Will there be a role for genotyping in warfarin therapy?:

Improvement of anticoagulant treatment using a dynamic decision support algorithm: a Danish Cohort study

INTRODUCTION

Warfarin is the most widely prescribed vitamin K antagonist and in the United States and Europe more than 10 million people are currently in long-term oral anticoagulant treatment. This study aims to retrospectively validate a dynamic statistical model providing dosage suggestions to patients in warfarin treatment.

MATERIALS AND METHODS

The model was validated on a cohort of 553 patients with a mean TTR of 83%. Patients in the cohort were self-monitoring and managed by a highly specialised anticoagulation clinic. The predictive model essentially consists of three parts handling INR history, warfarin dosage and biological noise, which allows for prediction of future INR values and optimal warfarin dose to stay on INR target. Further, the model is based on parameters initially being set to population values and gradually individualised during monitoring of patients.

PRIMARY OUTCOME

Time in therapeutic range was used as surrogate quality measure of the treatment, and model-suggested dosage of warfarin was used to assess the accuracy of the model performance.

RESULTS

The accuracy of the model predictions measured as median absolute error was 0.53 mg/day (interquartile range from 0.25 to 1.0). The model performance was evaluated by the difference between observed and predicted warfarin intake in the preceding week of an INR measurement. In more than 70% of the cases where INR measurements were outside the therapeutic range, the model suggested a more reasonable dose than the observed intake.

CONCLUSION

Applying the proposed dosing algorithm can potentially further increase the time in INR target range beyond 83%.

Pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) observed during drug development have been the cause for discontinuing development of many drugs. In addition, serious but rare ADRs observed after marketing have led to withdrawal of some drugs. A priori identification of individuals at risk of developing ADRs for a given drug will help develop strategies to reduce the risk for ADRs in these patients. US FDA initiatives and efforts at reducing ADRs to make drugs safer are described, including updating of drug labels to include genomic information intended to reduce ADRs. Pharmacogenomics can also be harnessed to identify individuals at risk of developing serious ADRs and to treat these individuals with alternative therapy, thus converting ADRs that are traditionally considered unavoidable to avoidable ADRs.