Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition.
Pattern recognition approaches were developed and applied to the classification of 600 MHz 1H NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in either the liver or kidney. Male rats were separated into groups (n = 5) and each treated with one of the following compounds; adriamycin, allyl alcohol, 2-bromoethanamine hydrobromide, hexachlorobutadiene, hydrazine, lead acetate, mercury II chloride, puromycin aminonucleoside, sodium chromate, thioacetamide, 1,1,2-trichloro-3,3,3-trifluoro-1-propene or dose vehicle. Urine samples were collected over a 7 day time-course and analysed using 600 MHz 1H NMR spectroscopy. Each NMR spectrum was data-reduced to provide 256 intensity-related descriptors of the spectra. Data corresponding to the periods 8-24 h, 24-32 h and 32-56 h post-dose were first analysed using principal components analysis (PCA). In addition, samples obtained 120-144 h following the administration of adriamycin and puromycin were included in the analysis in order to compensate for the late onset of glomerular toxicity. Having established that toxin-related clustering behaviour could be detected in the first three principal components (PCs), three-quarters of the data were used to construct a soft independent modelling of class analogy (SIMCA) model. The remainder 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 test of the model, data from the 1H 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 urinalysis data confirming the robust nature of the derived model.