Stephen Edwards has more than 25 years of experience in pharmacology and toxicology. His expertise includes data mining, the design of web-accessible knowledgebases, and ontology-based modeling of biological information. As a senior bioinformatics scientist at RTI, he focuses on integrating data from genetics, epidemiology, toxicology, and clinical studies to define disease causes and identify interventions that could improve human health and well-being.
Dr. Edwards’ research examines the combined impact of genetic and environmental factors on disease manifestation to better support precision medicine and public health protection. This work is built upon the adverse outcome pathway (AOP) framework initially proposed by the U.S. Environmental Protection Agency (EPA) to standardize the description of mechanisms of toxicity. While most applications have focused on chemical toxicity, this framework has broad potential to integrate data on genetic and environmental stressors (including pathogens such as COVID-19) by incorporating information from in vitro assays, laboratory animals, and human studies. He collaborates with colleagues worldwide on the continued development of an AOP knowledgebase to house AOP descriptions in a central repository. Dr. Edwards has several projects supported by the National Institutes of Health, EPA, and other funders to formalize the semantics used to describe AOPs, integrate biomedical and environmental data, incorporate systematic review methods into the AOP development process, and effectively utilize AOP information for a broad range of applications. He has more than 70 peer-reviewed publications and was the chief architect of the Adverse Outcome Pathway Wiki.
Dr. Edwards received his bachelor of science in chemistry from the University of North Carolina at Chapel Hill and his doctorate in pharmacology from Vanderbilt University Medical Center. Prior to joining RTI in 2017, Dr. Edwards worked for the EPA. He used computational approaches to describe the mechanisms by which chemicals cause disease and thereby aid the interpretation of high-throughput toxicity test results. Before that, he worked in the pharmaceutical industry, where he led a target discovery team focused on identifying potential diabetes targets for the Merck high throughput screening program.