Bayesian Prediction of Serum Phenytoin Concentration in a Simulation study

概要

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The predictive performance of the Bayesian weighted least-squares method (BWLS), viz., the Bayesian method, was evaluated and compared with that of the ordinary weighted nonlinear least-squares method (OWLS) in a simulation study. Phenytoin was selected as a model drug for a one-compartment nonlinear model (Michaelis-Menten model). The patient population for the phenytoin study (K_m : 3.9±2.1 μg/ml, V_<max> : 6.78±1.27 mg/kg/d) was generated by using a random number generator.When estimating the parameters by the Bayesian method, the V_<max> estimate was more precise than the K_m : V_<max> could be estimated with a 15% and 10% prediction error with one and two observations, respectively, as opposed to 30% and 20% for K_m. It was also inferred that the predictive performance of the one- and two-observation BWLSs was a slightly better than or equal to that of the three-observation OWLS but did not approach that of the four-observation OWLS.The present simulation study indicated that the Bayesian method could provide reliable estimations of Michaelis-Menten parameters such as K_m and V_<max> and clinically acceptable predictions for the steady-state serum concentration with at least one observation (the root mean squared error, 3.4 μg/ml; 95% confidence interval, 1.4-4.6 μg/ml), if appropriate population pharmacokinetic parameters (i.e., means and variances) were available.
公益社団法人日本薬学会の論文
1988-05-25