Drug-drug interactions (DDIs) are a significant clinical and public health burden, especially for older adults. This burden will increase further as the population ages and the degree of polypharmacy increases. Knowledge about DDIs comes from many sources, with rigorous population-based studies of clinical outcomes providing the most clinically useful information. Because very few population DDI studies have been performed, there is profound disagreement among the DDI compendia about which drug pairs cause clinically important DDIs. Further, uncertainty about which potential DDIs are clinically important has hampered efforts to mitigate the effects of DDIs, such as computerized decision support. Population studies of the clinical effects of DDIs are therefore badly needed, and should be integrated with research on the biological basis of DDIs. In this renewal application, we propose to extend our approach to population DDI studies in two important and innovative ways: 1) by actively and explicitly incorporating mechanistic considerations;and 2) by implementing a sequential approach of initially identifying and quantifying risk, then, where warranted based on this step, characterizing important DDIs further, with the goal of providing the basis for future efforts to mitigate risk. Based on an extensive and structured consultative process to identify research foci, we propose to conduct a series of population DDI studies using a large 5-state Medicaid/Medicare dataset. These studies will examine hypothesized interactions involving lipid lowering drugs, anti-clotting drugs, and oral anti-diabetic drugs, three drug classes with enormous relevance for older adults, and with high potential for causing clinically important DDIs. Thus, we will study potential DDIs of great clinical and public health importance, especially to older adults, and in the process develop data that will help elucidate the mechanisms of these potential DDIs.

Public Health Relevance

Drug-drug interactions (DDIs) are a significant clinical and public health burden, especially for older adults. However, little is known about the clinical importance of most potential DDIs. We will conduct large population studies of the clinical importance of potential DDIs affecting lipid lowering, anti-clotting, and anti-diabetic therapies, addressing potential DDIs of great clinical and public health importance.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG025152-08
Application #
8658349
Study Section
Neurological, Aging and Musculoskeletal Epidemiology (NAME)
Program Officer
Salive, Marcel
Project Start
2004-12-01
Project End
2017-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
8
Fiscal Year
2014
Total Cost
$471,679
Indirect Cost
$171,574
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Leonard, Charles E; Hennessy, Sean; Han, Xu et al. (2017) Pro- and Antiarrhythmic Actions of Sulfonylureas: Mechanistic and Clinical Evidence. Trends Endocrinol Metab 28:561-586
Acton, Emily K; Leonard, Charles E; Bilker, Warren B et al. (2017) Lost in Translation: No Effect of a High-Profile Publication on the Concomitant Use of Interacting Drugs. Clin Transl Sci 10:426-430
Ji, Xinyao; Small, Dylan S; Leonard, Charles E et al. (2017) The Trend-in-trend Research Design for Causal Inference. Epidemiology 28:529-536
Leonard, Charles E; Brensinger, Colleen M; Bilker, Warren B et al. (2017) Gastrointestinal bleeding and intracranial hemorrhage in concomitant users of warfarin and antihyperlipidemics. Int J Cardiol 228:761-770
Leonard, Charles E; Brensinger, Colleen M; Bilker, Warren B et al. (2017) Thromboembolic and neurologic sequelae of discontinuation of an antihyperlipidemic drug during ongoing warfarin therapy. Sci Rep 7:18037
Han, Xu; Chiang, ChienWei; Leonard, Charles E et al. (2017) Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues. Epidemiology 28:459-468
Li, L (2017) Precision Medicine in Pharmacometrics and Systems Pharmacology. CPT Pharmacometrics Syst Pharmacol 6:151-152
Leonard, Charles E; Brensinger, Colleen M; Nam, Young Hee et al. (2017) The quality of Medicaid and Medicare data obtained from CMS and its contractors: implications for pharmacoepidemiology. BMC Health Serv Res 17:304
Huang, Kun; Liu, Yunlong; Huang, Yufei et al. (2016) Intelligent biology and medicine in 2015: advancing interdisciplinary education, collaboration, and data science. BMC Genomics 17 Suppl 7:524
Hennessy, S; Leonard, C E; Gagne, J J et al. (2016) Pharmacoepidemiologic Methods for Studying the Health Effects of Drug-Drug Interactions. Clin Pharmacol Ther 99:92-100

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