One-point feedback control method for phenytoin dosage adjustment.

Routine clinical pharmacokinetic data collected from patients receiving phenytoin have been analysed to propose a new and simple equation to aid the dosage adjustment of this drug. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The rate equation for the elimination of phenytoin can be written as Do = kCssn, which fits the steady-state serum concentration (Css) and daily dose data (Do). The parameter n is the kinetic order and the parameter k is an arbitrary rate constant. From the above equation, D2 = D1C1 -nC2n can be derived, which forms the basis of predicting the dosage, D2, to obtain a desired Css, C2, using one initial Css, C1, obtained with an initial dose, D1, and using a population value of n. The value of n for phenytoin was estimated to be 0.312 in this study. The predictive performance of this equation was compared with the Richens and Dunlop nomogram and Bayesian feedback method using two or more steady-state concentration/dose pairs from each of 78 outpatients. This equation allowed the prediction of a dose needed to produce a desired steady-state concentration with errors comparable with the Bayesian feedback method for therapeutic drug monitoring.     

Three Michaelis-Menten pharmacokinetic dosing methods compared with physician dosing of phenytoin in an outpatient neurology practice.

We compared predicted phenytoin serum concentrations using three Michaelis-Menten pharmacokinetic dosing methods with actual concentrations obtained from physician dosing in an outpatient neurology practice. Method 1 used population estimates for the Michaelis-Menten constant (Km) and maximum velocity (Vmax), method 2 used one dose and serum concentration pair to determine Vmax, and method 3 used two dose-concentration pairs to determine both Km and Vmax. In addition, physician doses were compared with pharmacokinetically calculated doses. Records of patients who received at least two phenytoin doses followed by two serum concentration determinations were reviewed. Data on age, gender, weight, physician doses, and resultant serum concentrations were collected. Pearson's correlation coefficient was used to compare physician maintenance doses with pharmacokinetically calculated predicted doses, whereas actual and predicted serum concentration data were used to determine precision and bias associated with each of the three methods. Actual serum concentrations fell into therapeutic range more frequently than predicted values in all but one comparison (method 3). Predicted and actual phenytoin doses were significantly correlated only with method 2. Only one of the three Michaelis-Menten pharmacokinetic dosing methods evaluated (method 3) was more predictive than physician phenytoin dosing.     

Linear and nonlinear kinetics of drug elimination I. Kinetics on the basis of a single capacity-limited pathway of elimination with or without simultaneous supply-limited elimination

The pharmacokinetic behavior of foreign substances that are completely or partially eliminated via metabolism by saturable enzyme systems is analyzed. General integrated equations are derived which describe the time course of the plasma concentration under the assumption of a saturable enzyme system according to Michaelis-Menten kinetics in combination with normal firstorder elimination processes. A procedure for the estimation of initial values of the elementary kinetic parameters on the basis of the models is outlined. These initial values have been used in a nonlinear curvefitting program in order to obtain reliable kinetic and enzyme parameters from the plasma curves. With these methods, kinetic and apparent enzyme parameters are calculated for ethanol, salicylic acid, 4-hydroxybutyric acid, and phenytoin. Financial support was provided by the Prevention Fund of the Dutch Ministry of Health and by the Dutch Foundation of Medical Research (Fungo-TNO).     

Disposition of phenytoin in critically ill trauma patients

Estimates of phenytoin pharmacokinetic variables and protein binding were determined in 10 adult critically ill trauma patients. Each study subject received phenytoin sodium as an intravenous loading dose of 15 mg/kg, followed by an initial intravenous maintenance dose of 6 mg/kg/day. Serial blood samples were obtained throughout the seven-day study period and analyzed for total and unbound serum phenytoin concentrations. The concentration data for each patients were fitted to a one-compartment model with elimination defined by the Michaelis-Menten constant Km and the maximum rate of metabolism (Vmax) and to a one-compartment model with first-order elimination. The Michaelis-Menten model used Bayesian parameter estimation while the linear model used weighted non-linear least-squares regression analysis. Unbound phenytoin fraction ranged from 0.073 to 0.25. Free fraction increased 7% to 108% in 9 of 10 patients (median increase 29%) from day 1 to day 7 of therapy. Variable estimates using the Michaelis-Menten model were as follows: volume of distribution, 0.76 +/- 0.15 L/kg (0.58-1.01 L/kg); Vmax, 568 +/- 197 mg/day (350-937 mg/day); and Km, 4.5 +/- 1.8 mg/L (1.8-6.2 mg/L). These estimates fell within the wide range of values obtained in studies using stable patients or healthy volunteers. The Michaelis-Menten model was significantly less biased and more precise than the linear model. Three of four patients who continued to receive their study maintenance dose had substantially lower measured total serum concentrations of phenytoin than predicted using the study variable estimates.(ABSTRACT TRUNCATED AT 250 WORDS)     

Factors influencing the population pharmacokinetic parameters of phenytoin in adult epileptic patients in South Africa

The influence of various covariates (including weight, race, smoking, gender, age, mild-to-moderate alcohol intake, and body surface area) on the population pharmacokinetic parameters of phenytoin in adult epileptic patients in South Africa was investigated. The parameters were the maximum metabolic rate (Vm) and the Michaelis-Menten (MM) constant (Km) of phenytoin. The study population comprised 332 black and colored epileptic patients (note: "black" refers to indigenous people of South Africa, who speak one of the Bantu languages as their native language; "colored" refers to people considered to be of mixed race, classified as such by the apartheid former government of South Africa). The influence of covariates on Vm and Km estimates was determined using nonlinear mixed-effects modeling (NONMEM). Parameter models describing the factors that could potentially influence Vm and Km were tested using the Michaelis-Menten parallel MM and first-order elimination models, to which 853 steady state dose-to-serum concentration pairs were fitted. The results indicated that body weight, smoking, race, and age (65 years or older), in descending order of importance, significantly influenced Vm (p < 0.05). Although a significant difference (p = 0.03) in Km was found between black and colored patients, incorporating the influence of race in Km in the final regression model did not improve the fit of the model to the data, which indicated that the variability in Km was accounted for by Vm. The scaling factors for smoking, colored patients and age (65 years or older) in Vm were 1.16, 1.10, and 0.88, respectively. These factors should be taken into account when adjusting phenytoin dose.     

Validation of population pharmacokinetic parameters of phenytoin using the parallel Michaelis-Menten and first-order elimination model

This study was conducted to assess whether the parallel Michaelis-Menten and first-order elimination (MM+FO) model fitted the data better than the Michaelis-Menten (MM) model, and to validate the MM+FO model and its parameter estimates. The models were fitted to 853 steady state dose: serum concentration pairs obtained in 332 adults with epilepsy using nonlinear mixed-effects modeling (NONMEM). The MM+FO model fitted the data better than the MM model. The validity of the pharmacokinetic models and the estimated population parameter values was tested using the naive prediction method. The estimation and validation of the pharmacokinetic parameters were undertaken in two separate patient groups (cross-validation) obtained by splitting the data set. Patients were randomly allocated to two equally matched groups (groups 1 and 2). The predictive performance was assessed using 770 paired predicted versus actual dose or measured serum concentrations. The population pharmacokinetic parameters estimated by NONMEM in group 1 were validated in group 2 and vice versa. When predicting steady state serum concentration, the MM+FO model was clearly superior to the MM model (mean bias of 0.91 and 8.13 mg/L, respectively).     

Determination of enzyme or binding constants using generalized linear models, with particular reference to Michaelis-Menten models

The estimation of Michaelis-Menten pharmacokinetic parameters in patients with epilepsy receiving phenytoin continues to be a vexing problem. The various approximate methods suggested in the literature have serious shortcomings, primarily due to the role of the error term in the statistical model. In this report we present an accurate statistical approach using the Generalized Linear Interactive Modeling (GLIM) computer package developed by the Numerical Algorithms Group, Oxford, U.K. There are several advantages to this model: a meaningful error term can be maintained by means of a link function, the model can incorporate within-subject and between-subject variables, and additional potential explanatory variables can be added to the model. The method is applied to predicting serum phenytoin levels of pregnant women monitored at monthly intervals during pregnancy and for two to five months after pregnancy. Michaelis-Menten parameters are estimated for each women and compared.     

Bayesian regression analysis of non-steady-state phenytoin concentrations: evaluation of predictive performance

Michaelis-Menten saturable pharmacokinetics confound the determination of appropriate phenytoin maintenance doses. This study retrospectively evaluated the performance of an IBM-PC/XT computer program applying Bayesian regression to the "explicit solution to the Michaelis-Menten equation." Zero to five non-steady-state phenytoin serum concentrations were used to predict either non-steady-state concentrations at least 10 days in the future (n = 49) or steady-state concentrations (n = 20). Non-steady-state concentration prediction precision (% mean absolute error) using 0-5 non-steady-state feedbacks was 137%, 62%, 39%, 31%, 25%, and 15%, respectively, and steady-state concentration prediction precision was 446%, 47%, 50%, 44%, 21%, and 13%, respectively. Elimination of subjects receiving concurrent drugs known to induce phenytoin metabolism significantly improved predictions based on population priors; however, performance improvements were not apparent after two serum level feedbacks. The program provided clinically acceptable predictions with four or more feedbacks. Refinement of population parameters and optimal sampling times should further improve performance.     

Steady-state pharmacokinetics of phenytoin from routinely collected patient data

Previously reported routine phenytoin clinical pharmacokinetic data from Japan, England, and Germany were analysed to estimate population pharmacokinetic parameters. There were 780 steady-state phenytoin concentrations and associated dosage rates (mg/day) from 322 patients. The patient group spanned paediatric and adult ages, mean age being 18.4 +/- 17.3 (SD) years; 53% were males. The data were analysed using NONMEM, a computer programme designed for population pharmacokinetic analysis. Estimates of the influence of age, gender, data source, height and weight on the maximum elimination rate (Vm) and Michaelis-Menten constant (Km) were obtained. The Vm and Km of a 70 kg adult male European were estimated to be 415 mg/day and 5.7 mg/L, respectively. Vm is not influenced by gender, age or data source. The parameters of a power function of height and weight were estimated to adjust Vm for body size. The best function adjusts Vm in proportion to weight to the 0.6 power; height contains no useful information. Km is not influenced by gender. The Km for patients less than 15 years old is 43% less than that of older patients. The Km of Japanese patients appears to be 23% less than that for European patients. Even after adjustments for age, etc., apparent Vm and Km vary unpredictably among individuals with a coefficient of variation between 10 to 20% and approximately 50% respectively.     

Phenytoin dose adjustment in epileptic patients

1 A preliminary survey showed that many outpatients with partially controlled epilepsy had serum concentrations of phenytoin below the recommended therapeutic range (10-20 μg/ml). A phenytoin tolerance test was devised with the intention of predicting a more adequate daily dose for such a patient.

2 Fifteen patients were each given an oral test dose of 600 mg phenytoin sodium and the serum concentration of phenytoin was measured at intervals over 48 h; the concentration rose during the first 4 h and decayed between 12-48 h as an almost linear function of time.

3 The serum concentration/time curves were fitted by an interative computer program based on the Michaelis-Menten equation. The mean saturated rate of elimination of phenytoin was 435 mg/day and the serum concentration (K_m) corresponding with 50% saturation was 3.8 μg/ml. The mean calculated dose of phenytoin sodium required for a steady state serum concentration of 10-20 μg/ml was 345-400 mg/day.

4 The Michaelis-Menten principle was used to predict steady state serum phenytoin concentrations in individual patients receiving daily doses of phenytoin sodium adjusted by steps of 100 mg. The serum concentrations tended to be either too low or too high. The steep relationship between phenytoin concentration and dose indicates that when the concentration reaches 5-10 μg/ml it is then appropriate to adjust dose by small steps of about 25 mg.