PROJECT SUMMARY / ABSTRACT This application for a Mentored Research Scientist Development Award (K01) is submitted by Katsiaryna Bykov, PharmD, ScD, in response to PA-20-190. Dr. Bykov is an Associate Epidemiologist and Instructor in Medicine in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital and Harvard Medical School. Her long-term goal is to generate clinically actionable evidence to guide the safe and effective use of medications in older adults in the complex milieu of polypharmacy and multimorbidity. Her recent research has focused on developing innovative methods for the detection and evaluation of drug-drug interactions and their clinical impact using electronic healthcare data. Dr. Bykov aims to acquire expertise in clinical geriatrics and diabetology, geriatric pharmacoepidemiology, and advanced statistical methods for data mining and signal detection to translate her methodological work into clinically relevant research. To achieve her aims, Dr. Bykov proposes a 5-year program of career development and mentored research centered on the development of a valid and efficient system for the detection and evaluation of drug-drug interactions in older adults with diabetes. Within the highly productive and supportive research environment of the Division of Pharmacoepidemiology, Dr. Bykov will work with an interdisciplinary team of mentors and collaborators drawn from across institutions at Harvard and University of Pennsylvania who have deep expertise and national/international reputations in the specific substantive areas of her proposed training: clinical geriatrics and diabetology, advanced statistical methods for data mining and signal detection, drug-drug interactions research, and pharmacoepidemiology. The overarching objective of the proposal is to develop a framework for the detection and evaluation of clinically relevant drug-drug interactions in older adults with diabetes. Dr. Bykov proposes a novel 2-stage screening that will identify potentially interacting drugs in electronic healthcare data and will be followed by a rigorous hypothesis-driven evaluation of identified interactions. The approach will use Medicare claims data, available in the Division and linked to electronic health records and outpatient laboratory test results for a subset of patients, and will be specifically tailored to address the heterogeneity and complexity of health status in older adults, including varying degrees of frailty and multimorbidity. The proposed work will provide high quality and clinically relevant evidence that will help older adults with diabetes and their clinicians to more fully assess the benefits and risks of adding a new medication in the presence of other medications and co-morbidities. The knowledge gained from this work will impact several million older adults with diabetes in the US and will provide the applicant with a solid background to become an independent investigator and, ultimately...