title: 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.
authors.affiliation: Department of Chemistry, University of Missouri-St. Louis 63121, USA.
authors.forename: W J
authors.initials: WJ
authors.lastname: Welsh
authors.forename: W
authors.initials: W
authors.lastname: Lin
authors.forename: S H
authors.initials: SH
authors.lastname: Tersigni
authors.forename: E
authors.initials: E
authors.lastname: Collantes
authors.forename: R
authors.initials: R
authors.lastname: Duta
authors.forename: M S
authors.initials: MS
authors.lastname: Carey
authors.forename: W L
authors.initials: WL
authors.lastname: Zielinski
authors.forename: J
authors.initials: J
authors.lastname: Brower
authors.forename: J A
authors.initials: JA
authors.lastname: Spencer
authors.forename: T P
authors.initials: TP
authors.lastname: Layloff
mesh_terms: D002626:Chemistry, Pharmaceutical; D002851:Chromatography, High Pressure Liquid; D004340:Drug Contamination; D005069:Evaluation Studies as Topic; D006801:Humans; D008958:Models, Molecular; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D014364:Tryptophan / Q000032:analysis*
chemical_list: D014364:Tryptophan