What is the clinical evidence from this study?

Study design: Observational. Population: Warfarin therapy (n=137). Intervention: Pharmacogenetic decision-support tool (CYP2C9 and VKORC1 genotype-guided modeling). Primary outcome: Association of plasma S-warfarin concentration required to yield target INR response with VKORC1 -1639G>A genotype (p=<0.05).

August 13, 2009Open Access

Interactive Modeling for Ongoing Utility of Pharmacogenetic Diagnostic Testing: Application for Warfarin Therapy

Q: What are the key findings of this study?

Pharmacogenetic modeling showed target plasma S-warfarin concentration was significantly associated with VKORC1 genotype (P<0.05), and CYP2C9 genotype predicted 58% of concentration variation.

Key Result

Pharmacogenetic modeling showed target plasma S-warfarin concentration was significantly associated with VKORC1 genotype (P<0.05), and CYP2C9 genotype predicted 58% of concentration variation.

Study Design

Type

Observational (n=137)

Structured PICO

Does genotype-guided pharmacokinetic/pharmacodynamic modeling using CYP2C9 and VKORC1 genotypes predict target plasma S-warfarin concentrations in patients with stable INRs?

Population

137 patients with stable INRs on warfarin therapy

Intervention

Genotype-guided pharmacokinetics/pharmacodynamics modeling using CYP2C9 and VKORC1 genotypes

Comparator

Actual measured steady-state plasma S-warfarin concentrations

Outcome

Plasma S-warfarin concentration required to yield target INR response and variation in measured S-warfarin concentrationsurrogate

Combining CYP2C9 and VKORC1 genotype data with pharmacokinetic/pharmacodynamic modeling accurately predicts target S-warfarin concentrations for therapeutic INR, providing a decision-support tool for warfarin dosing.

Main Result

p-value: p=<0.05

Abstract

BACKGROUND: The application of pharmacogenetic results requires demonstrable correlations between a test result and an indicated specific course of action. We developed a computational decision-support tool that combines patient-specific genotype and phenotype information to provide strategic dosage guidance. This tool, through estimating quantitative and temporal parameters associated with the metabolism- and concentration-dependent response to warfarin, provides the necessary patient-specific context for interpreting international normalized ratio (INR) measurements. METHODS: We analyzed clinical information, plasma S-warfarin concentration, and CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) and VKORC1 (vitamin K epoxide reductase complex, subunit 1) genotypes for 137 patients with stable INRs. Plasma S-warfarin concentrations were evaluated by VKORC1 genotype (-1639G>A). The steady-state plasma S-warfarin concentration was calculated with CYP2C9 genotype-based clearance rates and compared with actual measurements. RESULTS: The plasma S-warfarin concentration required to yield the target INR response is significantly (P A genotype (GG, 0.68 mg/L; AG, 0.48 mg/L; AA, 0.27 mg/L). Modeling of the plasma S-warfarin concentration according to CYP2C9 genotype predicted 58% of the variation in measured S-warfarin concentration: Measured S-warfarin = 0.67(Estimated S-warfarin) + 0.16 mg/L. CONCLUSIONS: The target interval of plasma S-warfarin concentration required to yield a therapeutic INR can be predicted from the VKORC1 genotype (pharmacodynamics), and the progressive changes in S-warfarin concentration after repeated daily dosing can be predicted from the CYP2C9 genotype (pharmacokinetics). Combining the application of multivariate equations for estimating the maintenance dose with genotype-guided pharmacokinetics/pharmacodynamics modeling provides a powerful tool for maximizing the value of CYP2C9 and VKORC1 test results for ongoing application to patient care.