Kedar Kulkarni

Assessing chronic liver toxicity based on relative gene expression data.

The risk associated with exposure to hepatotoxic drugs is difficult to quantify. Animal experiments to assess their chronic toxicological impact are time consuming. New quantitative approaches to correlate gene expression changes caused by drug exposure to chronic toxicity are required. This article proposes a mathematical model entitled Toxicologic Prediction Network (TPN) to assess chronic hepatotoxicity based on subchronic hepatic gene expression data in rats. A directed graph accounts for the interactions between the drugs, differentially expressed genes and chronic hepatotoxicity. A knowledge-based mathematical model estimates phenotypical exposure risk such as toxic hepatopathy, diffuse fatty change and hepatocellular adenoma for rats. The networks edges encoding the interaction strength are determined by solving an inversion problem that minimizes the difference between the observed and the predicted relative gene expressions as well as the chronic toxicity data. A realistic case study demonstrates how chronic health risk of three halogenated aromatic hydrocarbons can be inferred from subchronic gene expression data. The advantages of the TPN are further demonstrated through two novel applications: Estimation of toxicological impact of new drugs and drug mixtures as well as rigorous determination of the optimal drug formulation to achieve maximum potency with minimum side-effects. Prediction of animal toxicity may be relevant for assessing risk for humans in the future.