Nonsteroidal anti-inflammatory drugs (NSAIDs) are consumed by tens of millions worldwide. Although they relieve pain and inflammation, we understand poorly their mechanism of action. They also cause serious gastrointestinal and cardiovascular adverse effects and are thought to have caused thousands of deaths. Despite enrolling more than 100,000 patients in randomized trials, we still do not know the NSAID of choice for patients with arthritis and heart disease or if NSAIDs differ in clinical efficacy. Here we propose a paradigm shifting, strategic approach to harvest benefit and manage risk by personalizing therapy with NSAIDs. Data from studies from yeast, mammalian cells, zebrafish, mice and humans will be integrated to develop signaling networks that reflect perturbation by model NSAIDs and that generate hypotheses ultimately addressed by prospective, randomized trials in humans. Our hope is that these iteratively refined models, progressively informed by human data, will lead to algorithms of incremental value to clinicians in the prediction of efficacy and adverse effects. This interdisciplinary strategy will deliver innovative tools and technologies, quantitative models and biomarkers of drug response and if successful will allow more rational prescription of NSAIDs to minimize risk and maximize benefit to individuals, creating a novel paradigm for the development and approval of drugs, the design of randomized trials and the treatment of chronic disease.

Public Health Relevance

Drugs are prescribed based on detection of large average signals of effectiveness and hazard. This proposal attempts to refine the use of nonsteroidal anti-inflammatory drugs so that they are used in patients individually most likely to benefit and least likely to suffer adverse effects.

National Institute of Health (NIH)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Srinivas, Pothur R
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
Schools of Medicine
United States
Zip Code
Martin, Sarah A; Gijón, Miguel A; Voelker, Dennis R et al. (2014) Measurement of lysophospholipid acyltransferase activities using substrate competition. J Lipid Res 55:782-91
Keramati, Ali R; Fathzadeh, Mohsen; Go, Gwang-Woong et al. (2014) A form of the metabolic syndrome associated with mutations in DYRK1B. N Engl J Med 370:1909-19
Chakraborty, Raja; Bhullar, Rajinder P; Dakshinamurti, Shyamala et al. (2014) Inverse agonism of SQ 29,548 and Ramatroban on Thromboxane A2 receptor. PLoS One 9:e85937
Zarini, Simona; Hankin, Joseph A; Murphy, Robert C et al. (2014) Lysophospholipid acyltransferases and eicosanoid biosynthesis in zebrafish myeloid cells. Prostaglandins Other Lipid Mediat 113-115:52-61
Obinata, Hideru; Gutkind, Sarah; Stitham, Jeremiah et al. (2014) Individual variation of human S1P? coding sequence leads to heterogeneity in receptor function and drug interactions. J Lipid Res 55:2665-75
Toung, Jonathan M; Lahens, Nicholas; Hogenesch, John B et al. (2014) Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing. PLoS One 9:e112040
Jiang, Yan; Djuric, Zora; Sen, Ananda et al. (2014) Biomarkers for personalizing omega-3 fatty acid dosing. Cancer Prev Res (Phila) 7:1011-22
Tang, Wai Ho; Stitham, Jeremiah; Jin, Yu et al. (2014) Aldose reductase-mediated phosphorylation of p53 leads to mitochondrial dysfunction and damage in diabetic platelets. Circulation 129:1598-609
Lu, Yi-Chien; Chang, Sung-Hee; Hafner, Markus et al. (2014) ELAVL1 modulates transcriptome-wide miRNA binding in murine macrophages. Cell Rep 9:2330-43
O'Donnell, Valerie B; Murphy, Robert C; Watson, Steve P (2014) Platelet lipidomics: modern day perspective on lipid discovery and characterization in platelets. Circ Res 114:1185-203

Showing the most recent 10 out of 19 publications