Model‐Based Evaluation of Linear Limited and Bayesian Sparse Sampling for Therapeutic Monitoring of Recombinant Coagulation Factor IX

Abstract

Dosing of coagulation factor products is mainly determined based on a patient's body weight; however, several studies have reported high interindividual variability in their pharmacokinetics (PK). The objective of this study was to develop and evaluate 2 sparse sampling methods for the estimation of AUC of recombinant factor IX (BeneFIX) as proof of concept for dose individualization. A population pharmacokinetic model was used to generate the plasma factor IX activity-versus-time data. The linear limited sampling model (LLSM) was developed based on the correlation of factor IX activity versus AUC 0-72 hours following screening of several blood sampling times in adolescent and adult subjects (n = 90 subjects). Factor IX trough concentrations were predicted from a relationship established from AUC versus factor IX activity measured 72 hours postdosing. Using the best selected sampling time, the LLSM and Bayesian model were validated in separate data sets (n = 75 subjects). Using the LLSM and Bayesian analysis, a blood sample at 24 hours predicted AUC with bias and root mean square error < 5% and < 15%, respectively. The predicted trough concentrations were ≥1 IU/dL in 99% and 100% of subjects by the LLSM and Bayesian model, respectively. The average factor IX dose for a target AUC of 800 IU•h/dL was 61, 60, and 63 IU/kg using the extensive (reference), LLSM and Bayesian model, respectively. Overall, the AUC, trough concentrations and individualized dosing of recombinant factor IX could be reasonably predicted using the LLSM and Bayesian model.