Population pharmacokinetics of levosimendan in patients with congestive heart failure

AIMS: The aim of this study was to characterize the population pharmacokinetics of levosimendan in patients with heart failure (NYHA grades III and IV) and its relationship to demographic factors, disease severity and concomitant use of digoxin and beta-blocking agents. METHODS: Data from two efficacy studies with levosimendan administered by intravenous infusion were combined (190 patients in total). The data were analysed using a nonlinear mixed-effects modelling approach as implemented in the NONMEM program. The model development was done in three sequential steps. First the best structural model was determined (e.g. a one-, two- or three-compartment pharmacokinetic model). This was followed by the identification and incorporation of important covariates into the model. Lastly the stochastic part of the model was refined. RESULTS: A two-compartment model best described levosimendan pharmacokinetics. Clearance and the central volume of distribution were found to increase linearly with bodyweight. No other covariates, including concomitant use of digoxin and beta-blocking agents, influenced the pharmacokinetics. In the final model, a 76-kg patient was estimated to have a clearance +/- s.e. of 13.3 +/- 0.4 l h-1 and a central volume of distribution of 16.8 +/- 0.79 l. The interindividual variability was estimated to be 39% and 60% for clearance and central volume of distribution, respectively. Weight changed clearance by 1.5% [95% confidence interval (CI) 0.9%, 2.1%] and the central volume of distribution by 0.9% (95% CI 0.5%, 1.3%) per kg. CONCLUSIONS: The population pharmacokinetics parameters of levosimendan in this patient group were comparable to those obtained by traditional methods in healthy volunteers and patients with mild heart failure. Bodyweight influenced the clearance and the central volume of distribution, which in practice is accounted for by weight adjusting doses. None of the other covariates, including digoxin and beta-blocking agents, significantly influenced the pharmacokinetics of levosimendan.     

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 pharmacokinetics of ceftazidime in burn patients

AIM: The aim of this study was to characterize, via a population pharmacokinetic approach, the pharmacokinetics of ceftazidime in burn patients who were not in the acute post-injury phase. METHODS: The development of the pharmacokinetic model was based on data from therapeutic drug monitoring (41 patients, 94 samples). The estimation of population pharmacokinetic parameters and the selection of covariates (age, gender, body weight, size of burn and creatinine plasma concentration) that could affect the pharmacokinetics were performed with a nonlinear mixed effect modelling method. RESULTS: No relationship between covariates and the pharmacokinetic parameters was established with the exception of an inverse-linear relationship between creatinine plasma concentration and ceftazidime total clearance. The total clearance of ceftazidime was 2.72 l h-1[coefficient variation (CV) = 56.3%] and the distribution volume of the central compartment was 0.28 l kg-1 (CV = 13.2%) The transfer rate constants (k12, k 21) between the central and peripheral compartments were 0.06718 h-1 (CV = 87.2%) and 0.001823 h-1 (CV = 82.7%), respectively. From these parameters, the total ceftazidime volume of distribution (10.64 l kg-1) was calculated. CONCLUSION: The population parameters were different from those obtained in a previous study performed in fewer patients and in the early period after burn injury. In our study, the lower ceftazidime clearance could be explained by the relative decrease in ceftazidime elimination in relation to the burn area, and the higher ceftazidime volume of distribution in the presence of interstitial oedema, which could act as a reservoir from which ceftazidime returns slowly to the circulation.     

Population-based investigation of relative clearance of digoxin in Japanese neonates and infants by multiple-trough screen analysis

OBJECTIVE: The steady-state concentrations of digoxin at trough levels were studied to establish the role of patient characteristics in estimating doses for digoxin using routine therapeutic drug monitoring data. METHOD: The data (n = 448) showing steady state after repetitive oral administration in 172 hospitalized neonates and infants were analyzed using Nonlinear Mixed Effect Model (NONMEM), a computer program designed to analyze pharmacokinetics in study populations by allowing pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a simple steady-state pharmacokinetic model. The effects of a variety of developmental and demographic factors on the clearance of digoxin were investigated. RESULTS: Estimates generated using NONMEM indicated that clearance of digoxin (l.h-1) was influenced by the demographic variables of age, total body weight, serum creatinine, the coadministration of spironolactone, and the presence or absence of congestive heart failure. The interindividual variability in digoxin clearance was modeled with proportional errors with an estimated coefficient of variation of 32.1%, and the residual variability was 28.9%. In the validation set of 66 patients, the performance (bias, precision) of the final population model was good (mean prediction error -0.04 ng.ml-1; mean absolute prediction error 0.20 ng.ml-1).     

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 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-based pharmacokinetics of the soluble TNFr etanercept: a clinical study in 43 patients with ankylosing spondylitis compared with post hoc data from patients with rheumatoid arthritis

OBJECTIVE: The purpose of this study was to evaluate the pharmacokinetics of etanercept in patients with ankylosing spondylitis (AS) in a phase 3 study. METHODS: Serum etanercept concentrations were analyzed from samples obtained at weeks 4 and 12 from 43 patients with AS (median age: 45 years; median body weight: 75 kg; white/non-white: 40/3; male/female: 34/9) receiving 25 mg subcutaneously twice weekly for 12 weeks. A population pharmacokinetics analysis using NONMEM was conducted to estimate individual etanercept pharmacokinetic parameters. Initially, appropriate base and covariate population pharmacokinetic models were built based on data from 10 prior clinical studies of etanercept administered subcutaneously or intravenously to healthy subjects (n = 53) and to patients with rheumatoid arthritis (RA) (n = 212). The influence of demographic characteristics on the pharmacokinetics of etanercept was thoroughly evaluated. The stability of the final model was evaluated using both internal (bootstrapping) and external (data splitting) validation approaches. Finally, the selected final population covariate model was used to estimate the Bayesian pharmacokinetic parameters for the patients with AS. RESULTS: The data from the 10 prior clinical studies were optimally fitted to a 2-compartment linear population covariate model. Both age (< 17 years) and body weight (< 60 kg) were found to be important covariates on clearance. Both bootstrapping and data splitting validated the population model. The mean Bayesian-predicted etanercept clearance and steady-state trough concentration were 0.072 l/h and 2,004 ng/ml, respectively. The pharmacokinetic parameters of etanercept in the patients with AS were similar to those observed in the patients with RA. CONCLUSIONS: The pharmacokinetics of etanercept in patients with AS were similar to those in patients with RA. The AS disease state does not appear to alter the disposition of etanercept.     

Population kinetics of tobramycin in neonates

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

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.     

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.     

Influence of clinical diagnosis in the population pharmacokinetics of amikacin in intensive care unit patients

The aim of the present study was to analyse the pharmacokinetic behaviour of amikacin in intensive care unit (ICU) patients using a mixed-effect model and sparse data collected during routine clinical care. The patient population comprised 158 medical ICU patients divided into two groups: one for computing the population model (n = 120) and the other for validation (n = 38). A 1-compartment model was used and the following covariates were tested for their influence on clearance (CL) and volume of distribution (Vd): age, gender, weight, parenteral nutrition, creatinine clearance, duration of therapy and clinical diagnosis. The nonlinear mixed-effect model (NONMEM) was used to assess the population pharmacokinetic model of amikacin in this patient population. In this study, the final population model accounting for amikacin pharmacokinetics in ICU patients was: CL = 0.93 CL(CR) (1 + 0.22 Trauma), Vd = 0.39 TBW (1 + 0.24 Sepsis), where CL(CR) and TBW corresponded to the patients' creatinine clearance and total bodyweight, respectively. The 'Trauma' and 'Sepsis' variables referred to the clinical diagnosis of the patients. This model was subsequently used to predict amikacin serum levels obtained in the validation population by a priori and Bayesian methods. The predictive performance was adequate for clinical purposes, pointing to the feasibility of our population model to provide reference values for a priori prediction as well as the Bayesian approach for individualisation of amikacin therapy in ICU patients.     

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

Routine clinical pharmacokinetic data collected from patients receiving digoxin have been analysed to evaluate the role of patient characteristics for estimating dosing regimens. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of digoxin was described using a one-compartment steady-state model. The effect of a variety of developmental and demographic factors on clearance was investigated. NONMEM estimates indicate that digoxin clearance was influenced by the demographic variables of age, total body weight, serum creatinine and sex. The interindividual variability in digoxin clearance was modelled with additive error with an estimated standard deviation of 46.15 L day-1 and the intraindividual variability, or residual error was 0.209 ng mL-1. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady-state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.     

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 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 Risperidone and 9-Hydroxyrisperidone in Patients with Acute Episodes Associated with Bipolar I Disorder

A population model was developed with the aim to simultaneously describe risperidone and 9-hydroxyrisperidone pharmacokinetics; to obtain estimates for pharmacokinetic parameters and associated inter- and intra-individual variability of risperidone and 9-hydroxyrisperidone; and to evaluate the influence of patient demographic characteristics and other factors on risperidone, 9-hydroxyrisperidone, and active moiety pharmacokinetics. Data were obtained from 407 patients enrolled in four Phase 1 (serial blood sampling) and three Phase 3 trials (sparse sampling), representing dosage regimens ranging from 4 mg single dose to flexible 1–6 mg once daily. A pharmacokinetic model with two-compartment submodels for risperidone and 9-hydroxyrisperidone disposition and a sequential zero- and first-order absorption pathway was selected based on prior knowledge. A mixture model was incorporated due to CYP2D6 polymorphism of risperidone conversion to 9-hydroxyrisperidone. Patient characteristics tested as potential covariates were: age, sex, race, body weight, lean body mass, body mass index, creatinine clearance, liver function laboratory parameters, study, and carbamazepine comedication. The quasi-clearance of active moiety (the sum of risperidone and 9-hydroxyrisperidone) was simulated and linear regression performed to identify significant covariates. The selected pharmacokinetic model described the plasma concentration-time profiles for risperidone and 9-hydroxyrisperidone quite well and was able to determine each patient’s phenotype. Covariates significantly affecting the pharmacokinetics were carbamazepine comedication, and study because the proportion of patients assigned to the intermediate metabolizer status decreased from single to multiple dosing while the proportion assigned to extensive metabolizer status increased. Covariates with limited and clinically irrelevant effects on active moiety concentrations were patient phenotype, race, and total protein. Carbamazepine also decreased active moiety concentrations.     

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 pediatric patients

Digoxin pharmacokinetics were studied in a pediatric population with an age range of 6 days to 1 year using the population pharmacokinetic approach. Digoxin data were analyzed by mixed-effects modeling according to a one-compartment steady-state pharmacokinetic model using NONMEM software. The final model selected for the population prediction of digoxin clearance in pediatric patients was as follows: [equation: see text] Individual empirical Bayesian estimates were generated on the basis of the population estimates and were used to correlate the optimum dose of digoxin and patient age according to the following equation: [equation: see text] This equation and its derived nomogram may be used for the initial dosing of digoxin in children aged between 0 and 1 year. The use of this nomogram in routine monitoring requires further pharmacokinetic and clinical validation.     

Population pharmacokinetics of digoxin in Japanese patients: a 2-compartment pharmacokinetic model.

OBJECTIVE: To clarify the observed variability of digoxin disposition by performing a population pharmacokinetic analysis in a Japanese population. DESIGN: Retrospective analysis of clinical pharmacokinetic data. PATIENTS AND PARTICIPANTS: Data were obtained from 106 patients with heart failure and atrial fibrillation (43 males and 63 females). METHODS: Digoxin concentrations in serum were measured by fluorescence polarisation immunoassay. Population pharmacokinetic analysis was performed using a 2-compartment open pharmacokinetic model with the computer program NONMEM. RESULTS: 246 serum concentrations were obtained. Final pharmacokinetic parameters were: CL (L/h) = (0.036 x TBW + 0.112 x CL(CR)) x 0.77SPI x 0.784CCB, V1 = 1.83 L/kg, V2 = 22.6 L/kg and Q = 0.629 L/h/kg, where CL is total body clearance, V1 and V2 are the apparent volumes of distribution in the central and peripheral compartments, Q is intercompartmental clearance, TBW is total bodyweight (in kg), CL(CR) is creatinine clearance (in ml/min), SPI = 1 for concomitant administration of spironolactone (and zero otherwise) and CCB = 1 for concomitant administration of calcium antagonists (and zero otherwise). Concomitant administration of digoxin and spironolactone resulted in a 23% decrease in digoxin clearance. Concomitant administration of digoxin and calcium antagonists (diltiazem, nicardipine, nifedipine or verapamil) resulted in a 21.6% decrease in digoxin clearance. CONCLUSIONS: The estimated population parameter values may assist clinicians in the individualisation of digoxin dosage regimens.     

Population pharmacokinetics and exploratory pharmacodynamics of ifosfamide and metabolites after a 72-h continuous infusion in patients with soft tissue sarcoma

OBJECTIVE: The population pharmacokinetics and pharmacodynamics of the cytostatic agent ifosfamide and its main metabolites 2- and 3-dechloroethylifosfamide and 4-hydroxyifosfamide were assessed in patients with soft tissue sarcoma. METHODS: Twenty patients received 9 or 12 g/m2 ifosfamide administered as a 72-h continuous intravenous infusion. The population pharmacokinetic model was built in a sequential manner, starting with a covariate-free model and progressing to a covariate model with the aid of generalised additive modelling. RESULTS: The addition of the covariates weight, body surface area, albumin, serum creatinine, serum urea, alkaline phosphatase and lactate dehydrogenase improved the prediction errors of the model. Typical pretreatment (mean +/- SEM) initial clearance of ifosfamide was 3.03 +/- 0.18 l/h with a volume of distribution of 44.0 +/- 1.8 l. Autoinduction, dependent on ifosfamide levels, was characterised by an induction half-life of 11.5 +/- 1.0 h with 50% maximum induction at 33.0 +/- 3.6 microM ifosfamide. Significant pharmacokinetic-pharmacodynamic relationships (P = 0.019) were observed between the exposure to 2- and 3-dechloroethylifosfamide and orientational disorder, a neurotoxic side-effect. No pharmacokinetic-pharmacodynamic relationships between exposure to 4-hydroxyifosfamide and haematological toxicities could be observed in this population.