Predicting individual phenytoin dosage

Abstract

Most previously suggested methods for predicting phenytoin dosage from steady-state drug levels (Cpss) measured in the clinical setting fail to fully exploit all relevant (population) information. A bayesian prediction method, applicable to any drug, is available. It appropriately combines all types of information. In this paper, we compare the Bayesian method as applied to phenytoin to two other prediction methods (and a baseline, nonfeedback one). Actual doses are compared to predictions in 49 patients. Each method is optimized, as far as possible, for the test data. The comparison favors the Bayesian method. Since each of the other prediction methods for phenytoin can be shown to be a theoretically suboptimal special case of the Bayesian one, the superiority of the latter may be a general phenomenon. Because the pharmacokinetic model linking steady-state phenytoin levels and dosage is so simple, a good approximation of the general Bayesian method can be implemented as a graphical device, or as a program for a programmable calculator. We present and describe both of these approximations.

Phenytoin pharmacokinetics after intravenous administration to patients receiving enteral tube feeding

“…The pharmacokinetic parameters calculated in group 1 from both the linear and non-linear analysis were very similar to population parameters of patients reported in other studies [24][25][26][27].…”

OPT: a package of computer programs for parameter optimisation in clinical pharmacokinetics.

“…This does not apply, however, to phenytoin, and this drug is dealt with exactly as described by Vozeh et al (1981) (9). This is carried out using a SIMPLEX routine (Nelder & Mead, 1965) and the estimated errors in the parameter values are obiained.…”

Evaluation of a bayesian pharmacokinetic program for phenytoin concentration predictions in outpatient population

“…These drug dosing methods include nomograms [1], graphic methods using linear transformations of the Michaelis-Menten equation [2], techniques using population clearance values [3] and Bayesian methods [4]. Today, the Bayesian approach is considered to be the best one according to the precision and the reduced number of deviation errors.…”

“…Various previous studies showed that one of the main parameters affecting the predictive performances of such Bayesian method was the number of feedback concentrations from each patient used for predictions [4,15,16]. Therefore, the PKS program was used to predict following phenytoin serum concentration from 1 (data set b) or 2 (data set c) feedback concentrations.…”