Citations 2
The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems
Luzum, Jasmine A. , Pakyz, R E, Elsey, AR, Haidar, Cyrine E. , Peterson, JF, Whirl‐Carrillo, Michelle , Handelman, SK, Palmer, Kathleen , Pulley, JM, Beller, Michael, Schildcrout, JS, Field, JR, Weitzel, KW, Cooper‐DeHoff, RM, Cavallari, Larisa H. , O'Donnell, PH, Altman, Russ B. , Pereira, Naveen L. , Ratain, MJ, Roden, DM, Embi, PJ, Klein, Teri E. , Johnson, JA, Relling, MV, Wang, L, Weinshilboum, RM, Freimuth, Robert R. , Johnson, Julie A. , Peterson, Josh F. , Schildcrout, Jonathan S. , Elsey, Amanda R., Peterson, JF, Weitzel, KW, Cooper-DeHoff, RM, Ratain, Mark J. , Roden, DM, Sadée, Wolfgang , Relling, Mary V. , Shuldiner, Alan R. , Schildcrout, Jonathan S , Whirl-Carrillo, Michelle , Freimuth, Robert R , Peterson, Josh F , Cavallari, Larisa H , Luzum, Jasmine A , Klein, Teri E
Clinical Pharmacology & Therapeutics | January 16, 2017 | Volume: 102 | Issue: 3 | Pages: 502-510
Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real‐world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene–drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.
Melatonin Pathway and Atenolol‐Related Glucose Dysregulation: Is There a Correlation?
McDonough, Caitrin W. , LANGAEE, T, Kenari, N Nasiri, Beitelshees, AL, Gums, John G. , Chapman, AB, Turner, ST, Johnson, JA, Cooper‐DeHoff, RM, Johnson, Julie A. , Beitelshees, Amber L. , Beitelshees, Amber L. , Chang, SW, Gong, Yan , Cooper-DeHoff, RM
Clinical and Translational Science | March 7, 2016 | Volume: 9 | Issue: 2 | Pages: 114-122
Lower melatonin level, melatonin receptor gene variations, and atenolol treatment are associated with glucose dysregulation. We investigated whether atenolol‐related glucose and melatonin changes are correlated, and whether single nucleotide polymorphisms (SNPs) in melatonin candidate genes contribute to interindividual variation in glucose change. Hypertensive Caucasians (n = 232) from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study treated with atenolol for 9 weeks were studied. Urinary 6‐sulfatoxymelatonin (aMT6s) was measured pre‐ and posttreatment and normalized to urinary creatinine. Pharmacogenetic effects on glucose change of 160 SNPs in 16 melatonin candidate genes were assessed with multiple linear regression. Atenolol was associated with increased glucose (1.8 ± 10.1mg/dl, P = 0.02) and decreased aMT6s (–4.5 ± 10.1 ng/mg, P < 0.0001). However, the aMT6s change was not correlated with post‐atenolol glucose change. SNP rs11649514 in PRKCB was associated with glucose change (P = 1.0×10−4). PRKCB is involved in the melatonin‐insulin regulatory pathway, and may be important in mediating clinically meaningful atenolol‐related hyperglycemia.