Advancing Regulatory Science, Precision Medicine and Translational Network Science through the University of Rochester Clinical and Translational Science Institute Optional Cores

Poster
Image
Abstract

Regulatory Science is defined by the US Food and Drug Administration (FDA) as “the science of developing new tools, standards, and approaches to assess the safety, efficacy, quality and performance of FDA-regulated products” that ultimately enhances the overall translational research process and improves the development of safe and effective medical interventions.  Precision medicine holds tremendous promise to utilize genomic, environmental, lifestyle, and range of other health-related measures to more effectively develop and target treatments to individuals most likely to have a benefit (while limiting adverse events). At the same time, there are a number of regulatory science challenges to effectively develop and utilize personalized medicine tools and methods.  The University of Rochester Clinical and Translational Science Institute (UR-CTSI) established the Regulatory Science to Advance Precision Medicine Core to better prepare the UR-CTSI, CTSA Consortium, FDA and NIH to address key challenges in Regulatory Science, to ultimately speed the translation of research to advance precision medicine.  The Core established a working group under the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) network to help identify key topics for an annual Forum in DC, held in partnership with the PhRMA Foundation.   The Forum provides an interactive format for leading experts across government, academia, industry and foundations to further evaluate these key topics to identify regulatory science gaps and specific regulatory considerations, prioritize these needs and initially explore approaches to address these gaps.  The Forum has addressed three topics so far:  1) technologies and approaches that integrate and analyze genomic, proteomic, metabolomic, and/or epigenetic data for precision medicine, 2) additive manufacturing of medical products, and 3) digital health (particularly sensors and methods used to collect, transmit and analyze digital biomarker data).  Two subsequent publications have prioritized regulatory science gaps and proposed recommendations for the first two topics, while a third digital health manuscript is under development. 

 

Translational Network Science is the use of network analytics and graph theory to address translational barriers.  Network science has been used to analyze social networks, collaborations between translational researchers, identify gene regulatory networks, and analyze metabolic flux networks.  These methods are related to unsupervised clustering, classification, and machine learning methods.  The UR CTSI established the Translational Network Science Optional Core to apply these methods to problems of translational science.  During the last 3 years, we have focused on four areas.  (1) We mapped patient journeys for 40 million Medicare patients through ~800,000 Medicare providers over a single year for the entire United States, demonstrating that such trace-route mapping methods could be used to identify patterns of patient flow and co-management in any health system; (2) We have developed a new method for quantifying the risk of Clostridium difficile infections using a hospital flow network, contagion centrality, which is a function of patient transfers from hospital units with infected patients;  (3) We also developed a novel method of analyzing prescribing variation for very large provider data sets and used network-based dimension reduction methods to analyze regional variation prescribing for ~270,000 high volume Medicare Part D prescribers; and (4) We are using network science methods to analyze early stages of teaming and collaboration at Un-meetings using sociometric badges, in collaboration with the MIT Media Lab.  These efforts have resulted in 3 publications, and 3 manuscripts in preparation.