Using Data Science to Forecast Clinical Trial Outcomes may Help Biomedical Stakeholders De-Risk their Portfolios

A new study published in the inaugural issue of the Harvard Data Science Review, by researchers from the Massachusetts Institute of Technology, applies machine-learning and statistical techniques to predict the outcomes of randomized clinical trials for new drug and device candidates. In addition to the publication, the software used in the study will be made publicly available with an open-source license here. The research is part of an ongoing collaboration between the MIT Laboratory for Financial Engineering (LFE) and Informa Pharma Intelligence, named Project ALPHA (Analytics for Life-sciences Professionals and Healthcare Advocates). Project ALPHA leverages Informa datasets from Citeline and machine learning to train and validate its predictive models, with the goal of providing timely and more accurate estimates of the risks and rewards of clinical trials to the entire biopharma ecosystem. The ultimate goal of the project is to help patients and their families by developing analytics that allow investors, biopharma professionals, regulators, and patient advocates to better manage the tremendous risks of drug and device development. “Everyone is affected by the risk of a drug failing in its clinical trial process,” says Andrew W. Lo, the study’s senior author and director of MIT’s LFE as well as a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Kien Wei Siah and Chi Heem Wong, two LFE/CSAIL Ph.D. students who co-authored the publication, observed that “You can’t manage what you don’t measure, so this is a new tool for measuring the risk of clinical trials more accurately, allowing all stakeholders to plan more effectively for these risks.”