10/23/2023
By Hsien-Yuan Hsu
Date: Wednesday, Oct. 25
Time: Noon to 1 p.m.
Location: Coburn Hall 275
Speaker: Rachel Melamed, Ph.D.
Topic: Discovering drug combinations affecting late onset disease
Abstract:
Combinations of common drugs may, when taken together, have unexpected effects on diseases like cancer. It is not feasible to test for all combination drug effects in clinical trials, but in the real world, drugs are frequently taken in combination. Then, undiscovered effects may protect users of drug combinations from cancer–or increase their risk. By analyzing massive health data containing numerous people exposed to drug combinations, we have an opportunity to discover these associations. Here we describe, apply, and evaluate a new approach for discovering drug combination effects on cancer using health data. Our approach builds on marginal structural model methods to emulate a randomized trial where one arm is assigned to take a drug alone, while the other arm takes that drug in combination with a second drug. This tactic allows us to systematically perform cohort studies estimating effects of over 9,000 drug combinations on all common cancer types, using claims data covering more than 100 million people. But, because discovery of associations from observational data is always prone to confounding, we develop a number of strategies to distinguish confounding from biomedically relevant findings. We describe a robustly supported beneficial drug combination that may synergistically impact lipid levels to reduce risk of cancer. These findings can suggest new clinical uses for drug combinations to prevent or treat cancer. Our approach can be adapted to mine electronic health records for interactive effects on other late onset common diseases.
Rachel Melamed's Bio:
Rachel Melamed is an Assistant Professor in the Department of Biological Sciences at UMass Lowell. Melamed’s experience is interdisciplinary, with a focus on biomedical data science. The main goal of Melamed’s lab is to mine hidden causes of chronic disease using this data, and to discover new uses for existing drugs to treat these diseases. To this end, her lab analyzes both genomic data and health records. She collaborates both with clinical and epidemiology researchers as well as computational researchers to develop novel applications of recent advances in machine learning. Melamed’s research has been supported by the NIH Big Data 2 Knowledge career development award, as well as the NIGMS Maximizing Investigators Research Activity award.