0)1/4
F2 G1
G2 Area
0
1
2
3
0 1 2 3
(h /h 0)1/4
(v /v 0)1/4
F2 G1
G2 Area
17
Micro-imputities in G1
HPLC-DAD Chromatograms for G1HPLC-DAD Chromatograms for G1
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) HPLC- DAD Chromatograms of Fake (F2) and Genuine (G1, G2) Samplesand Genuine (G1, G2) Samples
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
Conclusion 1. Peak positions for samples G1 and G2 are identical, but for impurities 2-4 peak areas differed notably.
CE – UV results
Electropherograms for the genuine (G1) and fake (F2) samples
21
22
April 2009April 2009
Remedy for the treatment of heart and blood vessel diseases
Mildronate Trimethylhydrazinium propionate dihydrate
Applied for muscle relaxation in anaesthesia and intensive care
ListenonSuxamethonium
23
ConclusionsConclusions
• 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
• 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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