Pharmaceutical Fingerprinting:  Evaluation of Neural Networks and Chemometric Techniques for Distinguishing among Same-Product Manufacturers

Abstract

The present study was undertaken to evaluate several computer-based classifiers as potential tools for pharmaceutical fingerprinting by utilizing normalized data obtained from HPLC trace organic impurity patterns. To assess the utility of this approach, samples of L-tryptophan (LT) drug substance were analyzed from commercial production lots of six different manufacturers. The performance of several artificial neural network (ANN) architectures was compared with that of two standard chemometric methods, K-nearest neighbors (KNN) and soft independent modeling of class analogy (SIMCA), as well as with a panel of human experts. The architecture of all three computer-based classifiers was varied with respect to the number of input variables. The ANNs were also optimized with respect to the number of nodes per hidden layer and to the number of hidden layers. A novel preprocessing scheme known as the Window method was devised for converting the output of 899 data entries extracted from each chromatogram into an appropriate input file for the classifiers. Analysis of the test set data revealed that an ANN with 46 inputs (i.e., ANN-46) was superior to all other classifiers evaluated, with 93% of the chromatograms correctly classified. Among the classifiers studied in detail, the order of performance was ANN-46 (93%) > SIMCA-46 (87%) > KNN-46 (85%) = ANN-899 (85%) > "human experts" (83%) > SIMCA-899 (78%) > or = ANN-22 (77%) = KNN-22 (77%) > or = KNN-899 (76%) > SIMCA-22 (73%). These results confirm that ANNs, particularly when used in conjunction with the Window preprocessing scheme, can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting. Particular attention was paid to variations in the HPLC patterns of same-manufacturer samples due to differences in LT production lots, HPLC columns, and even run-days to quantify how these factors might hinder correct classifications. The results from these classification studies indicate that the chromatograms evidenced variations across LT manufacturers, across the three HPLC columns and, for one manufacturer, across lots. The extent of column-to-column variations is particularly noteworthy in that all three columns had identical specifications with respect to their stationary-phase characteristics and two of the three columns were from the same vendor.

Analytical Evaluation of Herbal Drugs

“…The other method is based on secured principal component regression (sPCR) that was originally developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after the PCR calibration. It can detect and consider unexpected chromatographic features for quality valuation of herbal samples from the point of view of analyzing fingerprint residual Cheng & Chen, 2003;Collantes, 1997;Vogt, 2003;Welsh, 1996;Wold, 1977;Xie, 2001].…”

Quality control of herbal medicines

“…However, correction of retention time shift of the fingerprint of herbal medicines should be taken into consideration first, since some types of variation sources are inevitably encountered from one chromatogram to another [7][8][9][10][11][12][13][166][167][168][169][170][171][172][173][174][175][176][177][178][179][180][181][182]. Under this situation, some unacceptable results, one of which is imposed by the retention time shifts, will be produced.…”

“…From this point of view, the conception of class of one herbal medicine seems to be more reasonable. Thus, the chemical pattern recognition methods, such as K-nearest neighbors (KNN) [190,192] and soft independent modeling of class analogy (SIMCA) [193], etc. should be taken into consideration for reasonable definition of the class of the herbal medicine [194][195][196].…”

“…This chromatographic profile should be featured by the fundamental attributions of "integrity" and "fuzziness" or "sameness" and "differences" so as to chemically represent the HM investigated [11,13]. It is suggested that with the help of chromatographic fingerprints obtained, the authentication and identification of herbal medicines can be accurately conducted ("integrity") even if the amount and/or concentration of the chemically characteristic constituents are not exactly the same for different samples of this HM (hence, "fuzziness") or, the chromatographic fingerprints could demonstrate both the "sameness" and "differences" between various samples successfully [11,14].…”

“…These excellent properties are the so-called dimension advantages proposed by Booksh and Kowalski [19]. A chemical fingerprint obtained by hyphenated chromatography, out of question, will become the primary tool for quality control of herbal medicines [13][14][15][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”