Pharmacotherapy
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The topic of precision medicine is increasingly more prevalent in the general medical literature, with oncology research leading the way. Many factors, such as availability of targeted drugs, advances in laboratory science, and improved information systems, converged to make precision medicine research possible on a large scale at the National Cancer Institute. The resultant big data will spur new kinds of research in the decades to come, but until then, all clinicians are challenged to make sense of an overabundance of information when managing individual patients.
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Interindividual variability in response to 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, or statins, with regard to both efficacy and safety is an obvious target for pharmacogenetic research. Many genes have been identified as possible contributors to variability in statin response and safety. Genetic polymorphisms may alter the structure or expression of coded proteins, with potential impacts on lipid and statin absorption, distribution, metabolism, and elimination as well as response pathways related to the pharmacologic effect. ⋯ In this review article, we provide a statin-based summary of available evidence describing pharmacogenetic associations that may be of clinical relevance in the future. Although currently available studies are often small or retrospective, and may have conflicting results, they may be useful in providing direction for future confirmatory studies and may point to associations that could be confirmed in the future when more patient outcomes-based studies are available. We also summarize the clinically relevant evidence currently available to assist clinicians with providing personalized pharmacotherapy for patients requiring statin therapy.
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Despite advances in technology and guidelines from the Clinical Pharmacogenetics Implementation Consortium (CPIC) that focus on how to use pharmacogene test results, hurdles remain that have delayed the widespread application of pharmacogenomics in clinical practice. These hurdles include a lack of prospective randomized controlled trials to address the utility of pharmacogenomics on clinical outcomes, what the clinical algorithm for pharmacogenomics should be, and whether pharmacogenomics is cost-effective. However, the implementation of clinical practice guidelines, such as those from professional organizations, is commonplace and often termed the application of evidence-based medicine. ⋯ Food and Drug Administration-approved labeling recommendations and the evidence supporting recommendations from CPIC. Although many clinical practice guideline recommendations are supported by the results of randomized controlled clinical trials, we cite examples of common clinical practices that are supported by levels and types of evidence similar to the evidence supporting many of the CPIC recommendations. Specifically, we discuss clinical recommendations for guidance related to drug-drug interactions, drug-gene interactions, therapeutic range selection, and dosage adjustments based on patient-specific factors within the context of a select set of cardiovascular therapeutic topics.
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Opioid analgesics are the standards of care for the treatment of moderate to severe nociceptive pain, particularly in the setting of cancer and surgery. Their analgesic properties mainly emanate from stimulation of the μ receptors, which are encoded by the OPRM1 gene. Hepatic metabolism represents the major route of elimination, which, for some opioids, namely codeine and tramadol, is necessary for their bioactivation into more potent analgesics. ⋯ The Clinical Pharmacogenetics Implementation Consortium guidelines provide CYP2D6-guided therapeutic recommendations to individualize treatment with tramadol and codeine. However, implementation guidelines for other opioids, which are more commonly used in real-world settings for pain management, are currently lacking. Hence, further studies are warranted to bridge this gap in our knowledge base and ultimately ascertain the role of pharmacogenetic markers as predictors of response to opioid analgesics.