Population pharmacokinetics of amphotericin B in children with malignant diseases

AIMS: To construct a population pharmacokinetic model for the antifungal agent, amphotericin B (AmB), in children with malignant diseases. METHODS: A two compartment population pharmacokinetic model for AmB was developed using concentration-time data from 57 children aged between 9 months and 16 years who had received 1 mg kg(-1) day(-1) doses in either dextrose (doseform=1) or lipid emulsion (doseform=2). P-Pharm (version 1.5) was used to estimate the basic population parameters, to identify covariates with significant relationships with the pharmacokinetic parameters and to construct a Covariate model. The predictive performance of the Covariate model was assessed in an independent group of 26 children (the validation group). RESULTS: The Covariate model had population mean estimates for clearance (CL), volume of distribution into the central compartment (V) and the distributional rate constants (k12 and k21) of 0.88 l h(-1), 9.97 l, 0.27 h(-1) and 0.16 h(-1), respectively, and the intersubject variability of these parameters was 19%, 49%, 55% and 48%, respectively. The following covariate relationships were identified: CL (l h(-1)) = 0.053 + 0.0456 weight (0.75) (kg) + 0.242 doseform and V (l) = 7.11 + 0.107 weight (kg). Our Covariate model provided unbiased and precise predictions of AmB concentrations in the validation group of children: the mean prediction error was 0.0089 mg l(-1) (95% confidence interval: -0.0075, 0.0252 mg l(-1)) and the root mean square prediction error was 0.1245 mg l(-1) (95% confidence interval: 0.1131, 0.1349 mg l(-1)). CONCLUSIONS: A valid population pharmacokinetic model for AmB has been developed and may now be used in conjunction with AmB toxicity and efficacy data to develop dosing guidelines for safe and effective AmB therapy in children with malignancy.     

Population pharmacokinetics of high-dose methotrexate in children with acute lymphoblastic leukaemia

OBJECTIVE: To develop and a priori validate a methotrexate population pharmacokinetic model in children with acute lymphoblastic leukaemia (ALL), receiving high-dose methotrexate followed by folinic acid rescue, identifying the covariates that could explain part of the pharmacokinetic variability of methotrexate. METHODS: The study was carried out in 49 children (aged 6 months to 17 years) who received high-dose methotrexate (3 g/m(2) per course) in long-term treatment. In an index group (37 individuals; 1236 methotrexate plasma concentrations), a population pharmacokinetic model was developed using a nonlinear mixed-effects model. The remaining patients' data (12 individuals; 278 methotrexate plasma concentrations) were used for model validation. Age, sex, total bodyweight (TBW), height, body surface area, lowest urine pH during infusion, serum creatinine, ALT, AST, folinic acid dose and length of rescue were analysed as possible covariates. The final predictive performance of the pharmacokinetic model was tested using standardised mean prediction errors. RESULTS: The final population pharmacokinetic model (two-compartmental) included only age and total bodyweight as influencing clearance (CL) and volume of distribution of central compartment (V(1)). For children aged < or =10 years: CL (L/h) = 0.287 . TBW(0.876); V(1) (L) = 0.465 . TBW, and for children aged >10 years: CL (L/h) = 0.149 . TBW; V(1) (L) = 0.437 . TBW. From the base to the final model, the inter-individual variabilities for CL and V(1) were significantly reduced in both age groups (30-50%). The coefficients of variation of the pharmacokinetic parameters were <30%, while residual and inter-occasional coefficients maintained values close to 40%. Validation of the proposed model revealed the suitability of the model. CONCLUSION: A methotrexate population pharmacokinetic model has been developed for ALL children. The proposed model could be used in Bayesian algorithms with a limited sampling strategy to estimate the systemic exposure of individual patients to methotrexate and adapt both folinic acid rescue and methotrexate dosing accordingly.     

Population pharmacokinetics of raltitrexed in patients with advanced solid tumours

AIMS: To investigate the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and to identify patient covariates contributing to the interpatient variability in the pharmacokinetics of raltitrexed. METHODS: Patient covariate and concentration-time data were collected from patients receiving 0.1-4.5 mg m(-2) raltitrexed during the early clinical trials of raltitrexed. Data were fitted using nonlinear mixed effects modelling to generate population mean estimates for clearance (CL) and central volume of distribution (V). The relationship between individual estimates of the pharmacokinetic parameters and patient covariates was examined and the influence of significant covariates on the population parameter estimates and their variance was investigated using stepwise multiple linear regression. The performance of the developed model was tested using an independent validation dataset. All patient data were pooled in the total cohort to refine the population pharmacokinetic model for raltitrexed. RESULTS: three-compartment pharmacokinetic model was used to fit the concentration-time data of raltitrexed. Estimated creatinine clearance (CL(CR)) was found to influence significantly the CL of raltitrexed and explained 35% of variability in this parameter, whilst body weight (WT) and serum albumin concentrations (ALB) accounted for 56% of the variability in V. Satisfactory prediction (mean prediction error 0.17 micro g l(-1) and root mean square prediction error 4.99 micro g l(-1)) of the observed raltitrexed concentrations was obtained in the model validation step. The final population mean estimates were 2.17 l h(-1)[95% confidence interval (CI) 2.06, 2.28] and 6.36 l (95% CI 6.02, 6.70) for CL and V, respectively. Interpatient variability in the pharmacokinetic parameters was reduced (CL 28%, V 25%) when influential covariates were included in the final model. The following covariate relationships with raltitrexed parameters were described by the final population model: CL (l h(-1)) = 0.54 + 0.02 CL(CR) (ml min(-1)) and V (l) = 6.64 + 0.08 WT (kg) - 0.16 ALB (g l(-1)). CONCLUSIONS: A population pharmacokinetic model has been developed for raltitrexed in patients with advanced cancer. Pharmacokinetic parameters of raltitrexed are markedly influenced by the patient's renal function, body weight and serum albumin levels, which may be taken into account in dose individualization. The use of influential covariates to guide anticancer dosage selection may result in less variability in drug exposure and potentially a better clinical outcome.     

Population pharmacokinetic modeling of flurbiprofen

The paper is to report the establishment of a population pharmacokinetic model for flurbiprofen (FP), an active metabolite of flurbiprofen axetil (FA). 246 FP serum concentration and clinical data were perspectively collected from 23 general anaesthesia patients receiving FA intravenously before operation in Dentofacial Surgery and Otorhinolaryngology Department of the First Affiliated Hospital of Fujian Medical University. Population pharmacokinetic data analysis was performed using NONMEM software. The measure of Bootstrap was applied for internal validation, while Visual Predictive check was adopted for external validation. The data of FP correspond with two-compartment model. The body weight (WT) had conspicuous effect on clearance and volume of central compartment, while sex, age and daily dose of administration had no marked effect on pharmacokinetic parameter of FP. The basic model was described as follows: CL (L x h(-1)) = 1.28x EXP(ETA(1)), V1 (L) = 5.03x EXP(ETA(2)), Q (L x h(-1)) = 8.5 x EXP(ETA(3)), V2 (L) = 4.39 x EXP(ETA(4)). The final model was described as follows: CL (L x h(-1)) = 1.32 x (WT/60) x EXP(ETA(1)), V1 (L) = 5.23 x (WT/60) x EXP(ETA(2)), Q (L x h(-1)) = 8.45 x EXP(ETA(3)), V2 (L) = 4.37 x EXP(ETA(4)). The population typical value of CL, V1, Q and V2 were: 1.32 L x h(-1), 5.23 L, 8.45 L x h(-1) and 4.37 L, respectively. Bootstrap and visual predictive check show that the final model of FP is stable, effective and predictable. A novel population pharmacokinetic model is developed to estimate the individual pharmacokinetic parameter for patients intravenous injecting FA in terms of patients' characteristics and dosing history, and to design a prior dosage regimen.     

Validation of a population pharmacokinetic model for adjunctive lamotrigine therapy in children

AIMS: This analysis was performed to validate a previously developed population pharmacokinetic model for lamotrigine in order to establish a basis for dosage recommendations for children. METHODS: (a) The importance of the covariates in the final model was confirmed using the model validation dataset. Population and individual (Bayesian estimate) pharmacokinetic parameters were estimated using both the initial model, which included none of the covariates, and the final model. Accuracy and precision of parameter estimation and of concentration prediction were compared between the two models. (b) The performance in predicting the validation concentrations by the final model parameters obtained previously from the model development dataset was assessed. (c) The parameters of the final model were refined using a dataset combining both the development and validation data. RESULTS: Prediction performance of the final pharmacostatistical model was superior to that of the initial model. The results of the validation confirmed that concomitant antiepileptic drugs that increased or reduced lamotrigine clearance in adults had similar effects in children. The validation also verified the linear relationship between weight and clearance. The previously seen small sex effect on clearance was found statistically insignificant. CONCLUSIONS: The current analysis confirmed the previous findings. To achieve the same concentrations, children receiving enzyme-inducing antiepileptic drugs without valproate require higher doses than those receiving valproate; and heavier children require higher doses.     

Pharmacokinetics of intravenous busulfan in children prior to stem cell transplantation

AIMS: Intravenous formulations of busulfan have recently become available. Although busulfan is used frequently in children as part of a myeloablative regimen prior to bone marrow transplantation, pharmacokinetic data on intravenous busulfan in children are scarce. The aim was to investigate intravenous busulfan pharmacokinetics in children and to suggest a limited sampling strategy in order to determine busulfan systemic exposure with the minimum of inconvenience and risk for the patient. METHODS: Plasma pharmacokinetics after the first administration was investigated in six children using nonlinear mixed effect modelling. RESULTS: Pharmacokinetics showed little variability and were described adequately with a one-compartment model (population estimates CL,av=0.29 l h(-1) kg(-1); V,av=0.84 l kg(-1); t(1/2)=1.7-2.8 h). Combined with limited sampling and a Bayesian fitting procedure, the model can adequately estimate the systemic exposure to intravenous busulfan, which in children appears to be at the lower end of the adult range. CONCLUSIONS: Busulfan systemic exposure in children during intravenous administration can be estimated adequately with limited sampling and a Bayesian fitting procedure from a one-compartment model. Intravenous busulfan pharmacokinetics in children should be the subject of more research.     

Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients

AIMS: The purpose of this study was to describe the population pharmacokinetics of intravenous and oral tacrolimus (FK506) in 20 Asian paediatric patients, aged 1-14 years, following liver transplantation and to identify possible relationships between clinical covariates and population parameter estimates. METHODS: Details of drug dosage histories, sampling times and concentrations were collected retrospectively from routine therapeutic drug monitoring data accumulated for at least 4 days after surgery. Before analysis, patients were randomly allocated to either the population data set (n = 16) or a validation data set (n = 4). The population data set was comprised of 771 concentration measurements of patients admitted over the last 3 years. Population modelling using the nonlinear mixed-effects model (NONMEM) program was performed on the population data set, using a one-compartment model with first-order absorption and elimination. Population average parameter estimates of clearance (CL), volume of distribution (V) and oral bioavailability (F) were sought; a number of clinical and demographic variables were tested for their influence on these parameters. RESULTS: The final optimal population models related clearance to age, volume of distribution to body surface area and bioavailability to body weight and total bilirubin concentration. Predictive performance of this model evaluated using the validation data set, which comprised 86 concentrations, showed insignificant bias between observed and model-predicted blood tacrolimus concentrations. A final analysis performed in all 20 patients identified the following relationships: CL (l h-1) = 1.46 *[1 + 0. 339 * (AGE (years) -2.25)]; V (l) = 39.1 *[1 + 4.57 * (BSA (m2)-0. 49)]; F = 0.197 *[1 + 0.0887 * (WT (kg) -11.4)] and F = 0.197 *[1 + 0.0887 * (WT (kg) -11.4)] * [1.61], if the total bilirubin > or = 200 micromol l-1. The interpatient variabilities (CV%) in CL, V and F were 33.5%, 33.0% and 24.1%, respectively. The intrapatient variability (s.d.) among observed and model-predicted blood concentrations was 5.79 ng ml-1. CONCLUSIONS: In this study, the estimates of the pharmacokinetic parameters of tacrolimus agreed with those obtained from conventional pharmacokinetic studies. It also identified significant relationships in Asian paediatric liver transplant patients between the pharmacokinetics of tacrolimus and developmental characteristics of the patients.     

Melphalan pharmacokinetics in children with malignant disease: influence of body weight, renal function, carboplatin therapy and total body irradiation

AIM: To analyse the pharmacokinetics of melphalan in 52 children (0.3-18 years) and determine whether any clinical factors affect the pharmacokinetic parameters Additionally, to examine whether a test melphalan dose can predict the pharmacokinetics of a full dose, when there are 5 intervening days of carboplatin therapy. METHODS: Melphalan concentrations were measured in 14 blood samples collected from each child following doses ranging from 30 to 180 mg m(-2). The pharmacokinetics were analysed with Kinetica 4.0. RESULTS: Children who did not have carboplatin (n = 27) had median melphalan clearance (CL) of 15.5 l h(-1) m(-2) (interquartile range: 12.4-19.9 l h(-1) m(-2)) and steady state volume of distribution (Vss) of 14.9 l m(-2) (interquartile range: 12.7-18.3 l m(-2)). Children who had carboplatin (n = 25) had 34% lower median CL (10.2 l h(-1) m(-2)) and 18% lower median Vss (12.2 l m(-2)) (P < 0.001). Melphalan elimination was impaired in a separate group of three children given concomitant carboplatin and etoposide. Stepwise multiple linear regression indicated that weight, carboplatin, glomerular filtration rate (GFR) and total body irradiation (TBI) significantly affected CL, while weight and carboplatin influenced Vss. A test dose (10 mg m(-2)) tended to underpredict the area-under-the-concentration-vs.-time-curve for a full (180 mg m(-2)) dose in 19 individuals given carboplatin. CONCLUSIONS: In children, melphalan CL is influenced by weight, carboplatin, TBI and GFR. Vss is influenced by weight and carboplatin.     

Population pharmacokinetics model and limited sampling strategy for intravenous vinorelbine derived from phase I clinical trials

AIMS: a) To characterize the pharmacokinetics of intravenous vinorelbine, b) to use a population analysis for the identification of patient covariates that might appreciably influence its disposition and c) to define a limited sampling strategy for further Bayesian estimation of individual pharmacokinetic parameters. METHODS: All data were collected from 64 patients (99 courses) entered in three different phase I trials that have been previously reported. All patients received vinorelbine as a 20 min infusion with dose levels ranging from 20-45 mg m-2. The population pharmacokinetic model was built in a sequential manner on a subset of two-thirds of the data, starting with a covariate-free model then progressing to a covariate model using the nonlinear-mixed effect methodology. The remaining one-third of the data were used to validate several sparse sampling designs. RESULTS: A linear three-compartment model characterized vinorelbine blood concentrations (n=1228). Two primary pharmacokinetic parameters (total clearance and volume of distribution) were related to various combinations of covariates. The relationship for total clearance (CLtotal (l h-1)=29.2xBSAx(1-0.0090 Plt)+6.7xWt/Crs) was dependent on the patient's body surface area (BSA), weight (Wt), serum creatinine (Crs) and platelet count before administration (Plt). The optimal limited sampling strategy consisted of a combination of three measured blood concentrations; the first immediately before the end of infusion or 20 min later, the second at either 1 h, 3 h or 6 h and the third at 24 h after drug administration. CONCLUSIONS: A population pharmacokinetic model and a limited sampling strategy for intravenous vinorelbine have been developed. This is the first population analysis performed on the basis of a large phase I database that has identified clinical covariates influencing the disposition of i.v. vinorelbine. The model can be used to obtain accurate Bayesian estimates of pharmacokinetic parameters in situations where extensive pharmacokinetic sampling is not feasable.     

Population pharmacokinetic model of carbamazepine derived from routine therapeutic drug monitoring data

The aim of the present study was to develop a population pharmacokinetic model of carbamazepine from routine therapeutic drug monitoring data. Steady-state carbamazepine plasma concentrations determined by homogenous enzyme immunoassay technique, dosing history including cotherapy, schedule of blood sampling, and patients' demographic characteristics were collected retrospectively from patients' chart histories. A one-compartment model was fitted to the data using nonlinear mixed effects modeling. The influence of weight, age, gender, smoking, allergy, carbamazepine daily dose, and cotherapy on clearance (CL/F) was evaluated. Additionally, bioavailability of controlled-release relative to immediate-release tablets was assessed. Two hundred sixty-five patients (423 concentrations) were used to develop a population pharmacokinetic model. The population estimate of CL/F from the base model was 5.14 L/h with interindividual variability of 50.20%. Patients' gender, age, smoking, allergy, cotherapy with lamotrigine and benzodiazepines had no effect on CL/F. Patient weight (WT), daily carbamazepine dose (DCBZ), daily dose of phenobarbitone (DPB) and valproic acid (VPA), when its daily dose exceeded 750 mg, significantly influenced CL/F and were included in the final model:[equation: see text] where VPA is 1 if dose is greater than 750 mg or 0 otherwise. No difference in bioavailability of carbamazepine between controlled- and immediate-release tablets was detected. The model predictions in the validation set had no bias and satisfactory precision. The model can be used for estimation of carbamazepine CL/F in individual patients in the postautoinduction phase and for selection of optimum dosing regimen in routine patient care.     

A comparison of two sparse sampling population pharmacokinetic approaches for the estimation of pharmacokinetic parameters in children

BACKGROUND AND OBJECTIVE: The objectives of this study were to assess pharmacokinetic parameters (clearance, volume and half-life) in children using sparse sampling population as well as Bayesian (post hoc) approach. METHODS: Three drugs were selected for this study. Two sparse sampling methods (variable or fixed) using population and Bayesian approaches were used to assess pharmacokinetic parameters in children following a single oral dose. The initial estimates of the model parameters and inter- and intrasubject variability were obtained from the pharmacokinetic studies conducted in adults. The estimated pharmacokinetic parameters using sparse sampling (3 blood samples) were compared with the pharmacokinetic parameters obtained by extensive sampling (> or = 7 blood samples). RESULTS AND CONCLUSIONS: The results indicated that both variable and fixed sampling approaches could be used to estimate mean population as well as individual pharmacokinetic parameters in children with fair degree of accuracy. The methods described here can be used to assess either population or individual pharmacokinetic parameters in children, provided there is a prior knowledge of the pharmacokinetics of a drug in adult population.     

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。     

Assessment of the validity of a population pharmacokinetic model for epirubicin

AIMS: The aim of this study was to evaluate a population model for epirubicin clearance using internal and external validation techniques. METHODS: Jackknife samples were used to identify outliers in the population dataset and individuals influencing covariate selection. Sensitivity analyses were performed in which serum aspartate transaminase (AST) values (a covariate in the population model) or epirubicin concentrations were randomly changed by +/-10%. Cross-validation was performed five times, on each occasion using 80% of the data for model development and 20% to assess the performance of the model. External validation was conducted by assessing the ability of the population model to predict concentrations and clearances in a separate group of 79 patients. RESULTS: Structural parameter estimates from all jackknife samples were within 7.5% of the final population estimates and examination of log likelihood values indicated that the selection of AST in the final model was not due to the presence of outliers. Alteration of AST or epirubicin concentrations by +/-10% had a negligible effect on population parameter estimates and their precision. In the cross-validation analysis, the precision of clearance estimates was better in patients with AST concentrations>150 U l-1. In the external validation, epirubicin concentrations were over-predicted by 81.4% using the population model and clearance values were also poorly predicted (imprecision 43%). CONCLUSIONS: The results of internal validation of population pharmacokinetic models should be interpreted with caution, especially when the dataset is relatively small.     

Population pharmacokinetics of (R)-, (S)- and rac-methadone in methadone maintenance patients

AIM: To construct a population pharmacokinetic model for methadone enantiomers in the setting of methadone maintenance treatment for opioid dependence. METHODS: A population pharmacokinetic model was developed using P-Pharm software for rac-, (R)- and (S)-methadone using data (8-13 plasma samples per subject) obtained from 59 methadone maintenance patients during one interdosing interval at steady state. The patients were randomly assigned to either a development (n = 38) or a validation dataset (n = 21). The model was refined by inclusion of all subjects to construct a final basic model, which was used to construct a covariate model. RESULTS: A population-based two-compartment open model with first-order absorption and lag time was developed and validated for all analytes. The population geometric mean (coefficient of variation) of maximum a posteriori probability Bayesian estimated values for clearance, terminal half-life and volume of distribution at steady-state of the active (R)-enantiomer were 8.7 (42%) l h(-1), 51 (45%) h and 597 (45%) l, respectively. For all analytes, the volume of the central compartment was decreased with increasing plasma alpha(1)-acid glycoprotein concentration and was lower in females, while the delay in absorption was longer at higher doses. No covariates were identified for apparent oral clearance. The apparent oral clearance of (R)-methadone (geometric mean ratio; 95% confidence interval) was 105% (99, 110), that of (S)-methadone (P = 0.19), while (R)-methadone V(c)/F (154%; 151, 157), V(dss) /F (173%; 164, 183), t(1/2beta) (162%; 153, 172) and mean residence time (166%; 156, 176) were significantly greater (P < 0.0001) than for (S)-methadone. The population pharmacokinetic models were able to predict accurately oral clearance values from limited (one or two samples) blood sampling protocols. CONCLUSIONS: The substantial stereoselectivity in methadone disposition reinforces the potential for misinterpretation of racemic methadone disposition data. The marked interindividual variability in (R)-methadone clearance, with no covariates identified, highlights the need for alternative methods to determine an individual's metabolic clearance. The ability to predict (R)-methadone clearance from one to two blood samples at steady state may prove clinically useful if a drug-drug interaction or poor adherence are suspected and guide the prescriber in deciding if a client's request for a dose increase is warranted or whether an alternative opioid would be more appropriate.     

Easy-to-use, accurate and flexible individualized Bayesian limited sampling method without fixed time points for ciclosporin monitoring after liver transplantation

BACKGROUND: New methods to estimate the systemic exposure to ciclosporin such as the level 2 h after dosing and limited sampling formulas may lead to improved clinical outcome after orthotopic liver transplantation. However, most strategies are characterized by rigid sampling times. AIM: To develop and validate a flexible individualized population-pharmacokinetic model for ciclosporin monitoring in orthotopic liver transplantation. METHODS: A total of 62 curves obtained from 31 patients at least 0.5 year after orthotopic liver transplantation were divided into two equal groups. From 31 curves, relatively simple limited sampling formulas were derived using multiple regression analysis, while using pharmacokinetic software a two-compartment population-pharmacokinetic model was derived from these same data. We then tested the ability to estimate the AUC by the limited sampling formulas and a different approach using several limited sampling strategies on the other 31 curves. The new approach consists of individualizing the mean a priori population-pharmacokinetic parameters of the two-compartment population-pharmacokinetic model by means of maximum a posteriori Bayesian fitting with individual data leading to an individualized population-pharmacokinetic limited sampling model. From the individualized pharmacokinetic parameters, AUC(0-12h) was calculated for each combination of measured blood concentrations. The calculated AUC(0-12h) both from the limited-sampling formulas and the limited-sampling model were compared with the gold standard AUC(0-12h) (trapezoidal rule) by Pearson's correlation coefficient and prediction precision and bias were calculated. RESULTS: The AUC(0-12h) value calculated by individualizing the population-pharmacokinetic model using several combinations of measured blood concentrations: 0 + 2 h (r(2) = 0.94), 0 + 1 + 2 h (r(2) = 0.94), 0 + 1 + 3 h (r(2) = 0.92), 0 + 2 + 3 h (r(2) = 0.92) and 0 + 1 + 2 + 3 h (r(2) = 0.96) had excellent correlation with AUC(0-12h), better than limited sampling formulas with less than three sampling time points. Even trough level with limited sampling method (r(2) = 0.86) correlated better than the level after 2 h of dosing (r(2) = 0.75) or trough level (r(2) = 0.64) as single values without limited sampling method. Moreover, the individualized population-pharmacokinetic model had a low prediction bias and excellent precision. CONCLUSION: Multiple rigid sampling time points limit the use of limited sampling formulas. The major advantage of the Bayesian estimation approach presented here, is that blood sampling time points are not fixed, as long as sampling time is known. The predictive performance of this new approach is superior to trough level and that after 2 h of dosing and at least as good as limited sampling formulas. It is of clear advantage in busy out-patient clinics.     

Cefepime in intensive care unit patients: validation of a population pharmacokinetic approach and influence of covariables

AIM: The purpose of our study was to define and validate a population-pharmacokinetic model including the influence of patients' characteristics on the pharmacokinetics of cefepime. PATIENTS AND METHODS: A total of 55 patients were randomized in Group 1 (34 patients, 320 cefepime concentrations) for the model building and Group 2 (21 patients, 196 cefepime concentrations) for the validation group. They received cefepime as 2 g A 2 or as 4 g continuously. The population pharmacokinetic analysis was carried out using NONMEM and a baseline model was constructed for studying the influence of demographic and biological variables. The model was then validated by a comparison of the predicted and observed concentrations in Group 2. A final model was elaborated from the whole population. RESULTS: Total clearance (CL) was significantly correlated with the serum creatinine (CREA) and the central volume of distribution (V1) was correlated with the body weight (WT). The final model was: CL = 7.14 + (-0.0133 A CREA). V1 = (-16.8) + (0.475 A WT). Q (intercompartmental clearance) = 10.5. V2 = 18.1. The mean pharmacokinetic parameters and their individual variability were: CL (8.24 l/h, 45%), V1 (20.89 l, 60%), V2 (17.95 l, 49%), total volume (38.85 l, 42%) and Q (10.56 l/h, 9%). The bias (1.07 mg/l, IC 95% = -40.46 -+42.60), precision (21.19%) and AFE (1.15) demonstrated the performance of the model. CONCLUSION: We have developed and validated a pharmacokinetic model to estimate cefepime concentrations. We showed that serum creatinine and body weight are factors that may influence the standard dose of cefepime. Our model enabled us to predict cefepime concentrations in other patients.     

Development of a pharmacokinetic and Bayesian optimal sampling model for individualization of oral busulfan in hematopoietic stem cell transplantation

Therapeutic drug monitoring is used to minimize toxicity and maximize the therapeutic efficacy of busulfan, which shows high intra- and interpatient pharmacokinetic variability and erratic oral absorption. This study was designed to develop a pharmacokinetic model that could accommodate the erratic oral absorption of busulfan and to use this model to develop an optimal sparse pharmacokinetic sampling strategy to improve the precision and efficiency of therapeutic drug monitoring. Twenty-one pharmacokinetic profiles were collected from 12 patients receiving oral busulfan before hematopoietic stem cell transplantation. Each pharmacokinetic profile was defined by 5 to 9 plasma concentrations. Candidate pharmacokinetic models were initially fit to the data by maximum likelihood, with model discrimination by Akaike's Information Criterion. Maximum likelihood results were used to derive Bayesian previous parameter estimates, and D-optimal design was used to determine optimal sparse sampling strategies. Each candidate sampling strategy was tested in each patient by comparing the resultant Css obtained from the sparse strategy to the actual Css derived from each patient's full pharmacokinetic dataset. The final model was a 1-compartment model, with oral busulfan absorbed in 1 to 3 phases, and fit the data well. All limited sampling models tested were unbiased in their results, and a 4-sample scheme proved to adequately characterize busulfan pharmacokinetics, and should allow for a reduced sampling frequency for therapeutic drug monitoring.     

Population pharmacokinetics of APOMINE: a meta-analysis in cancer patients and healthy males

AIMS: 1) To characterize the population pharmacokinetics of apomine in healthy males and in male and female patients with solid tumours and 2) to understand more fully the influence of induction and between- and within-subject variability on exposure to drug using Monte Carlo simulation. METHODS: Apomine was administered once- or twice-daily with or without food in single and multiple oral doses of 30-2100 mg to healthy males (n = 19) and patients with solid tumours (n = 19). The data were divided into model development and validation sets. Models were developed using standard population methods. These were the identification of an appropriate base model, calculation of the empirical Bayes estimates of the primary pharmacokinetic parameters, covariate screening, forward stepwise addition of covariates using the likelihood ratio test as a model selection criteria, and backwards elimination to obtain the final model. To study the influence of data from individual subjects, the model development dataset was subjected to the delete-1 jack-knife and the final model was fitted to each jack-knifed dataset. Principal components analysis of the jack-knifed matrix of model parameters identified two influential subjects who were removed from the dataset, and the final model contained data from the remaining subjects. Model validation was examined using goodness of fit statistics and relative error measures using independent datasets from cancer patients. The model provided a reasonable approximation to the pharmacokinetic measurements in the validation datasets. Computer simulations were undertaken to understand further the pharmacokinetics of apomine in otherwise healthy females, a population not yet studied. RESULTS: Apomine pharmacokinetics were complex and consistent with a two-compartment model with a lag-time. Apparent oral clearance at baseline and apparent volume of distribution at steady-state were larger in healthy males than in cancer patients (41 ml h(-1) and 14.1 l vs 10 ml h(-1) and 8.9 l, respectively, for a 75 kg person). Clearance was time-variant showing a maximal increase with full induction of 320 ml h(-1), independent of patient type. The time to reach 50% maximal induction was about 2 days. The fraction of drug absorbed was relatively constant at doses less than 100-200 mg once daily but decreased at higher doses. Food also decreased relative bioavailability by 36%. Patient characteristics had no effect on apomine pharmacokinetics except for weight, which was proportional to the volume of the central compartment. Between-subject variability (68% for clearance, 30% for central volume, and 141% for peripheral volume) was moderate to large and independent of patient type. Inter-occasion variability was small (18% for both clearance and central volume). Residual variability was modelled with an additive and proportional error model. Cancer patients had slightly higher plasma concentrations than healthy males but this difference was probably not clinically significant. Steady-state was reached in about 3-4 days after once-daily drug administration. The half-life of apomine after three weeks of once-daily dosing was 41 h in cancer patients and 32 h in healthy males. CONCLUSIONS: A population model for apomine has been developed has been developed that characterizes its pharmacokinetics in cancer patients and healthy subjects under a variety of conditions.     

Development and validation of limited sampling strategies for prediction of the systemic exposure to the novel anticancer agent E7070 (N-(3-chloro-7-indolyl)-1,4-benzenedisulphonamide)

AIMS: E7070 is a novel, sulphonamide anticancer agent currently under clinical development for the treatment of solid tumours. The aim of this study was to develop and validate limited sampling strategies for the prediction of E7070 exposure in two different treatment schedules for phase II studies using the Bayesian estimation approach. METHODS: Data from two phase I dose finding studies were used in which E7070 was administered either as a single 1 h infusion or as a daily 1 h infusion for 5 days. Plasma concentration-time data from 75 patients were randomly divided into an index data set, used for the development of the strategies, and a validation data set. Population pharmacokinetic parameters were derived on the basis of the index data set. The D-optimality algorithm was used for the selection of optimal time points for both treatment schedules. The developed strategies were compared by assessment of their predictive performance of exposure, expressed as AUC (area under the plasma concentration vs time curve), in the validation data set. RESULTS: The developed population pharmacokinetic model comprised three compartments, with saturable distribution to one peripheral compartment and both linear and saturable elimination from the central compartment. For the 1 h infusion, a four sample strategy was selected which resulted in unbiased and accurate predictions of AUC (bias 0.74%, precision 13%). A five sample strategy was generated for the daily times five schedule yielding unbiased (bias 3.2%) and precise (12% precision) predictions of AUC. CONCLUSIONS: Optimal sampling strategies were developed and validated for estimation of E7070 exposure in two different treatment schedules. Both schedules enabled accurate and unbiased predictions of AUC.     

Population pharmacokinetics of weekly docetaxel in patients with advanced cancer

AIMS: Previous pharmacokinetic studies of the 3-weekly regimen (100 mg m(-2) every 3 weeks) of docetaxel have shown that docetaxel clearance is affected by liver function, body surface area, age, serum alpha1-acid glycoprotein and cytochrome P450 3A4 (CYP3A4) activity. However, the pharmacokinetics of a weekly docetaxel (40 mg m(-2) week(-1)) schedule are not well characterized. The aims of this study were (a) to investigate the pharmacokinetics of docetaxel (40 mg m(-2) week(-1)) using sparse concentration-time data collected from patients with advanced cancer and (b) to utilize a population pharmacokinetic approach to identify patient covariates that significantly influence the clearance of docetaxel when administered according to this regimen. METHODS: A two-compartment pharmacokinetic model was used to describe the docetaxel concentration-time data from 54 patients with advanced cancer. The mean population and individual posterior Bayesian estimates of docetaxel clearance were estimated using P-PHARM. The relationships between docetaxel clearance and 21 covariates were investigated. This included estimates of CYP3A4 function in each patient using the erythromycin breath test (1/tmax). Significant covariates were included into the final population pharmacokinetic model. Pharmacokinetic models were validated using a data splitting approach with a dataset consisting of 16 patients. RESULTS: Significant relationships were found between docetaxel clearance and 1/tmax (erythromycin breath test parameter) and several of the liver function enzymes and CL was best described by the equation; CL = 21.51 + 217 (1/tmax) - 0.13 (ALT). This final population pharmacokinetic model provided both precise and unbiased predictions of docetaxel concentrations in a validation group of patients and an estimate of the population mean (95% confidence interval) clearance of docetaxel was 30.13 l h(-1) (12.54, 46.04 l h(-1)) with an intersubject variability 30%. CONCLUSIONS: A population pharmacokinetic model has been developed and validated for weekly docetaxel (40 mg m(-2)) in patients with advanced cancer. These results indicate that CYP3A4 activity and hepatic function have an impact on the pharmacokinetics of docetaxel when administered weekly.