Big data approach predicts drug toxicity in humans

Researchers can now predict the odds of experimental drugs succeeding in clinical trials, thanks to a new data-driven approach developed by Weill Cornell Medicine scientists. The method detects toxic side effects that may disqualify drugs from human use, giving drug developers an early warning before initiating clinical trials, according to a new study published Sept. 15 in Cell Chemical Biology. The approach upends conventional wisdom about the criteria on which to evaluate new drugs for their safety. Rather than exclusively examining molecular structure to determine viability, this new computational method combines a host of structural features and features related to how the drug binds to molecules in the body. 'We looked more broadly at drug molecule features that drug developers thought were unimportant in predicting drug safety in the past. Then we let the data speak for itself,' said author Dr. Olivier Elemento, an associate professor of physiology and biophysics and of computational genomics in computational biomedicine, associate director of the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, and head of the computational biology group at the Caryl and Israel Englander Institute for Precision Medicine.