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).     

Relationships between steady-state and single-dose plasma drug concentrations for pharmacokinetic systems with nonlinear elimination

Equations were examined to predict or correlate steady-state (SS) plasma concentrations and single-dose (SD) data for pharmacokinetic systems with various types of input and nonlinear elimination. The effects of variation of the Michaelis-Menten parameters, input rate of drug, or bioavailability on the predictability of steady-state plasma concentrations from single-dose data were demonstrated by computer simulations for a one-compartment model. Use of apparently linear equations with Michaelis-Menten parameters to predict steady-state plasma concentrations from single dose data is adequate only in limiting low single-dose cases. Linear SS versus SD correlations for both intravenous and oral dosing can be observed in population data only when Km is the principal variable; other conditions produce curvilinear behavior. Equations to calculate values of the Michaelis-Menten parameters from dual single-dose and steady-state AUC values are derived and tested for drugs which are intravenously administered. These equations and simulations provide insight into factors determining the relationship between SS and SD AUC for drugs with nonlinear elimination.     

An estimation of pharmacokinetic parameters for each dosing at unequal doses and dosing intervals

The calculations of pharmacokinetic parameters for each dosing at unequal doses and dosing intervals were proposed. Systemic clearance of a drug following one-compartment open model can be determined by the product of the apparent first-order elimination rate constant and the apparent volume of distribution. The determination of maximum plasma concentration (Cmax) and the time required for the Cmax at each dosing were also presented here. These theoretical considerations are applicable to multicompartment open model.     

Pharmacokinetics of moxonidine after single and repeated daily doses in healthy volunteers

The pharmacokinetics of the centrally acting alpha-2 agonist moxonidine were investigated in 12 healthy male subjects after single and repeated oral doses (q12h for five days) of moxonidine 0.2 mg. Plasma concentration-time data followed the general characteristics of a one-compartment model with first-order absorption. Peak plasma concentrations of 1.29 +/- 0.32 ng/mL were achieved 0.74 +/- 0.35 hours after ingestion of a 0.2-mg tablet. The terminal half-life of elimination was 2.12 +/- 0.58 hours. The oral clearance (CL/F) amounted to 830 +/- 171 mL/min with renal elimination being the major route of elimination. No significant differences in pharmacokinetic parameters could be observed following repeated dosing over five days.     

Application of optimal sampling theory to the determination of metacycline pharmacokinetic parameters: Effect of model misspecification

Use of optimal sampling theory (OST) in pharmacokinetic studies allows the number of sampling times to be greatly reduced without loss in parameter estimation precision. OST has been applied to the determination of the bioavailability parameters (area under the curve (AUC), maximal concentration (C_max), time to reach maximal concentration (T_max), elimination half-life (T_1/2), of metacycline in 16 healthy volunteers. Five different models were used to fit the data and to define the optimal sampling times: one-compartment first-order, two-compartment first-order, twocompartment zero-order, two-compartment with Michaelis-Menten absorption kinetics, and a stochastic model. The adequacy of these models was first evaluated in a 6-subject pilot study. Only the stochastic model with zero-order absorption kinetics was adequate. Then, bioavailability parameters were estimated in a group of 16 subjects by means of noncompartmental analysis (with 19 samples per subject) using each optimal sampling schedule based procedure (with 6 to 9 samples depending on the model). Bias (PE) and precision (RMSE) of each bioavailability parameter estimation were calculated by reference to noncompartmental analysis, and were satisfactory for the 3 adequate models. The most relevant criteria for discrimination of the best model were the coefficient of determination, the standard deviation, and the mean residual error vs. time plot. Additional criteria were the number of required sampling times and the coefficient of variation of the estimates. In this context, the stochastic model was superior and yielded very good estimates of the bioavailability parameters with only 8 samples per subject.     

Determination of drug half-life while giving doses between blood samples.

Traditional methods of determining drug elimination half-life require that no doses be given between the blood samples, which delays drug administration. This article details a new method to determine drug half-life from blood samples drawn with intervening drug doses, that is, while blood levels are building rather than falling. This method was derived from one-compartment pharmacokinetic modeling to assist regulation of doses of central nervous system drugs whose blood concentrations have clinical meaning, for example, lithium and some antidepressants. It should improve the accuracy and practicality of pharmacokinetic methods to predict desirable steady-state drug doses at start-up and to monitor the total daily drug exposure during followup.     

Pharmacokinetic study of tacrolimus in cystic fibrosis and non-cystic fibrosis lung transplant patients and design of Bayesian estimators using limited sampling strategies

OBJECTIVES: To: (i) test different pharmacokinetic models to fit full tacrolimus concentration-time profiles; (ii) estimate the tacrolimus pharmacokinetic characteristics in stable lung transplant patients with or without cystic fibrosis (CF); (iii) compare the pharmacokinetic parameters between these two patient groups; and (iv) design maximum a posteriori Bayesian estimators (MAP-BE) for pharmacokinetic forecasting in these patients using a limited sampling strategy. METHODS: Tacrolimus blood concentration-time profiles obtained on three occasions within a 5-day period in 22 adult lung transplant recipients (11 with CF and 11 without CF) were retrospectively studied. Three different one-compartment models with first-order elimination were tested to fit the data: one with first-order absorption, one convoluted with a gamma distribution to describe the absorption phase, and one convoluted with a double gamma distribution able to describe secondary concentration peaks. Finally, Bayesian estimation using the best model and a limited sampling strategy was tested in the two groups of patients for its ability to provide accurate estimates of the main tacrolimus pharmacokinetic parameters and exposure indices. RESULTS: The one-compartment model with first-order elimination convoluted with a double gamma distribution gave the best results in both CF and non-CF lung transplant recipients. The patients with CF required higher doses of tacrolimus than those without CF to achieve similar drug exposure, and population modelling had to be performed in CF and non-CF patients separately. Accurate Bayesian estimates of area under the blood concentration-time curve from 0 to 12 hours (AUC12), AUC from 0 to 4 hours, peak blood concentration (Cmax) and time to reach Cmax were obtained using three blood samples collected at 0, 1 and 3 hours in non-CF patients (correlation coefficient between observed and estimated AUC12, R2 = 0.96), and at 0, 1.5 and 4 hours in CF patients (R2 = 0.91). CONCLUSION: A particular pharmacokinetic model was designed to fit the complex and highly variable tacrolimus blood concentration-time profiles. Moreover, MAP-BE allowing tacrolimus therapeutic drug monitoring based on AUC12 were developed.     

Population kinetics of tobramycin in neonates

The population kinetics of tobramycin were studied in 140 neonates (100/40 patients for the index/validation groups, respectively) of 30 to 42 weeks' gestational age and 0.8 to 4.25 kg current body weight in their first 2 weeks of life, undergoing routine therapeutic drug monitoring of their tobramycin serum levels. The 365 tobramycin concentration measurements obtained were analyzed by use of NONMEM according to a one-compartment open model with zero-order absorption and first-order elimination. The effect of a variety of demographic, developmental, and clinical factors (gender, height, birth weight, current weight, gestational age, postnatal age, postconceptional age, and serum creatinine concentration) on clearance and volume of distribution was investigated. Forward selection and backward elimination regression identified significant covariates. The final pharmacostatistical model with influential covariates was as follows (full population): clearance (L/h) = 0.0508 x current weight (kg), multiplied by 0.843 if birth weight was 2.5 kg or less (low-birthweight infants), and volume of distribution (L) = 0.533 x current weight (kg). Using the proportional error model for the random-effects parameters, interindividual variability for clearance and for volume of distribution was determined to be 25.8% and 21.9%, respectively, and the residual variability was 19.2%. In this study, the use of the NONMEM gave significant and consistent information on the pharmacokinetics and the determinants of the pharmacokinetic variability of tobramycin in neonates when compared with available bibliographic information. Moreover, the final population pharmacokinetic model may be used to design a priori recommendations for tobramycin and to improve the dosing readjustments through Bayesian estimation.     

Postinduction carbamazepine clearance in an adult psychiatric population

The objective of this study was to describe the postinduction clearance of carbamazepine (CBZ) in adult psychiatric patients by population pharmacokinetic analysis using the NONMEM program. Specifically, an estimate of CBZ clearance and insight into the effect of common patient characteristics on clearance were sought. Steady-state trough CBZ serum concentrations, CBZ dosing history, concomitant drug administration, and other data from 45 patients were collected retrospectively. A one-compartment model with first-order absorption and first-order elimination was used, with absorption rate, bioavailability, and volume of distribution fixed to literature values. No evidence was found that race, sex, age, ethanol use, smoking, and concomitant lithium significantly affected CBZ clearance. In the final model, clearance was based on lean body weight. The coefficient of variation for clearance estimates was 16.5%. Residual variability was modest. Estimates for volume of distribution, rates of absorption and elimination, and bioavailability could not be pursued rigorously. Although these results may assist in understanding CBZ disposition in this population, their general clinical application should be approached with caution.     

Mathematical analysis and drug exposure evaluation of pharmacokinetic models with endogenous production and simultaneous first-order and Michaelis–Menten elimination: the case of single dose

Drugs with an additional endogenous source often exhibit simultaneous first-order and Michaelis–Menten elimination and are becoming quite common in pharmacokinetic modeling. In this paper, we investigate the case of single dose intravenous bolus administration for the one-compartment model. Relying on a formerly introduced transcendent function, we were able to analytically express the concentration time course of this model and provide the pharmacokinetic interpretation of its components. Using the concept of the corrected concentration, the mathematical expressions for the partial and total areas under the concentration time curve (AUC) were also given. The impact on the corrected concentration and AUC is discussed as well as the relative contribution of the exogenous part in presence of endogenous production. The present findings theoretically elucidate several pharmacokinetic issues for the considered drug compounds and provide guidance for the rational estimation of their pharmacokinetic parameters.     

Estimation of population pharmacokinetics using the Gibbs sampler

Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates. A grant from the British Heart Foundation supported Nicola G. Best.     

口服多剂量茶碱个体化给药方案的改良设计

目的:基于Excel表格程序设计茶碱个体化给药方案。方法:茶碱具有一室模型一级消除特征,根据群体药动学参数和患者个体的病理生理状态,设计个体化给药方案。结果:只需输入患者的年龄、身高、体重和病理生理状态及初始给药剂量和给药间隔,通过逻辑判断,程序即可自动快速计算出相应的稳态血药浓度值,通过改变给药剂量和给药间隔,观察稳态血药浓度变化范围,直至给药方案可行;也可求解任一时间点血药浓度。结论:根据茶碱群体药动学参数和患者的病理生理特点,可利用Excel表格程序设计茶碱个体化给药方案,方法简单、可靠、直观、易学。     

Pharmacokinetics of daptomycin in a critically ill adolescent with vancomycin-resistant enterococcal endocarditis

Daptomycin is a lipopeptide antibiotic active against multidrug-resistant gram-positive organisms. Our search of the literature found no published pediatric pharmacokinetic data. We report the use of pharmacokinetic analysis of daptomycin in a 13-year-old boy with vancomycin-resistant Enterococcus faecium endocarditis. Pharamcokinetic parameters were found to be significantly different from published adult parameters, such as a faster elimination rate, shorter half-life, and increased clearance. These age-related differences in the pharmacokinetic profile of daptomycin have significant dosing implications. As the use of this drug for off-label indications and in pediatric populations increases, it is important for clinicians to better understand the drug's pharmacokinetic profile in these patient populations.     

儿童肾功能评估公式和更昔洛韦清除率的相关性确证与剂量优化

目的:采用群体药动学分析方法考察不同肾小球滤过率(GFR)公式估计更昔洛韦清除率的适用性,并用于更昔洛韦给药剂量优化。方法:收集100例患者的血药浓度数据和临床资料,采用基于不同生物标志物的公式计算GFR。建立儿童患者静脉滴注更昔洛韦的群体药动学模型,考察体质量和肾功能对药动学参数的影响,并对最终模型进行内部验证。在建模过程中探索不同公式获取的GFR和更昔洛韦清除率之间的相关性。确定最适用的GFR公式后,根据体质量和肾功能对给药剂量进行个体化设计。结果:具有一级消除的一房室模型能够很好地描述更昔洛韦在儿童群体中的药动学特征。验证结果显示最终模型稳定可靠,预测性能较好。综合可视化检验和协变量分析结果,可确定Flanders Metadata公式计算获得的GFR与更昔洛韦清除率相关性较高,临床适用性较好。在此基础上提出了基于GFR和患者体质量的个体化给药方案。结论:本研究确证了最适用于预测儿童群体更昔洛韦清除率的GFR公式,为该药物治疗提供了一种基于建模手段的个体化给药策略。     

Considerations in analyzing single-trough concentrations using mixed-effects modeling

The purpose of this study was to assess the effect of trial design and data analysis choices on the bias and precision of pharmacokinetic (PK) parameter estimation. NONMEM was used to simulate and analyze plasma concentrations collected according to a dense (five samples) or sparse (single-trough samples) sampling scheme for a one-compartment open model with intravenous administration. The results indicated that the bias on estimates of CL with only single-trough data was 17% compared to less than 1% for only dense data. The estimates of CL were improved by fixing all other parameters and estimating only mean and variance of CL (-11% to 1.4%, depending on the estimation method). Adding dense data led to further improvements (-2.3% to 0.3%, depending on further improvements). In these cases, first-order conditional estimation (FOCE) methods resulted in better estimates of CL than first-order (FO) methods. These steps also improved the Bayesian estimates of CL. These studies support the following recommendations: (1) avoid collecting single-trough concentrations unless there is reasonable knowledge about the PK of the drug; (2) if collecting single-trough concentrations is inevitable, avoid estimating all parameters when modeling single-trough concentration data; (3) use prior information by modeling the single-trough concentration data along with dense data from other studies; and (4) use Bayes estimates if the PK model and its parameters are known with reasonable certainty.     

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)     

Phenobarbitone population pharmacokinetics from routine clinical data: role of patient characteristics for estimating dosing regimens.

Routine clinical pharmacokinetic data collected from patients receiving phenobarbitone have been analysed to evaluate the role of patient characteristics for estimating dosing regimens. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of phenobarbitone was described using a one-compartment steady-state model. The effect of a variety of developmental and demographic factors on clearance was investigated. NONMEM estimates indicated a nonlinear function of total body weight as the optimum adjustment of phenobarbitone clearance. Concomitant administration of phenobarbitone and other antiepileptic drugs showed a decrease of phenobarbitone clearance in young children. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady-state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.