Prevalence and types of inconsistencies in clinical pharmacogenetic recommendations among major U.S. sources

Clinical implementation of pharmacogenomics (PGx) is slow. Previous studies have identified some inconsistencies among clinical PGx recommendations, but the prevalence and types of inconsistencies have not been comprehensively analyzed among major PGx guidance sources in the U.S. PGx recommendations from the Clinical Pharmacogenetics Implementation Consortium, U.S. Food and Drug Administration drug labels, and major U.S. professional medical organizations were analyzed through May 24, 2019. Inconsistencies were analyzed within the following elements: recommendation category; whether routine screening was recommended; and the specific biomarkers, variants, and patient groups involved. We identified 606 total clinical PGx recommendations, which contained 267 unique drugs. Composite inconsistencies occurred in 48.1% of clinical PGx recommendations overall, and in 93.3% of recommendations from three sources. Inconsistencies occurred in the recommendation category (29.8%), the patient group (35.4%), and routine screening (15.2%). In conclusion, almost one-half of clinical PGx recommendations from prominent U.S. guidance sources contain inconsistencies, which can potentially slow clinical implementation

Race and beta-blocker survival benefit in patients with heart failure: an investigation of self-reported race and proportion of African genetic ancestry

BACKGROUND: It remains unclear whether beta-blockade is similarly effective in black patients with heart failure and reduced ejection fraction as in white patients, but self-reported race is a complex social construct with both biological and environmental components. The objective of this study was to compare the reduction in mortality associated with beta-blocker exposure in heart failure and reduced ejection fraction patients by both self-reported race and by proportion African genetic ancestry. METHODS AND RESULTS: Insured patients with heart failure and reduced ejection fraction (n=1122) were included in a prospective registry at Henry Ford Health System. This included 575 self-reported blacks (129 deaths, 22%) and 547 self-reported whites (126 deaths, 23%) followed for a median 3.0 years. Beta-blocker exposure (BBexp) was calculated from pharmacy claims, and the proportion of African genetic ancestry was determined from genome-wide array data. Time-dependent Cox proportional hazards regression was used to separately test the association of BBexp with all-cause mortality by self-reported race or by proportion of African genetic ancestry. Both sets of models were evaluated unadjusted and then adjusted for baseline risk factors and beta-blocker propensity score. BBexp effect estimates were protective and of similar magnitude both by self-reported race and by African genetic ancestry (adjusted hazard ratio=0.56 in blacks and adjusted hazard ratio=0.48 in whites). The tests for interactions with BBexp for both self-reported race and for African genetic ancestry were not statistically significant in any model (P\u3e0.1 for all). CONCLUSIONS: Among black and white patients with heart failure and reduced ejection fraction, reduction in all-cause mortality associated with BBexp was similar, regardless of self-reported race or proportion African genetic ancestry

Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for <i>CYP2D6, ADRB1, ADRB2, ADRA2C, GRK4</i>, and <i>GRK5 </i>Genotypes and Beta-Blocker Therapy

Beta-blockers are widely used medications for a variety of indications, including heart failure, myocardial infarction, cardiac arrhythmias, and hypertension. Genetic variability in pharmacokinetic (e.g., CYP2D6) and pharmacodynamic (e.g., ADRB1, ADRB2, ADRA2C, GRK4, GRK5) genes have been studied in relation to beta-blocker exposure and response. We searched and summarized the strength of the evidence linking beta-blocker exposure and response with the six genes listed above. The level of evidence was high for associations between CYP2D6 genetic variation and both metoprolol exposure and heart rate response. Evidence indicates that CYP2D6 poor metabolizers experience clinically significant greater exposure and lower heart rate in response to metoprolol compared with those who are not poor metabolizers. Therefore, we provide therapeutic recommendations regarding genetically predicted CYP2D6 metabolizer status and metoprolol therapy. However, there was insufficient evidence to make therapeutic recommendations for CYP2D6 and other beta-blockers or for any beta-blocker and the other five genes evaluated (updates at www.cpicpgx.org).</p

Comparison of clinical pharmacogenetic recommendations across therapeutic areas

Objectives: Evaluations from pharmacogenetics implementation programs at major US medical centers have reported variability in the clinical adoption of pharmacogenetics across therapeutic areas. A potential cause for this variability may involve therapeutic area-specific differences in published pharmacogenetics recommendations to clinicians. To date, however, the potential for differences in clinical pharmacogenetics recommendations by therapeutic areas from prominent US guidance sources has not been assessed. Accordingly, our objective was to comprehensively compare essential elements from clinical pharmacogenetics recommendations contained within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and clinical practice guidelines from US professional medical organizations across therapeutic areas. Methods: We analyzed clinical pharmacogenetics recommendation elements within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and professional clinical practice guidelines through 05/24/19. Results: We identified 606 unique clinical pharmacogenetics recommendations, with the most recommendations involving oncology (217 recommendations), hematology (79), psychiatry (65), cardiovascular (43) and anesthetic (37) medications. Within our analyses, we observed considerable variability across therapeutic areas within the following essential pharmacogenetics recommendation elements: the recommended clinical management strategy; the relevant genetic biomarkers; the organizations providing pharmacogenetics recommendations; whether routine genetic screening was recommended; and the time since recommendations were published. Conclusions: On the basis of our results, we infer that observed differences in clinical pharmacogenetics recommendations across therapeutic areas may result from specific factors associated with individual disease states, the associated genetic biomarkers, and the characteristics of the organizations providing recommendations