NIH VideoCast - Translating from Chemistry to Clinic with Deep Learning

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Translating from Chemistry to Clinic with Deep Learning

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Air date:	Wednesday, March 7, 2018, 2:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 330 (130 Live, 200 On-demand)
Category:	Special
Runtime:	01:01:00
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	NLM Informatics and Data Science Lecture Series 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 Stevens Johnson Syndrome in some patients. The most difficult to predict drug reactions, idiosyncratic adverse drug reactions, 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, predicting exactly 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. S. Joshua Swamidass is an Assistant Professor of Laboratory and Genomic Medicine at Washington University School of Medicine (http://swami.wustl.edu). His group studies information with new computational methods, at the intersection of biology, medicine and chemistry. He is funded by the National Library Medicine (NLM) to model bioactivation pathways, and how bioactivation pathways change in children. Dr. Swamidass is currently on the NLM Biomedical Informatics, Library and Data Sciences Review Committee.

NLM Title:	Translating from chemistry to clinic with deep learning / S. Joshua Swamidass.
Author:	Swamidass, S Joshua. National Institutes of Health (U.S.),
Publisher:
Abstract:	(CIT): 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 Stevens Johnson Syndrome in some patients. The most difficult to predict drug reactions, idiosyncratic adverse drug reactions, 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, predicting exactly 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.
Subjects:	Drug-Related Side Effects and Adverse Reactions--metabolism Machine Learning Models, Theoretical Pharmaceutical Preparations--chemistry Pharmaceutical Preparations--metabolism Quantum Theory
Publication Types:	Lecture Webcast

NLM Classification:	QV 38
NLM ID:	101724465
CIT Live ID:	27195
Permanent link:	https://videocast.nih.gov/watch=27195

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