ment option for a disease where few options existed. Based on studies reviewed, sildenafil and NAC treatments did not slow disease progression as measured by change in percent FVC and their use in IPF should be limited.
Statins, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitors, have proven efficacy in both lowering low-density-lipoprotein levels and preventing major coronary events, making them one of the most commonly prescribed drugs in the United States. Statins exhibit a class-wide side effect of muscle toxicity and weakness, which has led regulators to impose both dosage limitations and a recall. This review focuses on the best-characterized genetic factors associated with increased statin muscle concentrations, including the genes encoding cytochrome P450 enzymes (CYP2D6, CYP3A4, and CYP3A5), a mitochondrial enzyme (GATM), an influx transporter (SLCO1B1), and efflux transporters (ABCB1 and ABCG2). A systematic literature review was conducted to identify relevant research evaluating the significance of genetic variants predictive of altered statin concentrations and subsequent statin-related myopathy. Studies eligible for inclusion must have incorporated genotype information and must have associated it with some measure of myopathy, either creatine kinase levels or self-reported muscle aches and pains. After an initial review, focus was placed on seven genes that were adequately characterized to provide a substantive review: CYP2D6, CYP3A4, CYP3A5, GATM, SLCO1B1, ABCB1, and ABCG2. All statins were included in this review. Among the genetic factors evaluated, statin-related myopathy appears to be most strongly associated with variants in SLCO1B1.
Background-New anticoagulants may improve health outcomes in patients with atrial fibrillation, but it is unclear whether their use is cost-effective. Methods and Results-A Markov state transition was created to compare 4 therapies: dabigatran 150 mg BID, apixaban 5 mg BID, rivaroxaban 20 mg QD, and warfarin therapy. The population included those with newly diagnosed atrial fibrillation who were eligible for treatment with warfarin. Compared with warfarin, apixaban, rivaroxaban, and dabigatran, costs were $93 063, $111 465, and $140 557 per additional quality-adjusted life year gained, respectively. At a threshold of $100 000 per quality-adjusted life year, apixaban provided the greatest absolute benefit while still being cost-effective, although warfarin would be superior if apixaban was 2% less effective than expected. Although apixaban was the optimal strategy in our base case, in probabilistic sensitivity analysis, warfarin was optimal in an equal number of iterations at a cost-effectiveness threshold of $100 000 per quality-adjusted life year. Conclusions-While at a standard cost-effectiveness threshold of $100 000 per quality-adjusted life year, apixaban seems to be the optimal anticoagulation strategy; this finding is sensitive to assumptions about its efficacy and cost. In sensitivity analysis, warfarin seems to be the optimal choice in an equal number of simulations. As a result, although all the novel oral anticoagulants produce greater quality-adjusted life expectancy than warfarin, they may not represent good value for money.
Background: Genetic biomarkers that predict a drug's efficacy or likelihood of toxicity are assuming increasingly important roles in the personalization of pharmacotherapy, but there is concern that there may be insufficient evidence linking use of some biomarkers to clinical benefit. Nevertheless, information about the use of biomarkers appears in the drug label for many prescription medications. This may add confusion to the clinical decisionmaking process. Objective: To evaluate the evidence supporting pharmacogenomic biomarker testing in drug labels, how frequently testing is recommended, and completeness of citation of the supporting studies. Methods: We used publicly-available databases from the US Food and Drug Administration (FDA) to identify drug labels that described the use of a biomarker and evaluated whether the label contained or referenced convincing evidence of its clinical validity (i.e., the ability to predict phenotype) and clinical utility (i.e., the ability to improve clinical outcomes) using guidelines published by the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) group. We graded the completeness of the citation of supporting studies and determined whether the label recommended incorporation of biomarker test results in therapeutic decision-making. Results: Of the 119 drug-biomarker combinations, only 36% (n=43) had labels that provided convincing clinical validity evidence while 15% (n=18) provided convincing evidence for clinical utility. Fifty-one percent of the labels (51%, n=61) made recommendations based on the results of a biomarker test; 30% of these contained
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