Opportunities and Challenges in Cardiovascular Pharmacogenomics: From Discovery to Implementation

Abstract

This review will provide an overview of the principles of pharmacogenomics from basic discovery to implementation, encompassing application of tools of contemporary genome science to the field (including areas of apparent divergence from disease-based genomics), a summary of lessons learned from the extensively studied drugs clopidogrel and warfarin, the current status of implementing pharmacogenetic testing in practice, the role of genomics and related tools in the drug development process, and a summary of future opportunities and challenges.

Keywords: clopidogrel; genomics; pharmacogenetics; pharmacokinetics; warfarin.

Figure 1

Variability in LDL lowering in response to 40 mg of simvastatin daily for 6 weeks in 1,007 patients. Most patients achieve 20–60% LDL lowering, in a few the effect is greater and in a few there is little effect. The distribution does not show any clear subset, consistent with the idea that multiple genetic variants contribute (see Figure 5, bottom panel). Adapted by permission from Simon et al, 2006.

Figure 2

The spectrum of pharmacogenomic science, from discovery to implementation.

Figure 3

Pharmacokinetic and pharmacodynamic sources of variability in drug action. Pharmacokinetic processes determine drug concertation at molecular targets that through multiple mechanisms broadly termed pharmacodynamics transduce beneficial and undesirable drug effects.

Figure 4

A common splice variant (arrow on the top panel) results in generation of an mRNA (bottom) that encodes a CYP3A5 with markedly reduced hepatic expression, and associated with reduced tacrolimus bioinactivation. Subjects of African origin, in whom this variant is uncommon, display lower tacrolimus concentrations and this has been associated with an increased risk of rejection.

Figure 5

Contrasting outcomes when variants in a single gene (top) or multiple genes (bottom) drive variability in drug response. In the top panel, the distribution of drug responses is not normally distributed, but shows distinct antimodes (heavy arrows) separating subjects with the poor metabolizer trait (due to two non-functional alleles) and those with the ultra-rapid metabolizer trait (due to gene duplication or the presence of hyperfunctional alleles) from the majority (extensive and intermediate metabolizers) that are not readily distinguished. “High risk pharmacokinetic” scenarios discussed in the text are examples. The bottom panel shows a more usual distribution of drug responses without clear antimodes. Alleles associated with decreased drug responses are shown in darker colors, and the distribution reflects varying combinations of multiple alleles to overall drug response.

Figure 6

Point of care clinical decision support. This pop-up appears in the Vanderbilt EHR when clopidogrel is prescribed to a patient who has had pharmacogenetic data deposited in their record and is known to carry CYP2C19 loss of function allele(s).