Computational modelling predicts liver toxicity effects of 15 drugs

pharmafile | February 7, 2017 | News story | Research and Development modelling 

A team from Germany’s RWTH Aachen University, in collaboration with two researchers from Wellcome Genome Campus in the UK, have utilised computational modelling to document drug-induced toxicity levels caused by fifteen hepatotoxic drugs.

The study A Comparative Analysis of Drug-Induced Hepatotoxicity in Clinically Relevant Situations, published in PLOS Computational Biology, set out to better understand the molecular mechanisms behind liver damage as a result of drug use, a common and significant issue facing clinical care, to build better treatment and prevention methods.

To this end, the researchers utilised a computational modelling approach to mimic the effect of clinically-relevant doses of each drug on the body’s liver cells, combining experimental observation with established data on the body’s distribution and metabolisation of the drugs.

Utilising full-body models, the team was able to create predictive simulations for each drug and validate them through existing experimental data, before applying the models to collected laboratory data to predict the effects of each drug on the liver for each specific patient.

The method revealed several similar effects caused by the drugs, which could then be categorised into different groups based on patient response.

“Consistently applied to the design of clinical development programs, the approach presented has the potential to early identify medical and economic risks of new drugs,” co-author Lars Kuepfer explained.

The approach informs life science professionals of which genes would be transcribed in response to toxic doses, which could lead to a predictive model from which early signs of toxicity can be identified, and; it could also be applied to current and future drugs to determine which combinations can pose a toxic threat. The team was clear however that these simulations cannot be relied upon to consistently predict toxicity levels and effects in real world patients, and there is further work to be done to develop the approach.

Matt Fellows

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