Session 1 Chair: Stephen Edwards
Stephen Edwards is a Bioinformatics Senior Scientist within the Research Computing Division at RTI International
Bioinformatics Senior Scientist
Predictive toxicology requires a large amount of data spanning multiple levels of biological organization from molecular to cellular through to organ and organism-level effects. Fortunately, the fundamental understanding of the mechanisms underlying disease can draw from research performed outside of the toxicology community. In 2015, the U.S. government launched a precision medicine initiative to account for people’s genes, environment, and lifestyles when diagnosing and treating disease. This initiative has spawned an immense amount of research in this area including the Trans-Omics for Precision Medicine (TOPMed - https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program) and All of Us (https://allofus.nih.gov) research programs funded by the National Institutes of Health (NIH). In parallel, multiple large studies funded by the NIH such as Children's Health Exposure Analysis Resource (CHEAR - https://chearprogram.org/) and Environmental Influences on Child Health Outcomes (ECHO - http://www.echochildren.org) have focused on environmental exposures that impact children’s health. Recognizing the need to integrate data across the many different studies, NIH has also funded several initiatives such as the Data Translator (https://ncats.nih.gov/translator/) and Data Commons (https://commonfund.nih.gov/commons) programs to assemble these data and make them available to researchers for further study. Data from these large human studies could provide unprecedented data to inform the field of predictive toxicology if we learn how to access and merge it with the wealth of data traditionally used for predictive toxicology including in vitro and in vivo laboratory data and epidemiology results. This session will examine recent work in precision medicine to initiate a broader discussion regarding how this information could be used to inform predictive toxicology.