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