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The aim of this study was to characterize the relationship between warfarin concentrations and international normalized ratio (INR) response and to identify predictors important for dose individualization. S- and R-warfarin concentrations, INR, and CYP2C9 and VKORC1 genotypes from 150 patients were used to develop a population pharmacokinetic/pharmacodynamic model in NONMEM. The anticoagulant response was best described by an inhibitory E(MAX) model, with S-warfarin concentration as the only exposure predictor for response. Delay between exposure and response was accounted for by a transit compartment model with two parallel transit compartment chains. CYP2C9 genotype and age were identified as predictors for S-warfarin clearance, and VKORC1 genotype as a predictor for warfarin sensitivity. Predicted INR curves indicate important steady-state differences between patients with different sets of covariates; differences that cannot be foreseen from early INR assessments alone. It is important to account for CYP2C9 and VKORC1 genotypes and age to improve a priori and a posteriori individualization of warfarin therapy.

The objective of the study was to update a previous NONMEM model to describe the relationship between warfarin dose and international normalized ratio (INR) response, to decrease the dependence of the model on pharmacokinetic (PK) data, and to improve the characterization of rare genotype combinations. The effects of age and CYP2C9 genotype on S-warfarin clearance were estimated from high-quality PK data. Thereafter, a temporal dose-response (K-PD) model was developed from information on dose, INR, age, and CYP2C9 and VKORC1 genotype, with drug clearance as a covariate. Two transit compartment chains accounted for the delay between exposure and response. CYP2C9 genotype was identified as the single most important predictor of required dose, causing a difference of up to 4.2-fold in the maintenance dose. VKORC1 accounted for a difference of up to 2.1-fold in dose, and age reduced the dose requirement by ~6% per decade. This reformulated K-PD model decreases dependence on PK data and enables robust assessment of INR response and dose predictions, even in individuals with rare genotype combinations.

Purpose

Numerous studies have investigated causes of warfarin dose variability in adults whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.

Method

An adult population PK/PD-model for warfarin, with CYP2C9 and VKORC1 genotype, age and target INR as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external dataset of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children.

Results

Overall, the bridged model predicted INR response well in 64 warfarin treated Swedish children (median age 4.3 years), but with a tendency to over predict INR in children ≤ 2 years old. The bridged model predicted 20 of 49 children (41%) within ± 20% of actual maintenance dose (median age 7.2 years). In comparison the published dosing algorithms predicted 33-41% of the children within ± 20% of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ± 20% of actual dose to 70%.

Conclusion

A mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.

Warfarin is the most widely prescribed anticoagulant for prevention and treatment of thromboembolic events. Although highly effective, the use of warfarin is limited by a narrow therapeutic range combined with a more than ten-fold difference in the dose required for adequate anticoagulation in adults. For each patient, an optimal dose that leads to a favourable balance between the wanted antithrombotic effect and the risk of bleeding, measured as the prothrombin time International Normalised Ratio (INR), must be found. A model capable of describing the time-course of the INR response to warfarin therapy can be used to aid dose selection, both before starting therapy (a priori dose prediction) and after therapy has been initiated (a posteriori dose revision). In this paper we describe the transfer of a population PKPD-model for warfarin developed in NONMEM to a platform independent decision support tool written in Java. The tool proved capable of solving a system of differential equations representing the pharmacokinetics and pharmacodynamics of warfarin, with a performance comparable to NONMEM. To estimate an a priori dose the user provides information on body weight, age, CYP2C9 and VKORC1 genotype, baseline and target INR. With addition of information about previous doses and INR observations, the tool will use a Bayesian forecasting method to suggest an a posteriori dose, i.e. the dose with the highest probability to result in the desired INR. Results are displayed as the predicted dose per day and per week, and graphically as the predicted INR curve. The tool can also be used to predict INR following any given dose regimen, e.g. a loading-dose regimen. We believe it will provide a clinically useful tool for initiating and maintaining warfarin therapy in the clinic. It will ensure consistent dose adjustment practices between prescribers, and provide more efficient individualization of warfarin dosing in both children and adults.

It is difficult to predict anticoagulation response to warfarin in children mainly because of a wide inter-individual variability in warfarin dose requirement. The present study objective was to identify important predictors of dose in children and to optimize a previous NONMEM warfarin model for a priori and a posteriori dose and INR predictions in children. Data from 163 warfarin treated children with underlying heart disease (median age 6.3 years) were used. CYP2C9 and VKORC1 genotype caused up to 4-fold and 2-fold differences in warfarin dose requirement, respectively. Other important predictors of warfarin dose were bodyweight, age, baseline and target INR, and time since initiation of therapy with lower doses during the initiation. CYP4F2 genotype had only a marginal effect on dose. The present study findings will aid the development of a personalised approach to warfarin therapy in children, in the pursuit of improving both efficacy and safety of anticoagulation therapy.