TITLE: An Analytics Approach to Designing Drug Therapies for Cancer

ABSTRACT:

Worldwide, cancer is a leading cause of death, and metastatic breast cancer (MBC) is the top cause of cancer death among women. We present a data-driven approach to planning clinical trials and designing novel drug therapies for metastatic breast cancer. First, we describe construction of a large database of MBC clinical trial results and tools to help clinicians visualize the data. Next, we use statistical models to predict efficacy and toxicity outcomes of trials before they are run, with implications for selecting between multiple drug therapies for testing. Finally, we use optimization models to design novel therapies that strike a balance between improving patient outcomes and learning about new drugs; we present evidence that these models may improve trial outcomes compared to current practice.

BIO

John Silberholz is currently a Lecturer at MIT Sloan.  He received his PhD in 2015 from the MIT Operations Research Center, and a BS in computer science and a BS in mathematics from the University of Maryland. John's research interests lie in healthcare analytics - broadly construed - with a current focus on applications to cancer screening and treatment.  John is a recipient of the William Pierskalla Best Paper Award (2013) for the top healthcare management science paper, the INFORMS Undergraduate Operations Research Prize, and an NSF Graduate Research Fellowship.