Pharmacogenetics of cardiovascular drug therapy.

In developed countries cardiovascular disease is one of the leading causes of death. Cardiovascular drugs such as platelet aggregation inhibitors, oral anticoagulants, antihypertensives and cholesterol lowering drugs are abundantly prescribed to reduce risk of cardiovascular disease. Notable interindividual variation exists in the response to these pharmacotherapeutic interventions, which can be partially explained by factors such as gender, age, diet, concomitant drug use and environmental factors. Notwithstanding, a great part of this variability remains unknown. To a smaller or larger extent, genetic variability may contribute to the variability in response to these cardiovascular drugs. This review gives an overview of pharmacogenetic studies of genes that were reported to be associated with four commonly prescribed drugs/drug classes (platelet aggregation inhibitors, coumarins, antihypertensives and statins) and were studied at least 2 times with a similar outcome measure. In the field of cardiovascular drug therapy, polymorphisms in candidate genes such as the cycloxygenase-1, vitamin K reductase complex subunit 1, CYP2C9, alpha adducin and 3-hydroxy-3-methylglutaryl-CoA reductase have received a great amount of interest in the pharmacogenetics of aspirin, coumarins, antihypertensives and statins respectively. However, only variations in VKORC1 and CYP2C9 have consistently been associated with drug response (coumarins) and have clinical implications. Clinical trials should provide evidence for the effectiveness of genotyping before this procedure will be a part of every day anticoagulant therapy. In spite of the tremendous amount of publications in this field, there is no reason to advocate for genetic testing for any other drugs cardiovascular drug therapy yet. Current approaches in pharmacogenetic research do not seem to lead to results that meet our expectations of individualized medicine. Therefore, new approaches are needed addressing issues and challenges such as the number of SNPs studied, study power, study design and application of new statistical methods in (pharmaco-)genetic analysis.