DEVELOPMENT OF A MODEL FOR CLASSIFICATION OF TOXIN-INDUCED LESIONS USING H-1-NMR SPECTROSCOPY OF URINE COMBINED WITH PATTERN-RECOGNITION

Pattern recognition approaches were developed and applied to the class ification of 600 MHz H-1 NMR spectra of urine from rats dosed with com pounds that induced organ-specific damage in either the liver or kidne y. Male rats were separated into groups (n = 5) and each treated with one of the following compounds; adriamycin, allyl alcohol, 2-bromoetha namine hydrobromide, hexachlorobutadiene, hydrazine, lead acetate, mer cury II chloride, puromycin aminonucleoside, sodium chromate, thioacet amide, 1,1,2-trichloro-3,3,3-trifluoro-1-propene or dose vehicle. Urin e samples were collected over a 7 day time-course and analysed using 6 00 MHz H-1 NMR spectroscopy. Each NMR spectrum was data-reduced to pro vide 256 intensity-related descriptors of the spectra. Data correspond ing to the periods 8-24 h, 24-32 h and 32-56 h post-dose were first an alysed using principal components analysis (PCA). In addition, samples obtained 120-144 h following the administration of adriamycin and pur omycin were included in the analysis in order to compensate for the fa te onset of glomerular toxicity. Having established that toxin-related clustering behaviour could be detected in the first three principal c omponents (PCs), three-quarters of the data were used to construct a s oft independent modelling of class analogy (SIMCA) model. The remainde r of the data were used as a test set of the model. Only three out of 61 samples in the test set were misclassified. Finally as a further te st of the model, data from the H-1 NMR spectra of urine from rats that had been treated with uranyl nitrate were used. Successful prediction of the toxicity type of the compound was achieved based on NMR urinal ysis data confirming the robust nature of the derived model. (C) 1998 John Wiley & Sons, Ltd.