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 platinum after nedaplatin administration and model validation in adult patients

AIMS: The pharmacokinetics of unbound platinum after administration of an anticancer drug nedaplatin, cis-diammineglycolateplatinum were examined using population analysis. The relevant covariates and the extent of inter- and intra-individual variability were evaluated. METHODS: In order to clarify the pharmacokinetic profile of nedaplatin, unbound platinum concentrations (789 points) in plasma after intravenous infusion of nedaplatin were obtained from 183 courses for 141 patients. Plasma concentration data were analysed by nonlinear mixed effect modelling using NONMEM to evaluate the population mean parameters and variances for inter- and intra-individual random effects. The final population model was validated by parameter sensitivity analysis using objective function mapping, the bootstrap resampling and a data-splitting technique, i.e. the Jackknife method, and the predictive performance of the final model was evaluated. RESULTS: A two-compartment pharmacokinetic model with zero-order input and first order elimination described the current data well. The significant covariates were creatinine clearance (CLcr) for clearance of platinum (CL) [population mean [95% confidence interval (CI)] CL (l h(-1)) = 4.47 (3.27, 5.67) + 0.0738 (0.0581, 0.0896) x CLcr (CLcr: ml min(-1))] and body weight (BW: kg) for volume of distribution of platinum (Vc) [Vc (l) = 12.0 (7.5, 16.5) + 0.163 (0.081, 0.246) x BW]. Inter-individual variations (CV%, 95% CI) for CL and Vc were 25.5% (20.7, 29.6) and 21.4% (17.0, 24.1), respectively, and intra-individual variation (CV%, 95% CI) was 12.6% (10.5, 14.4). The effects of pretreatment with nedaplatin or other platinum agents on clearance and volume of distribution were also tested, but no significant effect was found. The relationship between the observed and predicted unbound platinum concentration by empirical Bayesian prediction showed good correlation with no bias, suggesting that the final model explains well the observed data in the patients. The mean prediction error and root mean square prediction error (95% CI) were - 0.0164 micro g ml(-1) (- 0.4379, 0.4051) and 0.2155 micro g ml(-1) (not calculable, 0.6523), respectively. The values of mean, standard error and 95% CI for objective function mapping, the bootstrap resampling, the Jackknife estimates and the final model coincided well. CONCLUSIONS: A population pharmacokinetic model was developed for unbound platinum after intravenous infusion of nedaplatin. Only creatinine clearance was found to be a significant covariate of clearance, and BW was found to be a significant covariate of volume of distribution. These population pharmacokinetic estimates are useful for setting initial dosing of nedaplatin using its population mean and can also be used for setting appropriate dosage regimens using empirical Bayesian forecasting.     

Population pharmacokinetics of liposomal daunorubicin in children

AIMS: To investigate the population pharmacokinetics of daunorubicin in children after administration of liposomal daunorubicin (Daunoxome). METHODS: Plasma samples from 19 children with relapsed acute myeloic leukaemia and five children with other malignancies were collected. Daunoxome was administered as a 1- to 2.5 h infusion with doses ranging from 30 to 60 mg m(-2). Overall, 214 samples were analysed for daunorubicin using capillary electrophoresis, and population pharmacokinetic modelling was performed using NONMEM. RESULTS: The data were best described by a one compartment model. Inclusion of interoccasion variability in the model (16.7% for clearance) improved strongly the precision of the estimates. The inclusion of body surface area or height as a covariate decreased interindividual variability. However, the best fit was obtained using the absolute dose, and when weight was included as a covariate for clearance (CL) and volume of distribution (V ). The final parameter estimates were: CL 6.41 ml h(-1) kg(-1) +/- 0.5 51% and V 65.4 ml kg(-1) +/- 0.5 27% (population mean +/- 0.5 interindividual variability). The area under the curve at a dose of 60 mg m(-2) was 231 mg l (-1)h. CONCLUSIONS: In comparison with free daunorubicin, Daunoxome shows a low volume of distribution, a lower clearance and a lower interindividual variability in these parameters. This might be advantageous in reducing the variability in exposure to the drug.     

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.     

Population pharmacokinetics of infliximab in patients with ankylosing spondylitis

The population pharmacokinetics of infliximab were characterized in patients with active ankylosing spondylitis (n = 274). Serum infliximab concentration data, from a 2-year period, were analyzed using NONMEM. A 2-compartment linear pharmacokinetic model was chosen to describe the pharmacokinetic characteristics of infliximab in serum. Population estimates (typical value +/- standard error) were obtained from the final covariate model: clearance (CL: 0.273 +/- 0.007 L/day), volume of distribution in the central compartment (V(1): 3.06 +/- 0.057 L), intercompartment clearance (Q: 1.72 +/- 0.48 L/day), and volume of distribution in the peripheral compartment (V(2): 2.94 +/- 0.17 L). Interindividual variability for CL and V(1) was 34.1% and 17.5%, respectively. White blood cell count at baseline and the antibody-to-infliximab status were significant covariates to CL; body surface area and sex were significant covariates to V(1). The CL for patients with a positive antibody-to-infliximab status was estimated to be 41.9% to 76.7% higher than for the remaining patients. Other covariates (baseline disease activity and the concomitant medication use of prednisolone, omeprazole, nonsteroidal anti-inflammatory drugs, or analgesics) did not affect infliximab pharmacokinetics. The development of antibodies to infliximab was associated with accelerated infliximab clearance and may represent a potential underlying mechanism for an inadequate response, or loss of response, to infliximab treatment.     

Population pharmacokinetic analysis for nelfinavir and its metabolite M8 in virologically controlled HIV-infected patients on HAART

AIMS: To describe the pharmacokinetics of nelfinavir and its main metabolite M8 in HIV-infected patients with a sustained virological response, to characterize the effect of covariates and to estimate inter- and intra-individual variability in the pharmacokinetics. METHODS: Three hundred and twenty concentrations of both nelfinavir and M8 were measured in 46 patients enrolled in the COPHAR 1-ANRS 102 study. Blood samples were taken at a first visit (one sample before drug administration and four samples at fixed times after) and at a second visit 1 to 3 months later (one before and one 3 h after drug administration). The data from both visits on nelfinavir and M8 were modelled jointly in all patients using a population approach. RESULTS: A one-compartment model with first-order absorption and elimination best described nelfinavir data, with an additional compartment incorporating a first order rate-constant describing the metabolism of the drug to M8. For nelfinavir, the apparent volume of distribution (V/F ) (95% confidence interval for the mean), was 309 l (185, 516), the absorption rate constant (k(a)) was 0.4 h(-1) (0.2, 0.8), and the apparent clearance (CL/F ) was 37.3 l h(-1) (32, 44). For M8, V(m) /(Fk(m)) and CL(m)/(Fk(m)) were 866 l h(-1) (351, 2161) and 1670 l (965, 2894), respectively. The interindividual variabilities were 34.9%, 34.3% and 62.2% for V/F, CL/F and CL(m)/(Fk(m)), respectively. The interoccasion variability was 27.8% for CL/F. The mean half-lives were 05.38 h and 00.44 h for nelfinavir and M8, respectively. Significant but opposite effects of comedication with zidovudine were found on nelfinavir CL/F and M8 CL(m)/(Fk(m)), but they were not considered to be clinically relevant. CONCLUSIONS: A joint model was found to describe adequately nelfinavir and M8 concentrations and was used to estimate pharmacokinetic parameters for M8. The model can be used to build reference pharmacokinetic profiles for therapeutic drug monitoring of the drug.     

Dexamethasone as a probe for vinorelbine clearance

AIM: To assess the value of using dexamethasone as an in vivo probe for predicting vinorelbine clearance (CL). METHODS: A population approach (implemented with NONMEM) was used to analyse blood vinorelbine pharmacokinetic data from 20 patients who received a 20-min intravenous infusion of vinorelbine (from 20 to 30 mg m(-2)). Selected patient clinical data as well as known functional single CYP3A5 and ABCB1 genotype were also tested as covariates. RESULTS: The best covariate model (with +/- 95% confidence intervals) was based on dexamethasone plasma clearance (DPC) and alkaline phosphatase (ALP): vinorelbine blood CL (l h(-1)) = 39.8(+/- 4.0) x (DPC/13.2)(0.524(+/-0.322)) x (ALP/137)(-0.198(+/-0.158)). Interindividual variability in vinorelbine CL decreased from 29.7% (model without covariate) to 14.7% when including DPC and ALP. Vinorelbine CL was not correlated with body surface area (BSA) or associated with CYP3A5 and ABCB1 genotype. CONCLUSIONS: These results indicate that individualization of vinorelbine dose would be improved by using dexamethasone clearance rather than BSA. Dexamethasone merits further evaluation as a probe of CYP3A metabolism.     

Pharmacokinetic modelling of valproic acid from routine clinical data in Egyptian epileptic patients

OBJECTIVE: This paper describes a developed pharmacokinetic model for the estimation of valproic acid (VPA) clearance (CL) calculated from routine clinical data taken from Egyptian epileptic patients. METHODS: Retrospective clinical data from 81 adult and paediatric epileptic patients with one trough VPA serum concentration per patient were analysed using NONMEM to estimate drug CL and determine the influence of different covariates. A qualification group of 20 epileptic children (3-13 years old) was used to evaluate the final model. RESULTS: The population CL as estimated by base model (no covariates) was 0.581 l h(-1) with inter-individual variability (C.V. %) of 17.4% and SD of residual error was 6.82 mg l(-1). Univariate selection and backward deletion of different covariates led to the development of the final regression model of CL as follows: CL(Lh-1) = 0.101 + 0.151 * CBZ + 0.000248 * VPADD + 0.0968 * age/20 + 0.0803 * INDI, in which CBZ indicates co-administration of carbamazepine, VPADD the daily dose of VPA and INDI uncontrolled epilepsy. The between-subject variability in CL was 23.6% while the standard deviation of the residual error was 5.24 mg l(-1). The model predictions in the qualification group were found to have no bias and satisfactory precision. CONCLUSION: The population pharmacokinetic model for VPA could be used for a priori recommendation and dose optimisation of that drug in the Egyptian population of epileptic patients.     

Intravenous indometacin in preterm infants with symptomatic patent ductus arteriosus. A population pharmacokinetic study

AIMS: To characterize the population pharmacokinetics of indometacin in preterm infants with symptomatic patent ductus arteriosus and to investigate the influence of various factors on the response to treatment. METHODS: Data were collected from 35 infants (gestational age 25-34 weeks; postnatal age 1-77 days) in neonatal units in Belfast and Copenhagen. Infants received an initial course of up to three doses of intravenous indometacin (0.1-0.2 mg kg(-1)) as considered appropriate by the treating physician. For those infants who did not respond to therapy or in whom the ductus reopened, a second course was sometimes given. Population analysis of the 185 plasma concentrations obtained was conducted using NONMEM and pharmacokinetic and demographic differences between responders and nonresponders were compared. RESULTS: The concentration-time course of indometacin was best described by a one-compartment model. The final population parameter estimates of clearance (CL) and volume of distribution (V) (standardized to the median weight of 1.17 kg) were 0.00711 l h(-1) and 0.266 l, respectively. CL increased from birth by approximately 3.38% per day and V by approximately 1.47% per day. Concomitant digoxin therapy resulted in a 30% decrease in V. Interindividual variability in CL and V was 41% and 21%, respectively. Interoccasion variability for CL was 43%. Residual variability corresponded to a standard deviation of 0.148 mg l(-1). Closure occurred in 75% of infants with a plasma concentration > or = 0.4 mg l(-1) 24 h after the last dose. CONCLUSIONS: Dosing regimens for indometacin should take into account the weight and postnatal age of the infant and any concomitant digoxin therapy. The population estimates can be used to determine typical values of CL and V allowing the prediction of individualized doses of indometacin that should increase the probability of achieving a 24 h plasma concentration > or = 0.4 mg l(-1). Although the pharmacokinetic estimates will be affected by both interindividual and within-individual variation, it is anticipated that this approach will decrease the variability of exposure and optimize treatment outcome.     

A population pharmacokinetic approach to ceftazidime use in burn patients: influence of glomerular filtration, gender and mechanical ventilation

AIMS: The aim of this study was to evaluate the disposition of ceftazidime in burn patients using a population pharmacokinetic approach, and to identify the clinical and biological parameters influencing its pharmacokinetics. METHODS: The development of the pharmacokinetic model was based on 237 serum ceftazidime concentrations from 50 burn patients. The determination of the pharmacokinetic parameters and the selection of covariates were performed using a nonlinear mixed-effect modelling method. RESULTS: A two-compartment model with first order elimination incorporating a proportional error model best fitted the data. Ceftazidime clearance (CL, l h(-1)) was significantly correlated with creatinine clearance (CL(CR)), and the distribution volume of the peripheral compartment (V2, l) was correlated with gender, mechanical ventilation and the CL(CR). The final model was defined by the following equations: Ceftazidime clearance was 6.1 and 5.7 l h(-1) for mechanically ventilated males and females, respectively, and 7.2 and 6.6 l h(-1) for nonventilated patients. The total volume of distribution was 31.6 and 49.4 l for mechanically ventilated males and females, respectively, and 22.8 and 28.1 l h (-1)for nonventilated patients. CONCLUSIONS: We have shown that gender, mechanical ventilation and CL(CR) significantly influence the disposition of ceftazidime in burn patients. Interindividual variability in the pharmacokinetics of ceftazidime was significant and emphasizes the need for therapeutic monitoring.     

Population pharmacokinetics of darbepoetin alfa in haemodialysis and peritoneal dialysis patients after intravenous administration

AIMS: To characterize the pharmacokinetics of darbepoetin alfa and covariate relationships in haemodialysis (HD) and peritoneal dialysis (PD) patients. METHODS: Data were collected from 131 (63 HD and 68 PD) patients who received darbepoetin alfa intravenously. A total of 917 serum concentrations were available. The data were analysed by nonlinear mixed effect modelling using NONMEM with a model including endogenous erythropoietin production. In addition, the final model was evaluated using bootstrap resampling. RESULTS: The selected basic model was a two-compartment model with a combination of additive and the constant coefficient of variation error models. The significant covariates were weight (WT) for clearance (CL) and the volume of central compartment (V(1)), and the dialysis technique (DIA) for V(1). The typical values of CL and V(1) were 0.0807 l h(-1) and 2.51 l, respectively. V(1) in PD patients was 17% higher than in HD patients. With the introduction of WT in CL and WT and DIA in V(1), interindividual variability decreased from 27.1% to 20.6% in CL and from 29.1% to 21.8% in V(1). The mean parameter estimates from the bootstrap datasets were similar to those from the original dataset. Evaluation by bootstrapping showed that the final model was stable. CONCLUSIONS: The results of the present analysis suggest no dosage regimen change is warranted for darbepoetin alfa in HD and PD patients over the range of distribution of covariates included in this study.     

Pharmacokinetics and effects of propofol 6% for short-term sedation in paediatric patients following cardiac surgery

AIMS: This paper describes the pharmacokinetics and effects of propofol in short-term sedated paediatric patients. METHODS: Six mechanically ventilated children aged 1-5 years received a 6 h continuous infusion of propofol 6% at the rate of 2 or 3 mg kg-1 h-1 for sedation following cardiac surgery. A total of seven arterial blood samples was collected at various time points during and after the infusion in each patient. Pharmacokinetic modelling was performed using NONMEM. Effects were assessed on the basis of the Ramsay sedation score as well as a subjective sedation scale. RESULTS: The data were best described by a two-compartment pharmacokinetic model. In the model, body weight was a significant covariate for clearance. Pharmacokinetic parameters in the weight-proportional model were clearance (CL) = 35 ml kg-1 min-1, volume of central compartment (V1) = 12 l, intercompartmental clearance (Q) = 0.35 l min-1 and volume of peripheral compartment (V2) = 24 l. The interindividual variabilities for these parameters were 8%, < 1%, 11% and 35%, respectively. Compared with the population pharmacokinetics in adults following cardiac surgery and when normalized for body weight, statistically significant differences were observed the parameters CL and V1 (35 vs 29 ml kg-1 min-1 and 0.78 vs 0.26 l kg-1P < 0.05), whereas the values for Q and V2 were similar (23 vs 18 ml kg-1 min-1 and 1.6 vs 1.8 l kg-1, P > 0.05). In children, the percentage of adequately sedated patients was similar compared with adults (50% vs 67%) despite considerably higher propofol concentrations (1.3 +/- 0.10 vs 0.51 +/- 0.035 mg l-1, mean +/- s.e. mean), suggesting a lower pharmacodynamic sensitivity to propofol in children. CONCLUSIONS: In children aged 1-5 years, a pharmacokinetic model for propofol was described using sparse data. In contrast to adults, body weight was a significant covariate for clearance in children. The model may serve as a useful basis to study the role of covariates in the pharmacokinetics and pharmacodynamics of propofol in paediatric patients of different ages.     

Population pharmacokinetics of digoxin in Korean patients

AIM: Digoxin possesses a narrow therapeutic index and shows a large inter-patient pharmacokinetic variability. The purpose of this study was to develop a population model for the pharmacokinetics of digoxin in Korean patients. METHODS: Plasma concentrations of digoxin after multiple administration at varying dosing schedules in Korean patients were used for population modeling. Data analysis was performed with the P-Pharm software. The data were best fitted by a one-compartment model. The effect of demographic and clinical factors like sex, age, weight, disease state, and renal function on the pharmacokinetic parameters of digoxin was investigated. RESULTS: The study indicated that the clearance of digoxin was influenced by creatinine clearance, while body weight and creatinine clearance were the covariates for its volume of distribution. The population mean estimates for CL and V were 4.4 l/h and 535 l, respectively. Absorption rate constant was lower in females and in the presence of concomitant drug treatment. CONCLUSION: A population pharmacokinetic model for the digoxin pharmacokinetics in a section of Korean patients was developed. The relationships between the pharmacokinetic parameters and the demographic data and the patient-specific covariates were established.     

Teicoplanin population pharmacokinetic analysis in hospitalized patients

The goal of this study was to build a population pharmacokinetic (PK) model to characterize the population PK parameters in our hospitalized patients. Teicoplanin serum concentrations from clinical routine were used. Antibiotic dose history and blood collection times were recorded and analyzed with NONMEM-V. Demographic and biologic data creatinine clearance (CLcr), weight (WT), and albumin (Alb) were tested for inclusion as covariates in the basic model. Intraindividual and residual variability were modeled. One hundred seven sparse samples (mainly trough levels), from 79 patients, were included. A 2-compartment PK model characterized by clearance (CL), central compartment volume of distribution (Vc), intercompartment clearance, and steady-state volume of distribution (VSS) with first-order elimination adequately described the data. CLcr and WT significantly influenced teicoplanin CL (CL = 0.57[0.15]*(1+0.0048[0.39]*(CLcr - averageCLcr)*WT) L/h). VSS was not affected by any covariate (VSS = 50.2[0.13]L). A negative trend between Alb and individual VSS estimates was observed without statistical significance. In a new data set, bias and precision resulted in mean values of -3.24% and 9.42%, respectively. In conclusion, CLcr and WT are significant covariates on teicoplanin CL. Results from predictive accuracy and precision show the usefulness of this model for implementation in a therapeutic drug monitoring program in the near future.     

Population pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia: results of a phase III study

AIMS: This study was designed to investigate the biochemical and physiological covariates or comedications that affect the pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia (CP CML). METHODS: Pharmacokinetic data were analyzed in 371 patients receiving 400 mg imatinib once daily during a phase III trial of imatinib vs interferon-alfa plus cytarabine for the treatment of newly diagnosed CP CML. Covariates included age, weight, sex, ethnicity, haemoglobin (Hb) concentration, white blood cell (WBC) count, liver function, and creatinine concentration. Blood samples for imatinib analysis were taken on treatment days 1 and 29. Nonlinear mixed effects modelling was used for the population pharmacokinetic analysis. RESULTS: Population mean estimates (95% confidence interval) at day 1 for apparent clearance (CL) and apparent volume of distribution (V) of imatinib were 14 (13-15) l h(-1) and 252 (237-267) l, respectively. Modelling suggested that CL decreased by 4 (3-5) l h(-1) from day 1 to day 29, whereas V remained unchanged. Interindividual variability in CL and V was 32% and 31%, respectively. Weight, Hb, and WBC count demonstrated small effects on CL and V. Doubling body weight or Hb or halving the WBC count was associated with a 12%, 86% and 8% increase in CL, respectively, and a 32%, 60% and 5% increase in V, respectively. Comedications showed no clear effects on imatinib CL. CONCLUSIONS: Population covariates and coadministered drugs minimally affected imatinib pharmacokinetics in newly diagnosed CP CML patients.     

Population pharmacokinetic model of fluvoxamine in rats: utility for application in animal behavioral studies.

The limitations of blood sampling in pharmacokinetic (PK)/pharmacodynamic (PD) studies in behavioral animal models could in part be overcome by a mixed effects modeling approach. This analysis characterizes and evaluates the population PK of fluvoxamine in rat plasma using nonlinear mixed effects modeling. The model is assessed for its utility in animal behavioral PK/PD studies. In six studies with a different experimental setup, study site and/or sampling design, rats received an intravenous infusion of 1, 3.7 or 7.3mg/kg fluvoxamine. A population three-compartment PK model adequately described the fluvoxamine plasma concentrations. Body weight was included as a covariate and mean population PK parameters for CL, V(1), V(2), Q(2), V(3) and Q(3) were 25.1 ml/min, 256 ml, 721 ml, 30.3 ml/min, 136 ml and 1.0 ml/min, respectively. Inter-individual variability was identified on CL (39.5%), V(1) (43.5%), V(2) (50.1%) and Q(2) (25.7%). A predictive check and bootstrap analysis confirmed the predictive ability, model stability and precision of the parameter estimates. Body weight was identified as a significant covariate of the inter-compartmental clearance Q(2). The pharmacokinetics was independent of factors such as dose, surgery (for instrumentation) and study site. The utility of the model in animal behavioral studies was demonstrated in a PK/PD analysis of the effects on REM sleep in which a sparse PK sampling design was used. By using the pertinent information from the population PK model, individual PK profiles and the PK/PD correlation could be adequately described.     

Population pharmacokinetics of nevirapine in an unselected cohort of HIV-1-infected individuals

AIMS: To study the population pharmacokinetics of nevirapine and to identify relationships between patient characteristics and pharmacokinetics in an unselected population of patients attending our outpatient clinic. METHODS: Ambulatory HIV-1-infected patients from the outpatient clinic of the Slotervaart Hospital who were being treated with a nevirapine-containing regimen were included. During each visit, blood samples were collected for the determination of nevirapine plasma concentrations and clinical chemistry parameters. Variables that were collected at baseline were serology for hepatitis B (HBV) and C (HCV) viruses, liver enzymes, and total bilirubin (TBR). In addition, information about concomitant use of St John's wort and patient demographics were included. The pharmacokinetics of nevirapine were described by first-order absorption and elimination using nonlinear mixed effect modelling (NONMEM V1.1). Population pharmacokinetic parameters (apparent clearance (CL/F), volume of distribution (V/F), absorption rate constant (k a)) were estimated, as were interindividual, interoccasion, and residual variability in the pharmacokinetics. The influence of patient characteristics on the pharmacokinetics of nevirapine was determined. RESULTS: From 173 outpatients a total number of 757 nevirapine plasma concentrations at a single random time point and full pharmacokinetic curves for 13 patients were available resulting in a database of 1329 nevirapine plasma concentrations. Mean CL/F, V/F, and k a were 3.27 l h-1, 106 l, and 01.66 h-1, respectively. CL/F of nevirapine was correlated with weight, chronic HCV infection, and baseline aspartate aminotransferase (ASAT). Chronic HCV and baseline ASAT> 1.5 x upper limit of normal (ULN) decreased CL/F by 27.4% and 13.2%, respectively, whereas an increase in body weight of 10 kg increased CL/F by 0.14 l h-1. A trend towards a lower CL/F in patients of the Negroid race was observed. No significant covariates were found for V/F. CONCLUSIONS: The pharmacokinetics of nevirapine were adequately described by our population pharmacokinetic model. Weight, chronic HCV infection, and baseline ASAT were found to be significant covariates for CL/F of nevirapine. The model incorporating these significant covariates may be an important aid in further optimizing nevirapine-containing therapy.     

他克莫司在中国肾移植患者中的群体药物动力学研究

本研究旨在考察口服他克莫司(tacrolimus)在中国肾移植患者中的群体药物动力学特征并探讨群体药物动力学参数和相关因素间的关系。研究中回顾性搜集了58例肾移植患者的802份他克莫司稳态全血样本资料。患者随机分为模型建立组(41例)和模型验证组(17例)。用非线性混合效应模型(NONMEM)程序中的一级评估法(first-order estimation，FO)对模型建立组的数据进行分析。计算清除率(CL/F)、表观分布容积(V/F)的群体典型值，定量评价人口统计学指标、生化指标和合并用药等固定效应因素对药物动力学参数的影响。单室一级吸收和消除模型能够较好地拟合数据。最终模型包含了移植术后时间(POD)、红细胞压积(HCT)、谷草转氨酶(AST)、合并使用佩尔地平(NICA)和地尔硫(DIL)等对CL/F的影响。用模型验证组数据进行验证的结果表明观测值和模型预测值之间没有明显的偏倚，模型的稳定性和准确度较好。CL/F和V/F的群体典型值分别为21.7 L·h^-1和241 L；相应的个体间变异分别为41.6%和49.7%。观测值与预测值之间的残差SD为2.19 μg·L^-1。本文建立的模型可以为临床他克莫司剂量选择提供一定参考。     

Population pharmacokinetics of high-dose methotrexate in Chinese pediatric patients with medulloblastoma

Methotrexate (MTX) pharmacokinetics has substantial inter-individual variability and toxicity. In children with medulloblastoma treated with high-dose methotrexate (HD-MTX), the pharmacokinetic properties of methotrexate have not been established. A total of 660 serum samples from 105 pediatric patients with medulloblastoma were included in a population pharmacokinetic (PPK) analysis of methotrexate by using the nonlinear mixed-effects modeling method. The basic one-compartment population pharmacokinetic model was established by NONMEM software and the first-order conditional estimation (FOCE) method, and the final covariate model was obtained by the stepwise regression method. Weight (WT), creatinine clearance (CrCL), and whether the treatment was combined with dexamethasone (DEX) were covariates that had significant effects on the clearance rate (CL) of the model. The pharmacokinetic equation of CL in the final covariate model was as follows: CL_i = 9.23× (1 + 0.0005× (θ_CrCL-105.78)) × (1 + 0.0017× (θ_WT-16)) × e^ηcl,i (L/h), IF (θ_DEX) CL_i = 1.19× CL_i (L/h). The estimation accuracy of all pharmacokinetic parameters were acceptable (relative standard error < 14.74%). The goodness-of-fit diagram and bootstrap tests indicated that the final PPK model was stable with acceptable predictive ability. The PPK model may be useful for determining personalized medication levels in pediatric medulloblastoma patients undergoing HD-MTX therapy.     

Memantine pharmacotherapy: a naturalistic study using a population pharmacokinetic approach

BACKGROUND AND OBJECTIVE: Memantine plasma concentrations show considerable interindividual variability. High memantine plasma concentrations are associated with the occurrence of neuropsychiatric adverse effects such as confusion. The objective of the present study was, therefore, to investigate the reasons for the observed variability of the pharmacokinetics of memantine in a representative patient population and to explore patient covariates on drug disposition. SUBJECTS: Fifty-six ambulatory Western European patients aged 50-91 years. METHODS: This prospective study used a full population pharmacokinetic sampling design. After at least 11 days of continuous memantine intake, the patients provided pharmacokinetic profiles, with six measurements each over a 12-hour period, with a total of 335 serum memantine concentrations. Covariates considered for inclusion in the models were: subject demographic factors (age, total bodyweight, gender), laboratory tests (urinary pH), total daily dose of memantine, memantine formulation type, comedication eliminated via tubular secretion and smoking history. The model development was conducted in three sequential steps. First, an adequate basic structural model was chosen (e.g. a one-, two- or three-compartment pharmacokinetic model). The data were analysed to estimate population pharmacokinetic parameters with the nonlinear mixed-effects model computer program NONMEM. Second, the effects of covariates were investigated on post hoc estimates using multivariate statistics. Third, the covariates with significant effects in the second step were used to build a final covariate pharmacokinetic model, again using NONMEM. RESULTS: A two-compartment model with first-order absorption satisfactorily described memantine pharmacokinetics. In the final regression model, total bodyweight, memantine formulation type (solution vs tablets) and concomitant medication eliminated via tubular secretion were all important determinants of the apparent clearance (CL/F). The final regression model was: CL/F (L/h) = (1.92 + 0.048 x BW (kg)) x 0.530(FRM) x 0.769(CMD) where FRM = 1 for patients receiving memantine solution, otherwise FRM = 0; CMD = 1 for patients receiving a comedication eliminated via tubular secretion, otherwise CMD = 0; and BW is bodyweight. Compared with the basic model, the final population pharmacokinetic model explained 61% of the interindividual variance of the apparent clearance. CONCLUSIONS: The population pharmacokinetic model that was developed identified a set of sources of variability in the apparent clearance of memantine, which can be used as a reference in order to optimise memantine therapy in Western European patients.