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.

Gas-llquld chromatography in the nematic region of N,N'bls(p-methoxybenzylldene)-a,&'-bi-p-toluidine has shown base-line separations for geometric isomers of 3-5 ring p0lyaromatic hydrocarbons (PAH). This application is appropriate for 2-6 ring PAH compounds. The unique selectivity of this liquid phase, based upon differences in the molecular length-to-breadth ratio of solute geometric isomers, has en-Present address, University of Tripoli, Tripoli, Libya.

A quantitative structure-retention relationship (QSRR) was developed from chromatographic data on 31 unsubstituted 3-6 ring polycyclic aromatic hydrocarbons (PAHs) using the 3D-QSAR method known as comparative molecular field analysis (CoMFA). The resulting CoMFA model gave an excellent correlation to high-performance liquid chromatography retention data for these PAHs yielding r2 values of 0.947 (conventional) and 0.865 (cross-validated). The steric and electrostatic contributions to the CoMFA model were 100% and 0%, respectively. A unique feature of this study was the use of moment of inertia, I, as a basis for CoMFA alignment of the PAH molecules. The moment of inertia also provided an alternative method for calculating the solute length-to-breadth ratio (L/B), which has been applied in previous QSRR studies as a molecular descriptor for PAH retention. By virtue of its mathematical simplicity and lack of ambiguity, the present derivation of L/B from I offers several advantages over other geometry-based schemes. Finally, Ix was evaluated for use as a molecular descriptor in QSRR regression analysis to predict the log of the retention index (log I) for these PAHs. The correlation with PAH retention was weak when the moment of inertia was considered alone but improved dramatically (r2 = 0.928) when the moment of inertia and connectivity index chi were used in combination as descriptors.

This paper describes the enantiorecognition of (±)nicotine and (±)nornicotine by high‐performance liquid chromatography using two derivatized cellulose chiral stationary phases (CSPs) operated in the normal phase mode. It was found that different substituents linked to the cellulose backbone significantly influence the chiral selectivity of the derivatized CSP. The results showed that, in general, the tris(4‐methylbenzoyl) cellulose CSP (Chiralcel OJ) surpasses tris(3,5‐dimethylphenyl carbamoyl) cellulose CSP (Chiralcel OD). On the former column, the resolution (±)nicotine and (±)nornicotine enantiomers depended largely on mobile phase compositions. For the separation of the nicotine enantiomers, the addition of trifluoroacetic acid to a 95:5 hexane/alcohol mobile phase greatly improved the enantioresolution, probably due to enhanced hydrogen bonding interactions between the protonated analytes and the CSP. For (±)nornicotine separation, a reduction in the concentration of alcohol in the mobile phase was more effective than the addition of trifluoroacetic acid. Possible solute‐mobile phase‐stationary phase interactions are discussed to explain how different additives in the mobile phase and different substituents on the cellulose glucose units of the CSPs affect the separation of both pairs of enantiomers. Chirality 10:364–369, 1998. Published 1998 Wiley‐Liss, Inc.

The immediate objective of this research program is to evaluate several computer-based classifiers as potential tools for pharmaceutical fingerprinting based on analysis of HPLC trace organic impurity patterns. In the present study, wavelet packets (WPs) are investigated for use as a preprocessor of the chromatographic data taken from commercial samples of L-tryptophan (LT) to extract input data appropriate for classifying the samples according to manufacturer using artificial neural networks (ANNs) and the standard classifiers KNN and SIMCA. Using the Haar function, WP decompositions for levels L = 0-10 were generated for the trace impurity patterns of 253 chromatograms corresponding to LT samples that had been produced by six commercial manufacturers. Input sets of N = 20, 30, 40, and 50 inputs were constructed, each one consisting of the first N/2 WP coefficents and corresponding positions from the overall best level (L = 2). The number of hidden nodes in the ANNs was also varied to optimize performance. Optimal ANN performance based on percent correct classifications of test set data was achieved by ANN-30-30-6 (97%) and ANN-20-10-6 (94%), where the integers refer to the numbers of input, hidden, and output nodes, respectively. This performance equals or exceeds that obtained previously (Welsh, W.J.; et al.Anal.Chem. 1996, 68, 3473) using 46 inputs from a so-called Window preprocessor (93%). KNN performance with 20 inputs (97%) or 30 inputs (90%) from the WP preprocessor also exceeded that obtained from the Window preprocessor (85%), while SIMCA performance with 20 inputs (86%) or 30 inputs (82%) from the WP preprocessor was slightly inferior to that obtained from the Window preprocessor (87%). These results indicate that, at least for the ANN and KNN classifiers considered here, the WP preprocessor can yield superior performance and with fewer inputs compared to the Window preprocessor.