Cell Chemical Biology
Volume 23, Issue 10, 20 October 2016, Pages 1294-1301
Journal home page for Cell Chemical Biology

Resource
A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

https://doi.org/10.1016/j.chembiol.2016.07.023Get rights and content
Under an Elsevier user license
open archive

Highlights

  • Computational approach predicts the likelihood of clinical trial toxicity

  • Identification of molecule and target properties associated with clinical toxicity

  • Development of a tool to facilitate interaction and interpretation of the model

Summary

Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar “moneyball” approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.

Cited by (0)

4

Lead Contact