Trends in Pharmacological Sciences
ReviewGenetic and clinical factors relating to warfarin dosing
Introduction
Almost 20 million prescriptions are written for warfarin each year in the US and it is one of the most challenging drugs in the modern medical formulary (www.rxlist.com/top200a.htm). Warfarin has a narrow therapeutic window and the hemorrhagic or thrombotic implications of over- or under-dosing can be devastating [1]. Worldwide, it is one of the leading causes of visits to the emergency department and hospitalizations owing to adverse drug events [2]. Eight adverse bleeding events have been estimated to occur annually for every 100 patients treated [1]. Adverse events from warfarin are more common during the initial months of treatment before the optimal dose is determined 3, 4, 5. Moreover, there is substantial individual variation in response to warfarin, necessitating frequent monitoring and dosage adjustments. The monitoring and dosing of warfarin is so challenging that many physicians have abdicated this role to specialized ‘warfarin clinics’, which are devoted solely to monitoring patients on this agent. Unfortunately, no good alternatives to warfarin exist for the common indications requiring chronic anticoagulation such as atrial fibrillation, deep vein thrombosis, pulmonary embolism and artificial heart valves.
Numerous genetic and clinical factors have been associated with variability in maintenance warfarin dose requirements 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, including age, race, weight, height, smoking status, medications and polymorphisms of the CYP2C9 and VKORC1 genes (see Glossary), which encode for enzymes important in warfarin pharmacology (Box 1). In this paper we review the genetic and clinical factors associated with warfarin dose requirements, including recent developments in the field. We highlight the role of known genetic polymorphisms in CYP2C9 and VKORC1, which have consistently been associated with warfarin dose requirements in numerous populations around the world and account for ∼15% and 25% of the variation in dose requirements, respectively. In addition, we describe recent reports of the association between dose requirements and polymorphisms in CYP4F2, which have been reported to account for between 2 and 7% of the variability in warfarin dose. We draw attention to the current understanding of the relative contributions of genetic polymorphisms to dose requirements based on data from large studies published recently, including the Warfarin Genetics (WARG) study, the International Warfarin Pharmacogenetic Consortium (IWPC), and a 2009 meta-analysis of 39 studies exploring the influence of CYP2C9 genotype. Lastly, we describe the prospective studies exploring the potential clinical utility of genotype-guided dosing for improving anticoagulation control, health outcomes or healthcare utilization. It remains unclear whether the use of dose prediction algorithms to guide warfarin dosing will be clinically beneficial, but studies are currently underway that will help to answer this question.
Section snippets
Genetic factors related to warfarin dose requirements
It has been estimated that more than 30 genes have a putative impact on anticoagulation with warfarin [19]. Many of these genes seem to have minimal or no impact on in vivo warfarin dose requirements. However, differences in the genes encoding the cytochrome p450 2C9 (CYP2C9) and vitamin K epoxide reductase (VKOR) enzymes account for ∼15% and 25%, respectively, of the variation in warfarin dose requirements 8, 9, 10, 15, 20, 21, 22, 23 (Figure 1).
Other genes
In addition to CYP2C9 and VKORC1, other genes have been studied as potential contributors to the variation in warfarin doses. These genes have generally been found to have minimal or no impact on warfarin dose requirements. Most of the genetic association studies of other genes have had sample sizes that were too small to detect small effects, and the findings in many of them could be due to chance, given that so many comparisons are evaluated. SNPs in the gene encoding γ-glutamyl carboxylation
Clinical and environmental factors related to warfarin dose requirements
Clinical and environmental factors such as age, race, weight, height, medications, diet, illness, smoking and adherence have all been found to influence warfarin dosing. Dose requirements decrease with age, owing to reduced clearance and/or increased responsiveness, by ∼8–10% per decade of life 6, 63, 64. A study of 297 patients on stable warfarin doses reported that mean warfarin daily dose requirements fell by 0.5 to 0.7 mg per decade between the ages of 20 to 90 years [8]. Multiple linear
Genotype-guided warfarin dosing
Considering all of the factors that have been associated with maintenance warfarin dose requirements, the overall contribution of a given factor to variability in dose requirement is best understood by evaluating several important factors with multiple linear regression, as many studies have done 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 52, 73. These studies have generally found a combination of the following factors to be important predictors of warfarin dose: age, race, weight and height (or
Concluding remarks
Many studies have evaluated genetic and clinical factors associated with warfarin dose requirements with multiple linear regression to determine the relative contribution of each factor to develop dose prediction algorithms. These studies have generally found age, race, weight and height, amiodarone, and CYP2C9 and VKORC1 SNPs to be important predictors of warfarin dose. The largest of these studies, published by the IWPC, used data from over 5000 subjects and found that a pharmacogenetic
Glossary
- ApoE
- apolipoprotein E.
- CI (confidence interval)
- a measure of the uncertainty around the main finding of a statistical analysis. Wider intervals indicate lower precision; narrow intervals, greater precision.
- CYP2C9
- cytochrome p450 2C9.
- HR (hazard ratio)
- a measure of effect produced by a survival analysis. This represents the increased risk with which one group is likely to experience the outcome of interest. For example, if the HR for death for a treatment is 0.5, then we can say that treated patients
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