Naturally occurring human genetic variants provide a valuable tool to identify drug targets and guide drug prioritization and clinical trial design. Ivabradine is a heart rate lowering drug with protective effects on heart failure despite increasing the risk of atrial fibrillation. In patients with coronary artery disease without heart failure, the drug does not protect against major cardiovascular adverse events prompting questions about the ability of genetics to have predicted those effects. This study evaluates the effect of a variant in HCN4, ivabradine s drug target, on safety and efficacy endpoints.
We used genetic association testing and Mendelian randomization to predict the effect of ivabradine and heart rate lowering on cardiovascular outcomes.
Using data from the UK Biobank and large GWAS consortia, we evaluated the effect of a heart rate-reducing genetic variant at the HCN4 locus encoding ivabradine s drug target. These genetic association analyses showed increases in risk for atrial fibrillation (OR 1.09, 95% CI: 1.06 1.13, P = 9.3 10-9) in the UK Biobank. In a cause-specific competing risk model to account for the increased risk of atrial fibrillation, the HCN4 variant reduced incident heart failure in participants that did not develop atrial fibrillation (HR 0.90, 95% CI: 0.83 0.98, P = 0.013). In contrast, the same heart rate reducing HCN4 variant did not prevent a composite endpoint of myocardial infarction or cardiovascular death (OR 0.99, 95% CI: 0.93 1.04, P = 0.61).
Genetic modelling of ivabradine recapitulates its benefits in heart failure, promotion of atrial fibrillation, and neutral effect on myocardial infarction.
Pharmacogenomic study using the UK Biobank data
The aims of our research project are to study the effect of genetic variation on 1) response to medication, 2) on the progression of cardiovascular and metabolic diseases in medicated patients, and 3) to use genetic variation in genes encoding drug targets or drug modulators to study possible effects of drugs. Mutation in genes encoding or modulating drug targets can be associated with clinical features similar to the effect of the drug and this can enable the identification of drug repurposing opportunities or the identification of possible adverse drug reaction. Identifying genetic determinants of drug response is a key step towards personalized medicine as it will allow clinicians to optimize treatment for individuals based on their genetic profile. The second objective of our project will help understand the genomic basis of metabolic and cardiovascular diseases, a leading global cause of death. Finally, our genomic approach to predict the effects of drugs could lead to the prediction of associated clinical outcomes. Such findings could eventually benefit patients by broadening the range of available drug treatments. To identify genetic determinants of drug response, we will assess the difference in disease incidence between genetic subgroups of individuals following pharmacotherapy for cardiovascular or metabolic diseases. For example, we could look at users of anti-diabetes drugs such as metformin and conduct genome-wide screens to identify genetic variants associated with reductions in HbA1c and disease severity such as retinopathy. We will also conduct phenome-wide scans for effects associated with mutations in drug target genes. This will be achieved by testing mutations between cardiovascular disease drug target genes and all available medical diagnostics reported in the UK Biobank. To maximize statistical power, the full cohort will be requested for this project.
|Lead investigator:||Marie-Pierre Dube|
|Lead institution:||Montreal Heart Institute|