Development of an efficient sampling strategy to predict enoxaparin pharmacokinetics in stage 5 chronic kidney...

Patients with renal dysfunction are at greater risk for hemorrhagic events than patients with normal renal function. The objective of this study was to develop an efficient sampling strategy that optimally predicts anti-Xa activity exposure after enoxaparin administration in patients with chronic kidney disease to optimize enoxaparin therapy. The antifactor Xa activity data of 8 anuric patients who were administered 1 mg/kg enoxaparin immediately after hemodialysis were used for the development of the optimal sampling strategies. Maximum A Posteriori Bayesian (MAPB) priors were developed by pharmacokinetic analysis. The 2, 3, and 4 D-optimal sampling designs were determined and Monte Carlo simulations using the MAPB estimator were performed. The original model with the estimator was used to determine the predictive power of the sampling schemes. The most efficient sampling scheme (OSS-2) was accurate and precise in predicting apparent enoxaparin clearance (-3.2% bias, 6.5% precision). The addition of a third sampling time at 30 minutes (OSS-3) was more accurate (P < 0.01) and precise (P < 0.01) than OSS-2 at predicting the rate of absorption (ka) but did not improve the accuracy (P > 0.05) or precision (P = 0.20) of the CLs/F estimate. The determination of antifactor Xa activity at 5 and 24 hours, along with the Bayesian estimators generated from this study, accurately and precisely predict the apparent clearance of antifactor Xa activity. This sampling strategy may be used for therapeutic drug monitoring of enoxaparin and for future clinical trials in patients with chronic kidney disease.