Speaker: S. Joshua Swamidass, MD PhD, Associate Professor in the Laboratory and Genomic Medicine Division, Department of Pathology and Immunology, Washington University in St. Louis. Faculty Lead in Translational Bioinformatics, Institute for Informatics, Washington University in St. Louis.

Date: Thursday, April 12, 2018

Time: 03:00pm – 04:15pm

Location:  Klaus Advanced Computing Building, Room 2443

Abstract: Many medicines become toxic only after bioactivation by metabolizing enzymes. Often, metabolic enzymes transformed them into chemically reactive species, which subsequently conjugate to proteins and cause adverse events. For example, carbamazepine is epoxidized by P450 enzymes in the liver, but then conjugates to proteins, causing Steven Johnsons Syndrome in some patients. The most difficult to predict drug reactions, idiosyncratic adverse drug reactions (IADRs), often depend on bioactivation. Our group has been using deep learning to model the metabolism of diverse chemicals, and the subsequent reactivity of their metabolites. Deep learning systematically summarizes the information from thousands of publications into quantitative models of bioactivation, modeling precisely how medicines are modified by metabolic enzymes. These models are giving deeper understanding of why some drugs become toxic, and others do not. At the same time, deep learning can be used to understand drug toxicity as it arises in clinical data, and why some patients are affected, but not others. A conversation between the basic and clinical sciences is now possible, where patient outcomes can be understood in light of bioactivation mechanisms, and these mechanisms can explain why some patients are susceptible to drug toxicity, and others are not.

Bio: Dr. S Joshua Swamidass MD PhD is an Associate Professor of Laboratory and Genomic Medicine at Washington University School of Medicine (http://swami.wustl.edu). He is also the Faculty Lead for Translational Bioinformatics at the Institute for Informatics. His group studies information with new computational methods, at the intersection of biology, medicine and chemistry. He is funded by the National Library Medicine (R01LM012482 and R01LM012222) to model bioactivation pathways, and how bioactivation pathways change in children.