DESCRIPTION (provided by applicant): This is a new submission for the Translational Scholar Career Awards in Pharmacogenomics and Personalized medicine (K23) for Andrew A. Monte, MD. Dr. Monte's overall career goal is to become a leading independent researcher in personalized medicine. His innovative research proposal integrates clinical, genomic, cytochrome metabolism phenotype, and metabolomic factors to develop a model that predicts systolic blood pressure decline due to metoprolol. Candidate: Dr. Monte is a board-certified Emergency Medicine & Medical Toxicology physician, Assistant Professor of Emergency Medicine & Medical Toxicology at the University of Colorado Denver (UCD) School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences. Training: The proposed career development plan augments Dr. Monte's prior training and experience to focus on specific, advanced training in: (1) pharmacogenomics methods; (2) pharmacokinetic analyses; (3) metabolomic analyses; and (4) predictive model building. Dr. Monte proposes to acquire these skills through intensive focused mentorship, relevant advanced coursework, and guided research. These training activities will help Dr. Monte gain the necessary skills to become a leader in personalized medicine. Mentors/Environment: Dr. Monte has established a close working relationship with his primary mentor, Dr. Vasilis Vasiliou, a leading researcher in genomics with a long track record of mentoring. His co-mentor, Dr. Richard M. Weinshilboum, is a national expert and researcher on drug metabolizing enzymes. In addition, an advisory panel of experts in pharmacokinetics, metabolomics, model building, and research career development will monitor Dr. Monte's progress and providing additional topical guidance. The Department of Emergency Medicine is committed to supporting Dr. Monte's growth into an independent researcher, including a commitment of protected time and resources for this career development award. In addition, UCD provides Dr. Monte with a rich research environment, including access to resources and collaborators at the CCTSI. Research: Dr. Monte's research proposal gathers clinical, genomic (Aim 1), cytochrome (CYP) metabolism phenotype (Aim 2), and metabolomic data (Aim 3) in a prospective trial of a patients beginning metoprolol therapy for uncontrolled hypertension. These factors will be compared to identify which variables predict the drug response to metoprolol. These data will be integrated into a model to predict systolic blood pressure reduction due to metoprolol. Summary: This innovative approach integrates clinical, genomic, CYP metabolism phenotype, and metabolomic data to predict the drug response of metoprolol. The integrative approach can be applied to other diseases and therapeutics to improve drug efficacy and safety. The training plan and research proposal will help Dr. Monte develop into an internationally-recognized independent investigator in personalized medicine.

PUBLIC HEALTH RELEVANCE: There has been an explosion on pharmacogenomic, pharmacokinetic, and clinical data associated with the efficacy of prescribed drugs. Unfortunately, this data has not resulted in widespread success of personalized medicine; defined as the right drug, at the right dose, in the right patient. An approach that integrates and accounts for all of these factors together is more likely to efficiently predict the clinical drug effect. This integrated approach could be applied to numerous drugs increasing the efficacy and safety of prescriptions. Metoprolol is a drug used to treat hypertension and represents the first drug with enough pharmacogenomic, pharmacokinetic, and clinical data to build an integrated model. The overarching objective of this career development award is to support the development of Dr. Andrew Monte, MD, into a leader in personalized medicine. He aims to coordinate a clinical trial that captures genomic, pharmacokinetic, metabolomic, and efficacy data to build and integrated model that predicts metoprolol efficacy for the treatment of hypertension.