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.     

Population Pharmacokinetics of Carbamazepine in Indian Epileptic Patients

The aim of this study was to describe the population pharmacokinetics of carbamazepine in Indian epileptic patient population. The covariates evaluated were total body weight, height, age, dose, and gender. A total of 307 steady state serum concentrations were collected from 84 patients and analyzed. Population pharmacokinetic parameters were calculated using NONMEM, with one compartment first order absorption and elimination. The absorption rate was set at a fixed value of 1.2 h^?1. Exponential interindividual error and additive residual error model were developed. The model that was found to best describe the data following FO method was: Apparent Clearance (CL/F) (L/h)= 0.785 + 1.16 * (TBW/41.2) ** 0.75 * EXP (0.0976); Apparent Volume (V/F) (L) = 24.8 + 21.4 *(TBW/41.2)* EXP (0.740). Similarly the model found to best describe the data following FOCE method was CL/F (L/h) = 0.632 + 1.63 * (TBW/41.2) ** 0.75 * EXP (2.35E-12); V/F (L) = 31.8 + 56.9 *(TBW/41.2)* EXP (0.180).The final model estimates of CL/F and V/F estimated by FO method were 0.05 L/h/kg and 1.7 L/kg respectively and by FOCE method are 0.043 L/h/kg 1.43L/kg respectively. The typical body weight used for this population was 40 kg.     

A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen.

We retrospectively analyzed amikacin pharmacokinetics in 19 critically ill patients who received amikacin intravenously. Fourteen subjects (577 serum amikacin concentrations, 167 urine measurements) were studied to obtain data for population modeling, while 5 patients (267 serum amikacin concentrations, 68 urine measurements) were studied for the assessment of predictive performance. The population analysis was performed using serum and urine amikacin measurements; the renal clearance of amikacin was expressed as a function of creatinine clearance. A two-compartment model was fitted to the population data by using NONMEM. The population characteristics of the pharmacokinetic parameters (fixed and random effects) were estimated using the FOCE method. The population pharmacokinetic parameters with the interindividual variability (CV%) were as follows: slope (0.254, 126%) and intercept (3 l/h, 59.6%) of the linear model which relate the amikacin renal clearance to the creatinine clearance, initial volume of distribution (17.1 l, 22.2%), intercompartment clearance (5.22 l/h, 104%), steady state volume of distribution (55.2 l, 64.1%) and urinary elimination (67.5%, 36.3%). The Bayesian approach developed in this study accurately predicts amikacin concentrations in serum and urine and allows for the estimation of amikacin pharmacokinetic parameters, minimizing the risk of bias in the prediction.     

左旋多巴中国人群药动学模型的建立

目的:建立中国人群左旋多巴/苄丝肼复合制剂中左旋多巴的群体药动学模型。方法:前瞻性收集服用多巴丝肼片的帕金森病(PD)门诊患者稳态谷浓度97例102个血样和健康志愿者13例153个密集血样,高效液相色谱-电化学(HPLC-ECD)法测定左旋多巴(LD)血药浓度。应用NONMEM软件进行群体药动学数据分析,Bootstrap重复抽样用于模型的内部验证。另收集20例PD患者22个血样点作为验证组进行模型外部验证,计算最简模型和最终模型对验证组的平均预测误差(MPE)和平均绝对误差(MAE)对模型进行外部验证。结果:数据采用一房室模型拟合,年龄(AGE)对LD清除率有显著影响,性别(SEX)、体质量(WT)、给药剂量(TAMT)、合并用药不影响LD的药动学参数。LD的基础模型为:CL(CL/F)(L.h-1)=18.2×EXP[ETA(1)],V(V/F)(L)=48.4,ka(h-1)=2.13×EXP[ETA(2)];最终模型为:CL(CL/F)(L.h-1)=17.9×(55/AGE)0.59×(EXP[ETA(1)],V(V/F)(L)=47.5,ka(h-1)=2.14×EXP[ETA(2)]。CL、V、ka的群体典型值分别为17.9 L.h-1、47.5 L、2.14 h-1。Bootstrap重复抽样显示所建立的最终模型稳定、可靠,最终模型对验证组的MPE和MAE较最简模型有显著改善,显示模型有效,且有一定代表性。结论:根据患者的生理用药资料,结合上述模型,可估算个体药动学参数,为临床个体化给药提供参考。     

Nonlinearity detection: Advantages of nonlinear mixed-effects modeling

The purpose of this study was to address the question of whether the use of nonlinear mixed-effect models has an impact on the detection and characterization of nonlinear processes (pharmacokinetic and pharmacodynamic) in rich data obtained from a few subjects. Simulations were used to assess the difference between applying population analysis, ie, nonlinear mixed-effects models as implemented in NONMEM, and the standard 2-stage (STS) method as the data analysis method for detection and characterization of nonlinearities. Three situations were considered, 2 pharmacokinetic and 1 pharmacodynamic. Both the first-order (FO) and FO conditional estimation (FOCE) algorithms were used for the population analyses. Within each situation, rich data were simulated for 8 subjects at multiple dose levels. The true nonlinear model and a simpler linear model were fit to each data set using each of the STS, FO, and FOCE methods. Criteria were prespecified to determine when each data analysis method detected the true nonlinear model. For all 3 simulated situations, the application of population analysis with the FOCE algorithm enabled the detection and characterization of the true nonlinear models in at least a 4-fold lower dose level than the STS approach. For both of the pharmacokinetic settings, population analysis with the FO algorithm performed much more poorly than the STS approach. The superior detection and characterization of nonlinearities provided by population analysis with the FOCE algorithm should allow drug developers to better predict and define how a drug should be used in clinical practice in such situations.     

Population Pharmacokinetics of Amikacin in Indian Pediatric Patients

The aim of this study was to describe population pharmacokinetics of amikacin in Indian pediatric population. Dosage adjustment based on individual pharmacokinetic parameters is of considerable importance for effective and safe use of drugs. Extensive work on amikacin and other aminoglycosides was carried out in different pediatric patient populations but no data are available in Indian pediatric patients. In the present study 74 steady state concentrations of amikacin were analyzed from 42 patients. Pharmacostatistical work was done by using NONMEM. The covariates evaluated in this study were age, body weight, height, and sex and creatinine clearance. The model found to best describe the data following FO method was: Clearance (CL)?=?θ1*(wt/14.2)*exp. (η1) and volume (V)?=?θ2*exp (η2) and following FOCE method was: Clearance (CL) = θ1*(age/5.38) + θ3*(wt/14.2)*exp.(η1) and volume(V) = θ2*exp(η2). The final model estimates of CL and V estimated by FO method were1.02 L/h and 4.55L respectively and by FOCE method were1.07L/hr and 4.91L respectively. These parameters are utilized for individualizing the loading and maintenance doses in pediatric patients.     

Population pharmacokinetics of cisplatin in Asian Indian cancer patients

The aim of this study was to describe population pharmacokinetics of cisplatin in an Indian cancer population. Dosage adjustment based on individual pharmacokinetic parameters is of considerable importance for effective and safe use of drugs. Extensive work on cisplatin and other was carried out in different cancer patient populations, but no data are available in Indian cancer patients. In the present study 154 steady state concentrations of cisplatin were analyzed from 46 patients. Pharmacostatistical work was done by using NONMEM. The covariates evaluated in this study were age, body weight, height, sex, and creatinine clearance. The model found to best describe the data following the FO and FOCE method was: Clearance (CL)?=?θ1*(CLCR/74.92) *EXP (η1) and Volume (V)?=?{θ2 *(AGE/52.3) + θ3*(BSA/1.55)}*EXP (η2). The final model estimates of CL and V estimated by FO method were 3.02?L/h and 2.72?L, respectively, and by FOCE method were 3.39?L/h and 4.48?L, respectively. These parameters are utilized for individualizing the loading and maintenance doses in pediatric patients.     

Simulation for population analysis of Michaelis-Menten elimination kinetics

A simulation study was conducted to compare the cost and performance of various models for population analysis of the steady state pharmacokinetic data arising from a one-compartment model with Michaelis-Menten elimination. The usual Michaelis-Menten model (MM) and its variants provide no estimate of the volume of distribution, and generally give poor estimates of the maximal elimination rate and the Michaelis-Menten constant. The exact solution to the Michaelis-Menten differential equation (TRUE) requires a precise analysis method designed for estimation of population pharmacokinetic parameters (the first-order conditional estimation method) and also considerable computational time to estimate population mean parameters accurately. The one-compartment model with dose-dependent clearance (DDCL), in conjunction with the first-order conditional estimation or Laplacian method, ran approximately 20-fold faster than TRUE and gave accurate population mean parameters for a drug having a long biological half-life relative to the dosing interval. These findings suggest that the well-known MM and its variants should be used carefully for the analysis of blood concentrations of a drug with Michaelis-Menten elimination kinetics, and that TRUE, in conjunction with a precise analysis method, should be considered for estimating population pharmacokinetic parameters. In addition, DDCL is a promising alternative to TRUE with respect to computation time, when the dosing interval is short relative to the biological half-life of a drug. This work was supported in part by the Epilepsy Research Foundation, the Nakatomi Foundation, and a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, and Culture of Japan.     

Vancomycin pharmacokinetics and Bayesian estimation in pediatric patients

The vancomycin pharmacokinetic profile was characterized in six pediatric patients and the potential of nonlinear mixed effects modeling and Bayesian forecasting for vancomycin monitoring was explored using NONMEM V (1.1). Based on steady state serial vancomycin concentrations, the estimates of mean t1/2, Vd, and Cl derived by the Sawchuk and Zaske method (1) were 3.52 hours, 0.57 L/kg, and 0.12 L/h per kg, respectively. NONMEM analysis demonstrated that a weight-adjusted two-compartment model described individual patients' data better than a comparable one-compartment model. The two-compartment estimates of mean t1/2alpha, t1/2beta, Vss, and Cl were 0.80 hour, 5.63 hours, 0.63 L/kg, and 0.11 L/h per kg, respectively. The relatively long mean t1/2alpha suggests that peak vancomycin concentrations measured earlier than 4 hours postdose do not reflect postdistributional serum concentrations. NONMEM population modeling revealed that a weight-adjusted two-compartment model provided a better fit than a comparable one-compartment model. The resulting population parameters and variances were fixed in NONMEM to obtain Bayesian predictions of individual vancomycin serum concentrations. Bayesian estimation with either a single midinterval or trough sample has the potential to provide accurate and precise predictions of vancomycin concentrations. This should be evaluated using a vancomycin population pharmacokinetic model based on a larger sample of pediatric patients.     

Indications for a ceftriaxone dosing regimen in Japanese paediatric patients using population pharmacokinetic/pharmacodynamic analysis and simulation

Objectives The objective of this study was to build a ceftriaxone population pharmacokinetic model for Japanese paediatric patients and to examine the dosing regimen of ceftriaxone based on pharmacokinetic/pharmacodynamic (PK/PD) analysis. Methods The population pharmacokinetic analysis using NONMEM was based on published serum concentrations of ceftriaxone. A Monte Carlo simulation was examined to evaluate the time above the minimum inhibitory concentration (TAM) in 20 and 60 mg/kg body weight dose regimen using the population pharmacokinetic parameters. Key findings The time course of the serum concentration of ceftriaxone in paediatric patients was fitted to a two‐compartment model and body weight was incorporated to pharmacokinetic parameters as the covariate. Based on the percent TAM estimated from the final population pharmacokinetic model and the minimum inhibitory concentration (MIC) of ceftriaxone in 2004, we have predicted that the once daily administration of 20 mg/kg ceftriaxone would be effective on various infecting organisms. Conclusions A population pharmacokinetic model of ceftriaxone was built for Japanese paediatric patients based on the available data. The estimated PK/PD result confirmed the appropriateness of once daily dose of 20 mg/kg. In some patients for whom no efficacy was observed at 20 mg/kg, an increase to 60 mg/kg may be required.     

Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization.

A simulation study was performed to determine how inestimable standard errors could be obtained when population pharmacokinetic analysis is performed with the NONMEM software on data from small sample size phase I studies. Plausible sets of concentration-time data for nineteen subjects were simulated using an incomplete longitudinal population pharmacokinetic study design, and parameters of a drug in development that exhibits two compartment linear pharmacokinetics with single dose first order input. They were analyzed with the NONMEM program. Standard errors for model parameters were computed from the simulated parameter values to serve as true standard errors of estimates. The nonparametric bootstrap approach was used to generate replicate data sets from the simulated data and analyzed with NONMEM. Because of the sensitivity of the bootstrap to extreme values, winsorization was applied to parameter estimates. Winsorized mean parameters and their standard errors were computed and compared with their true values as well as the non-winsorized estimates. Percent bias was used to judge the performance of the bootstrap approach (with or without winsorization) in estimating inestimable standard errors of population pharmacokinetic parameters. Winsorized standard error estimates were generally more accurate than non-winsorized estimates because the distribution of most parameter estimates were skewed, sometimes with heavy tails. Using the bootstrap approach combined with winsorization, inestimable robust standard errors can be obtained for NONMEM estimated population pharmacokinetic parameters with > or = 150 bootstrap replicates. This approach was also applied to a real data set and a similar outcome was obtained. This investigation provides a structural framework for estimating inestimable standard errors when NONMEM is used for population pharmacokinetic modeling involving small sample sizes.     

Population pharmacokinetics,protein binding and antiarrhythmic effects of disopyramide enantiomers in arrhythmic patients

Disopyramide (DP) is widely used as an antiarrhythmic agent. The antiarrhythmic effects of its enantiomers differ from each other and its metabolism and protein binding are also stereoselective. Population pharmacokinetic parameters of DP racemate, enantiomers (S(+)-DP, R(-)-DP), and their unbound concentrations (uDP, S(+)-uDP and R(-)-uDP) were analyzed using the nonlinear mixed effect model (NONMEM) program. Data were available from 108 points of 33 arrhythmic patients on maintenance therapy with DP racemate. We evaluated the factors to which pharmacokinetic parameters are attributed and the relationships between each serum concentration and the antiarrhythmic effect. A one-compartment model was fitted to the data using NONMEM. For DP, S(+)-DP and R(-)-DP, elimination rate constants (kes) were estimated as 0.0648, 0.0663 and 0.0691/h, respectively and the mean apparent volume of distribution (Vd/F) were estimated as 63.2, 54.1 and 71.6 l, respectively. Using the ke and Vd/F values estimated by NONMEM, time-concentration curves were well fitted to the observed data. Unbound fractions of both DP enantiomers showed nonlinearity and the binding ratio of S(+)-DP was 0.84 +/- 0.07, which was higher than that of R(-)-DP [0.70 +/- 0.11 (p < 0.01)]. Unbound fractions of both DP enantiomers correlated with alpha1-acid glycoprotein (AGP) (p < 0.01). On the other hand, using NONMEM, a significant proportion of the variability of Vd/F could be attributed only to AGP (p < 0.001). NONMEM was able to clarify the pharmacokinetic features in the protein binding of DP. Individual steady state concentrations were estimated by NONMEM using the Bayesian method. The average unbound concentrations of all nine responders were higher than those of the four non-responders, even though this difference was not significant. Unbound concentrations may reflect drug concentrations in the tissue, which suggests that these concentrations may indicate an antiarrhythmic effect rather than the total concentration.     

Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: Routine clinical pharmacokinetic data

Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual kinetic variability, and residual variability, including intraindividual variability and measurement error. Individual pharmacokinetics are estimated by fitting a pharmacokinetic model to individual data. Population pharmacokinetic parameters have traditionally been estimated by doing this separately for each individual, and then combining the individual parameter estimates, the Standard Two Stage (STS) approach. Another approach, NONMEM, appropriately pools data across individuals and is therefore less dependent on individual parameter estimates. This study provides further evidence of NONMEM's validity and usefulness by comparing both approaches on simulated routine-type pharmacokinetic data arising from a monoexponential model. The estimates of population parameters (notably those describing interindividual variability) provided by the STS method are poorer than those provided by NONMEM, especially when there is considerable residual error. Further, NONMEM's estimates of population parameters do not require that the data be restricted to special types of routine data such as those obtained only at steady state, or only at peak or trough, nor do the estimates improve with such data. NONMEM's estimates do improve, however, when a data set is enhanced by the addition of single-observation-per-individual type data. Thus, population parameters can be estimated efficiently from data that simulate real clinical pharmacokinetic conditions.     

An application of nonlinear mixed-effects modeling to pharmacokinetic data exhibiting nonlinear and time-dependent behavior

A population pharmacokinetic (PK) analysis was performed on plasma concentrations of GW468816 observed in dogs after 10, 25, and 50 mg/kg/day repeated intravenous administration. A two-compartment model was fitted to the concentration-time data using the NONMEM program. Dose and time dependency of PK parameters was investigated. Selection of the best model was performed using a stepwise approach. A Michaelis-Menten elimination process was used to describe the PK dose dependency, whereas an interoccasion variability on V(m) (the maximum elimination rate of the Michaelis-Menten elimination process) was initially used to describe the time dependency of the PKs, and the final model included an exponential function to account for time variance on V(m). The K(m) value of the final model was 29.6 microg/mL, whereas V(m) was estimated to vary with time from 4.97 microg/h/kg at day 1 to a maximum mean value of 9.64 microg/h/kg at day 14. This approach can be applied to either rich or sparse data leading to estimates of individual parameters by using Bayesian feedback. The overall information obtained can be used to interpret toxicological and pharmacological endpoints and integrated with further in vitro-in vivo studies to supply a comprehensive PK behavior before the first time in human studies.     

Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies

BACKGROUND AND OBJECTIVES: This study examined parametric and nonparametric population modelling methods in three different analyses. The first analysis was of a real, although small, clinical dataset from 17 patients receiving intramuscular amikacin. The second analysis was of a Monte Carlo simulation study in which the populations ranged from 25 to 800 subjects, the model parameter distributions were Gaussian and all the simulated parameter values of the subjects were exactly known prior to the analysis. The third analysis was again of a Monte Carlo study in which the exactly known population sample consisted of a unimodal Gaussian distribution for the apparent volume of distribution (V(d)), but a bimodal distribution for the elimination rate constant (k(e)), simulating rapid and slow eliminators of a drug. METHODS: For the clinical dataset, the parametric iterative two-stage Bayesian (IT2B) approach, with the first-order conditional estimation (FOCE) approximation calculation of the conditional likelihoods, was used together with the nonparametric expectation-maximisation (NPEM) and nonparametric adaptive grid (NPAG) approaches, both of which use exact computations of the likelihood. For the first Monte Carlo simulation study, these programs were also used. A one-compartment model with unimodal Gaussian parameters V(d) and k(e) was employed, with a simulated intravenous bolus dose and two simulated serum concentrations per subject. In addition, a newer parametric expectation-maximisation (PEM) program with a Faure low discrepancy computation of the conditional likelihoods, as well as nonlinear mixed-effects modelling software (NONMEM), both the first-order (FO) and the FOCE versions, were used. For the second Monte Carlo study, a one-compartment model with an intravenous bolus dose was again used, with five simulated serum samples obtained from early to late after dosing. A unimodal distribution for V(d) and a bimodal distribution for k(e) were chosen to simulate two subpopulations of 'fast' and 'slow' metabolisers of a drug. NPEM results were compared with that of a unimodal parametric joint density having the true population parameter means and covariance. RESULTS: For the clinical dataset, the interindividual parameter percent coefficients of variation (CV%) were smallest with IT2B, suggesting less diversity in the population parameter distributions. However, the exact likelihood of the results was also smaller with IT2B, and was 14 logs greater with NPEM and NPAG, both of which found a greater and more likely diversity in the population studied.For the first Monte Carlo dataset, NPAG and PEM, both using accurate likelihood computations, showed statistical consistency. Consistency means that the more subjects studied, the closer the estimated parameter values approach the true values. NONMEM FOCE and NONMEM FO, as well as the IT2B FOCE methods, do not have this guarantee. Results obtained by IT2B FOCE, for example, often strayed visibly away from the true values as more subjects were studied. Furthermore, with respect to statistical efficiency (precision of parameter estimates), NPAG and PEM had good efficiency and precise parameter estimates, while precision suffered with NONMEM FOCE and IT2B FOCE, and severely so with NONMEM FO. For the second Monte Carlo dataset, NPEM closely approximated the true bimodal population joint density, while an exact parametric representation of an assumed joint unimodal density having the true population means, standard deviations and correlation gave a totally different picture. CONCLUSIONS: The smaller population interindividual CV% estimates with IT2B on the clinical dataset are probably the result of assuming Gaussian parameter distributions and/or of using the FOCE approximation. NPEM and NPAG, having no constraints on the shape of the population parameter distributions, and which compute the likelihood exactly and estimate parameter values with greater precision, detected the more likely greater diversity in the parameter values in the population studied. In the first Monte Carlo study, NPAG and PEM had more precise parameter estimates than either IT2B FOCE or NONMEM FOCE, as well as much more precise estimates than NONMEM FO. In the second Monte Carlo study, NPEM easily detected the bimodal parameter distribution at this initial step without requiring any further information. Population modelling methods using exact or accurate computations have more precise parameter estimation, better stochastic convergence properties and are, very importantly, statistically consistent. Nonparametric methods are better than parametric methods at analysing populations having unanticipated non-Gaussian or multimodal parameter distributions.     

Population pharmacokinetic study of cyclosporine based on NONMEM in Chinese liver transplant recipients

A population pharmacokinetic study of cyclosporine (CsA) was performed in liver transplant recipients. A total of 3731 retrospective drug monitoring data points at predose (C0) and 2 hours postdose (C2) were collected from 124 liver transplant recipients receiving CsA microemulsion. Population pharmacokinetic analysis was performed using the program NONMEM (nonlinear mixed-effect modeling). Various covariates potentially related to CsA pharmacokinetics were explored, and the final model was validated by a bootstrap method and by assessing the predictive performance using empiric Bayesian estimates. A one-compartment model with first-order absorption was considered. Population parameters of apparent clearance (CL/F) and volume of distribution were estimated as 23.1 L/h and 105 L, respectively. CL/F was influenced by four covariates: duration of CsA therapy (DT), hematocrit (HCT), and concurrent prednisone dose (PR). The final model for CL/F was fitted as follows: CL/F = 23.1 + 0.5 × (DT/200) - 0.07 × HCT + 0.04 × PR. The interindividual variability in CL/F, volume of distribution, and Ka calculated as coefficient of variation were 15.1%, 9.3%, and 66.0%, respectively. The intraindividual variability was 18.6%. The model fitted well with the observed data, and the bootstrap method guaranteed robustness of the population pharmacokinetic study model. Model validation was performed by a visual predictive check. Moreover, simulation was conducted to facilitate the individualized treatment based on patient information and the final model. The model to characterize population pharmacokinetic study of CsA provided better clinical individualization of CsA dosing in liver transplant recipients based on patient information and to assess patients' suitability for CsA therapy.     

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)     

NONMEM法建立大鼠外周和中枢左旋多巴群体药动学模型

目的:建立大鼠左旋多巴(levodopa, LD)群体药动学模型,考察LD药动学参数的影响因素。方法:14只大鼠随机分为高、低两个剂量组,单次灌胃给予多巴丝肼片。采用脑微透析技术收集大鼠纹状体细胞外液透析液,同时采集外周血;高效液相色谱-电化学法测定透析液及血浆LD浓度,并利用非线性混合效应模型(Nonlinear mixed effect model, NONMEM)进行群体药动学数据分析。结果:建立了包含大鼠个体间变异、个体自身变异及体质量、给药剂量等固定效应参数的统计学模型,原始数据估算的参数值均位于Bootstrap估算参数值的2.5%~97.5%范围内,视觉预测评估法显示建模大鼠外周血和中枢纹状体LD浓度基本位于90%百分位数范围之内,所建立的最终模型稳定、有效、且有较强的预测能力。体质量可影响LD药动参数K₃₂。结论:建立的群体药动学模型能较好地描述LD在大鼠中枢及外周血的药动学特点。大鼠给药剂量对LD药动参数无影响,体质量可影响LD药动参数。     

Population pharmacokinetics of carbamazepine in Chinese epilepsy patients

AIM: To investigate the pharmacokinetic profile of carbamazepine (CBZ) in Chinese epilepsy patients. MATERIALS AND METHODS: Serum samples through concentrations at steady state (n = 687) were collected prospectively from 585 patients during routine clinical care. Data were analyzed by the non-linear mixed-effect modeling (NONMEM) technique with a one-compartment model of first-order absorption and elimination. RESULTS: The important determinants of clearance (CL) were total body weight (TBW); dose; patient age over 65 years (E); and comedication with phenytoin (PHT), phenobarbital (PB), or valproic acid (VPA) when VPA daily dose was greater than 18 mg/kg. The final pharmacokinetic model for relative CL and apparent distribution volume (V) were: Equation CONCLUSION: A population pharmacokinetic model was proposed to estimate the individual CL for Chinese patients receiving CBZ in terms of patient's dose, TBW, and comedications to establish a priori dosage regimens.     

中国健康人群西布曲明的群体药动学模型的建立

目的建立中国人群中西布曲明的群体药动学模型。方法 20例男性健康志愿者口服10 mg西布曲明,于服药后0～24 h采集13个采样点采血,采用已验证的HPLC法测定血药浓度。采用非线性混合效应模型（NONMEM）进行群体药动学分析,估算药动学参数。以直观预测检验（Visual predictive check,VPC）和正态预测分布误差（Normalized predictive distribution error,NPDE）,Bootstrap法进行模型性能评估。结果以有吸收时滞的一级吸收和消除的二房室模型为西布曲明的基础药动学模型。协变量筛选未见体重、年龄可显著影响模型参数。残差模型选择指数模型。西布曲明群体药动学参数V1,V2,CL,Q,Ka,Tlag的典型值分别为：7.85 L、2.03 L、1.08 L/h、0.289 L/h、1.95/h、0.187 h;个体间变异分别为42.8%、48.2%、38.5%、27.1%、56.8%和17.8%。Bootstrape、拟合优度、VPC和NPDE的评价结果均表明模型稳定,预测结果可靠。结论用非线性混合效应模型法建立的中国人群中西布曲明的群体药动学模型,结果稳定。