Chemometric models for toxicity classification based on NMR spectra of biofluids.
1H NMR spectroscopic and pattern recognition (PR)-based methods were used to investigate the biochemical variability in urine obtained from control rats and from rats treated with a hydrazine (a model hepatotoxin) or HgCl(2) (a model renal cortical toxin). The 600 MHz (1)H NMR spectra of urine samples obtained from vehicle- or toxin-treated Han-Wistar (HW) and Sprague-Dawley (SD) rats were acquired, and principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA) analysis were used to investigate the (1)H NMR spectral data. Variation and strain differences in the biochemical composition of control urine samples were assessed. Control urine (1)H NMR spectra obtained from the two rat strains appeared visually similar. However, chemometric analysis of the control urine spectra indicated that HW rat urine contained relatively higher concentrations of lactate, acetate, and taurine and lower concentrations of hippurate than SD rat urine. Having established the extent of biochemical variation in the two populations of control rats, PCA was used to evaluate the metabolic effects of hydrazine and HgCl(2) toxicity. Urinary biomarkers of each class of toxicity were elucidated from the PC loadings and included organic acids, amino acids, and sugars in the case of mercury, while levels of taurine, beta-alanine, creatine, and 2-aminoadipate were elevated after hydrazine treatment. SIMCA analysis of the data was used to build predictive models (from a training set of 416 samples) for the classification of toxicity type and strain of rat, and the models were tested using an independent set of urine samples (n = 124). Using models constructed from the first three PCs, 98% of the test samples were correctly classified as originating from control, hydrazine-treated, or HgCl(2)-treated rats. Furthermore, this method was sensitive enough to predict the correct strain of the control samples for 79% of the data, based upon the class of best fit. Incorporation of these chemometric methods into automated NMR-based metabonomics analysis will enable on-line toxicological assessment of biofluids and will provide a tool for probing the mechanistic basis of organ toxicity.
Holmes
E
E
Biological Chemistry, Biomedical Sciences Division, Imperial College of Science, Technology and Medicine, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK.
Nicholls
A W
AW
Lindon
J C
JC
Connor
S C
SC
Connelly
J C
JC
Haselden
J N
JN
Damment
S J
SJ
Spraul
M
M
Neidig
P
P
Nicholson
J K
JK
D000818:Animals; D001711:Biotransformation; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification* / Q000652:urine; D005163:Factor Analysis, Statistical; D006834:Hydrazines / Q000737:chemistry / Q000633:toxicity / Q000652:urine*; D007672:Kidney Cortex / Q000187:drug effects / Q000473:pathology; D008099:Liver / Q000187:drug effects / Q000473:pathology; D009682:Magnetic Resonance Spectroscopy; D008627:Mercuric Chloride / Q000737:chemistry / Q000633:toxicity / Q000652:urine*; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D051381:Rats; D017207:Rats, Sprague-Dawley; D017208:Rats, Wistar; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
D006834:Hydrazines; C029424:hydrazine; D008627:Mercuric Chloride