Pharmacogenomics implementation in cardiovascular disease in a highly diverse population: initial findings and lessons learned from a pilot study in United Arab Emirates.

BACKGROUND: Pharmacogenomic (PGx) testing has proved its utility and cost-effectiveness for some commonly prescribed cardiovascular disease (CVD) medications. In addition, PGx-guided dosing guidelines are now available for multiple CVD drugs, including clopidogrel, warfarin, and statins. The United Arab Emirates (UAE) population is diverse and multiethnic, with over 150 nationalities residing in the country. PGx-testing is not part of the standard of care in most global healthcare settings, including the UAE healthcare system. The first pharmacogenomic implementation clinical study in CVD has been approved recently, but multiple considerations needed evaluation before commencing. The current report appraises the PGx-clinical implementation procedure and the potential benefits of pursuing PGx-implementation initiatives in the UAE with global implications. METHODS: Patients prescribed one or more of the following drugs: clopidogrel, atorvastatin, rosuvastatin, and warfarin, were recruited. Genotyping selected genetic variants at genes interacting with the study drugs was performed by real-time PCR. RESULTS: For the current pilot study, 160 patients were recruited. The genotypes and inferred haplotypes, diplotypes, and predicted phenotypes revealed that 11.9% of the participants were poor CYP2C19 metabolizers, 35% intermediate metabolizers, 28.1% normal metabolizers, and 25% rapid or ultrarapid metabolizers. Notably, 46.9% of our cohort should receive a recommendation to avoid using clopidogrel or consider an alternative medication. Regarding warfarin, only 20% of the participants exhibited reference alleles at VKORC1-1639G > A, CYP2C9*2, and CYP2C9*3, leaving 80% with alternative genotypes at any of the two genes that can be integrated into the warfarin dosing algorithms and can be used whenever the patient receives a warfarin prescription. For statins, 31.5% of patients carried at least one allele at the genotyped SLCO1B1 variant (rs4149056), increasing their risk of developing myopathy. 96% of our cohort received at least one PGx-generated clinical recommendation for the studied drugs. CONCLUSION: The current pilot analysis verified the feasibility of PGx-testing and the unforeseen high frequencies of patients currently treated with suboptimal drug regimens, which may potentially benefit from PGx testing.

Antihypertensives associated adverse events: a review of mechanisms and pharmacogenomic biomarkers available evidence in multi-ethnic populations.

Hypertension remains a significant health burden worldwide, re-emphasizing the outstanding need for more effective and safer antihypertensive therapeutic approaches. Genetic variation contributes significantly to interindividual variability in treatment response and adverse events, suggesting pharmacogenomics as a major approach to optimize such therapy. This review examines the molecular mechanisms underlying antihypertensives-associated adverse events and surveys existing research on pharmacogenomic biomarkers associated with these events. The current literature revealed limited conclusive evidence supporting the use of genetic variants as reliable indicators of antihypertensive adverse events. However, several noteworthy associations have emerged, such as 1) the role of ACE variants in increasing the risk of multiple adverse events, 2) the bradykinin pathway's involvement in cough induced by ACE inhibitors, and 3) the impact of CYP2D6 variants on metoprolol-induced bradycardia. Nonetheless, challenges persist in identifying biomarkers for adverse events across different antihypertensive classes, sometimes due to the rarity of certain events, such as ACE inhibitors-induced angioedema. We also highlight the main limitations of previous studies that warrant attention, including using a targeted gene approach with a limited number of tested variants, small sample sizes, and design issues such as overlooking doses or the time between starting treatment and the onset of adverse events. Addressing these challenges requires collaborative efforts and the integration of technological advancements, such as next-generation sequencing, which can significantly enhance research outcomes and provide the needed evidence. Furthermore, the potential combination of genomic biomarker identification and machine learning is a promising approach for tailoring antihypertensive therapy to individual patients, thereby mitigating the risk of developing adverse events. In conclusion, a deeper understanding of the mechanisms and the pharmacogenomics of adverse events in antihypertensive therapy will likely pave the way for more personalized treatment strategies to improve patient outcomes.