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This Article Full Text Full Text (PDF) All Versions of this Article: clinchem.2009.125898v1 55/10/1861    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Linder, M. W. Articles by Valdes, R. PubMed PubMed Citation Articles by Linder, M. W. Articles by Valdes, R., Jr.

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

¹ Department of Pathology and Laboratory Medicine, University of Louisville School of Medicine, Louisville, KY; ² Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA; ³ PGXL Laboratories, Louisville, KY; Departments of ⁴ Internal Medicine and ⁵ Genomic and Laboratory Medicine, Washington University School of Medicine, St. Louis, MO; ⁶ Morehouse University, Atlanta, GA.

^aAddress correspondence to this author at: Department of Pathology and Laboratory Medicine, University of Louisville School of Medicine, Louisville, KY. E-mail mwlind01{at}gwise.louisville.edu.

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 < 0.05) associated with VKORC1 –1639G>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.