PHARMACEUTICAL FINGERPRINTING - EVALUATION OF NEURAL NETWORKS AND CHEMOMETRIC TECHNIQUES FOR DISTINGUISHING AMONG SAME PRODUCT MANUFACTURERS

The present study was undertaken to evaluate several computer-based cl assifiers as potential tools for pharmaceutical fingerprinting by util izing normalized data obtained from HPLC trace organic impurity patter ns, To assess the utility of this approach, samples of L-tryptophan (L T) 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 ch emometric methods, K-nearest neighbors (KNN) and soft independent mode ling of class analogy (SIMCA), as well as with a panel of human expert s, The architecture of all three computer-based classifiers was varied with respect to the number of input variables, The ANNs were also opt imized with respect to the number of nodes per hidden layer and to the number of hidden layers, A novel preprocessing scheme known as the Wi ndow method was devised for converting the output of 899 data entries extracted from each chromatogram into an appropriate input file for th e classifiers, Analysis of the test set data revealed that an ANN with 46 inputs (i.e., ANN-46) was superior to all other classifiers evalua ted, with 93% of the chromatograms correctly classified. Among the cla ssifiers studied in detail, the order of performance was ANN-46 (93%) > SIMCA-46 (87%) > KNN-46 (85%) = ANN-899 (85%) > ''human experts'' (8 3%) > SIMCA-899 (78%) greater than or equal to ANN-22 (77%)= KNN-22 (7 7%) greater than or equal to KNN-899 (76%) > SIMCA-22 (73%), These res ults confirm that ANNs, particularly when used in conjunction with the Window preprocessing scheme, can provide a fast, accurate, and consis tent methodology applicable to pharmaceutical fingerprinting. Particul ar attention was paid to variations in the HPLC patterns of same-manuf acturer 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 indica te 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 t o their stationary-phase characteristics and two of the three columns were from the same vendor.