A systems biology based integrative framework to enhance the predictivity of in vitro methods for drug-induced liver injury.

BACKGROUND: Liver injury is the most common cause of postmarketing withdrawal of drugs. Traditional animal toxicity testing methods have proved to be imperfect tools for predicting toxicity observed in the clinic.
OBJECTIVE: Predictive methods that integrate data and insights from several in vitro methods to provide a deeper understanding of the impact of a drug on the liver are the need of the hour.
METHOD: A systems approach based on mathematical modelling using the kinetics of biochemical pathways involved in liver homeostasis coupled with in vitro measurements to quantify drug-induced perturbations is described here.
CONCLUSIONS: Integrating in silico and in vitro methods provides a powerful platform that allows reasonably accurate and mechanistic-level prediction of drug-induced liver injury. The method demonstrates that several physiological situations can be accurately modelled as can the effect of perturbations induced by drugs. It can also be used along with high-throughput 'omic' data to generate testable hypotheses leading to informed decision-making.