Diagnostic performance of traditional hepatobiliary biomarkers of drug-induced liver injury in the rat.

Nonclinical studies provide the opportunity to anchor biochemical with morphologic findings; however, liver injury is often complex and heterogeneous, confounding the ability to relate biochemical changes with specific patterns of injury. The aim of the current study was to compare diagnostic performance of hepatobiliary markers for specific manifestations of drug-induced liver injury in rat using data collected in a recent hepatic toxicogenomics initiative in which rats (n = 3205) were given 182 different treatments for 4 or 14 days. Diagnostic accuracy of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbili), serum bile acids (SBA), alkaline phosphatase (ALP), gamma glutamyl transferase (GGT), total cholesterol (Chol), and triglycerides (Trig) was evaluated for specific types of liver histopathology by Receiver Operating Characteristic (ROC) analysis.

Multi-platform investigation of the metabolome in a leptin receptor defective murine model of type 2 diabetes.

We describe a multi-platform ((1)H NMR, LC-MS, microarray) investigation of metabolic disturbances associated with the leptin receptor defective (db/db) mouse model of type 2 diabetes using novel assignment methodologies. For the first time, several urinary metabolites were found to be associated with diabetes and/or diabetes progression and confirmed in both NMR and LC-MS datasets. The confirmed metabolites were trimethylamine-n-oxide (TMAO), creatine, carnitine, and phenylalanine.

Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat.

The Critical Path Institute recently established the Predictive Safety Testing Consortium, a collaboration between several companies and the U.S. Food and Drug Administration, aimed at evaluating and qualifying biomarkers for a variety of toxicological endpoints.

Weighted least-squares deconvolution method for discovery of group differences between complex biofluid 1H NMR spectra.

Biomarker discovery through analysis of high-throughput NMR data is a challenging, time-consuming process due to the requirement of sophisticated, dataset specific preprocessing techniques and the inherent complexity of the data. Here, we demonstrate the use of weighted, constrained least-squares for fitting a linear mixture of reference standard data to complex urine NMR spectra as an automated way of utilizing current assignment knowledge and the ability to deconvolve confounded spectral regions. Following the least-squares fit, univariate statistics were used to identify metabolites associated with group differences.