We propose to establish a Center of Excellence in Regulatory Sciences and Innovation, UCSF-Stanford CERSI, which brings together a world-class team of scientists from two academic institutions with partners at the FDA. The goal of the UCSF-Stanford CERSI is to advance regulatory sciences through the development and application of quantitative and systems level methodologies. Building on strengths of UCSF and Stanford in quantitative sciences, UCSF-Stanford CERSI will provide education, exchange and collaborative research programs that are focused on three areas of FDA's regulatory science and innovation strategy: (i) Improving preclinical assessments of safety and efficacy; (ii) Improving clinical studies and evaluation; and (v) harnessing diverse data through information sciences. Two highly collaborative units, Education-Exchange Unit (ED-EX-U) and Collaborative Research Unit (RES-U), will form the cornerstones of the CERSI. The ED-EX-U will provide courses in regulatory sciences, building on UCSF's prominent certificate program in regulatory sciences, the American Course in Regulatory Sciences and Innovation (ACDRS). The ACDRS will be complemented by a selection of online short courses adapted from the rich and diverse curricula of UCSF and Stanford offered through several renowned graduate educational programs (e.g., UCSF Clinical and Translational Sciences Institute). The ED-EX-U will establish exchange programs for FDA and academic scientists along with an FDA internship program for students and postdocs. Collaborative pilot projects between FDA and academic scientists will be supported. The RES-U will focus on two collaborative research projects between FDA and academic scientists that involve the development and application of quantitative pharmacological methods to problems in regulatory sciences: (a) predicting drug induced weight gain, a major safety issue for many approved drugs, and (b) the development of the first comprehensive, data-driven, biomarker guided disease progression model of Multiple Sclerosis, which will facilitate drug evaluation and approval. The ED-EX-U and the RES-U will be managed by a dynamic core infrastructure unit (DCIU), which will include a collaborative FDA-academia Steering Committee and an Industrial Advisory Board. A website Virtual Home to communicate across the CERSI will be established. A major goal of the DCIU will be to develop a robust business model to provide continuing support for the CERSI. Commitments from both UCSF and Stanford, for funds and space will help jumpstart UCSF-Stanford CERSI. Sustainable funding will come from income generated by the ED-EX-U as well as contributions from the Bay Area biotechnology industry, which through the Industry Advisory Board will develop a sustainable business model for the CERSI. Public- private partnership with the FDA, academia and the industry will be explored. The UCSF-Stanford CERSI will provide a west coast presence of the FDA and greatly advance the sciences underlying drug approval.

Public Health Relevance

Through education, exchange and collaborative research programs, the proposed UCSF-Stanford CERSI will greatly advance the ability of the FDA to evaluate and approve safe and effective drugs. Research programs in the UCSF-Stanford CERSI will develop quantitative and computer-based methods for the evaluation of new drug products. Public-private partnerships, which involve west coast industry, academia and FDA, will be created to accelerate and improve the process of drug approval.

National Institute of Health (NIH)
Food and Drug Administration (FDA)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZFD1-SRC (99))
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University of California San Francisco
Schools of Pharmacy
San Francisco
United States
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Lo, Yu-Chen; Rensi, Stefano E; Torng, Wen et al. (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538-1546
Wilson, Jennifer L; Altman, Russ B (2018) Biomarkers: Delivering on the expectation of molecularly driven, quantitative health. Exp Biol Med (Maywood) 243:313-322
López-Pineda, Arturo; Rodríguez-Moran, Mario F; Álvarez-Aguilar, Cleto et al. (2018) Data mining of digitized health records in a resource-constrained setting reveals that timely immunophenotyping is associated with improved breast cancer outcomes. BMC Cancer 18:933
Wang, Bo; Pourshafeie, Armin; Zitnik, Marinka et al. (2018) Network enhancement as a general method to denoise weighted biological networks. Nat Commun 9:3108
Lavertu, Adam; McInnes, Greg; Daneshjou, Roxana et al. (2018) Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum Mol Genet 27:R72-R78
Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E et al. (2018) Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome. Pac Symp Biocomput 23:56-67
Cheung, Kit Wun Kathy; Hsueh, Chia-Hsiang; Zhao, Ping et al. (2017) The Effect of Uremic Solutes on the Organic Cation Transporter 2. J Pharm Sci 106:2551-2557
Hsueh, Chia-Hsiang; Yoshida, Kenta; Zhao, Ping et al. (2016) Identification and Quantitative Assessment of Uremic Solutes as Inhibitors of Renal Organic Anion Transporters, OAT1 and OAT3. Mol Pharm 13:3130-40
Hibma, Jennifer E; Zur, Arik A; Castro, Richard A et al. (2016) The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin. Clin Pharmacokinet 55:711-21
Yee, S W; Giacomini, M M; Hsueh, C-H et al. (2016) Metabolomic and Genome-wide Association Studies Reveal Potential Endogenous Biomarkers for OATP1B1. Clin Pharmacol Ther 100:524-536

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