A simultaneous oral/intravenous population pharmacokinetic model for vinorelbine

OBJECTIVES: To develop a population pharmacokinetic (PK) model for simultaneous analysis of oral and intravenous data, to compare the variability between the two routes of administration of vinorelbine, to search for the main patient characteristics that explain this variability, and to estimate the mean population bioavailability of oral vinorelbine. PATIENTS AND METHODS: A PK model was developed from 175 phase I/II patients (419 courses) treated by intravenous (20-45 mg/m2) and/or oral (60-100 mg/m2) vinorelbine given as monotherapy. Oral and intravenous PK data were simultaneously fitted using the NONMEM program, allowing the estimation of oral PK parameters such as the bioavailability factor in patients who received only the oral formulation. Covariates included demographic characteristics, biological markers, hematological parameters, liver metastases, early vomiting, and food intake. The population covariate model was developed from rich sampling data ( n=187 phase I courses) and then assessed from sparse sampling data ( n=232 phase II courses). RESULTS: A three-compartment model best described the combined oral/intravenous blood concentration-time data. The mean absolute bioavailability was 36%, with moderate interindividual (CV=20%) and intraindividual (CV=19%) variability. Bayesian clearance was accurately estimated in 180 of 187 patients. The clearance of oral and intravenous vinorelbine showed comparable variability at usual doses (25-30 mg/m2 intravenous; CV=26%; 60-80 mg/m2 oral, CV=33%) and was moderately increased when including maximum tolerated doses (20-45 mg/m2 intravenous, CV=27%; 60-100 mg/m2 oral, CV=36%). Several relevant covariate relationships influencing the total body clearance of vinorelbine were independent of the route of administration: body surface area (proportional relationship), platelet count above 400 x 10(9)/l (negative correlation), creatinine clearance (positive correlation), and elevated transaminases (negative correlation). Food intake induced a lag time in the absorption of oral vinorelbine. A weak and poorly estimated relationship was observed between elevated alkaline phosphatase levels and bioavailability, although hepatic markers such as GGT, LDH, total protein, and liver metastases and age had no effect on vinorelbine pharmacokinetics. CONCLUSIONS: By means of the simultaneous analysis of oral and intravenous data the bioavailability (F=36%) and its associated variability were estimated. At usual doses similar levels of variability were observed between oral and intravenous routes. As a result of the identification of covariates from phase I data and their confirmation from phase II data further explorations based on limited sampling strategies are now possible. The use of a simultaneous oral/intravenous model allows a better characterization of the PK profile of vinorelbine after administration by either vascular or oral route     

A limited-sampling strategy to estimate individual pharmacokinetic parameters of vinorelbine in elderly patients with advanced metastatic cancer

The aim of this study was to characterize the population pharmacokinetic of vinorelbine in elderly patients and to propose a limited-sampling strategy to estimate individual pharmacokinetic parameters. Vinorelbine was administered by a 10-min continuous infusion at a dose of 20-30 mg/m2. The population parameters were computed, using a three-compartment model, from an initial group of 27 patients. Twelve additional courses were used for model validation and evaluation of eight different limited-sampling strategies. The inter-individual variability of CL was explained by a linear dependency with age. The population average parameters and the interindividual variabilities (CV%) were: CL=47.1 l/h (31.7%), V=16.6 l (64%), k21=0.776 h-1 (20%), k31=0.0346 h-1 (15.2%), alpha=0.431 h-1 (6.84%) and beta=0.0167 h-1 (25%). Bayesian estimation with three measured levels (end of infusion, and 6 and 48 h) can be selected, because it allows adequate estimation of CL, elimination half-life and vinorelbine concentrations with a non-significant bias. Moreover, the choice of these three sampling times presents practicality advantages for the patient's comfort. Vinorelbine clearance decreasing with age and AUC being a good predictor of several toxicity end points during vinorelbine treatment, the limited-sampling strategy developed in this paper may be clinically relevant.     

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.     

Population pharmacokinetic modeling and optimal sampling strategy for Bayesian estimation of amikacin exposure in critically ill septic patients

Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, the present study aimed to develop a population PK model for amikacin (AMK) in severe sepsis and to subsequently propose an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters. Concentration-time profiles for AMK were obtained from 88 critically ill septic patients during the first 24 hours of antibiotic treatment. The population PK model was developed using a nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, and comedication. Optimal sampling times were selected based on a robust Bayesian design criterion. Taking into account clinical constraints, a two-point sampling approach was investigated. A two-compartment model with first-order elimination best fitted the AMK concentrations. Population PK estimates were 19.2 and 9.34 L for the central and peripheral volume of distribution and 4.31 and 2.21 L/h for the intercompartmental and total body clearance. Creatinine clearance estimated using the Cockcroft-Gault equation was retained in the final model. The two optimal sampling times were 1 hour and 6 hours after onset of the drug infusion. Predictive performance of individual Bayes estimates computed using the proposed optimal sampling strategy was reported: mean prediction errors were less than 5% and root mean square errors were less than 30%. The present study confirmed the significant influence of the creatinine clearance on the PK disposition of AMK during the first hours of treatment in critically ill septic patients. Based on the population estimates, an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters was developed, meeting the need of clinical practice.     

Sirolimus population pharmacokinetic/pharmacogenetic analysis and bayesian modelling in kidney transplant recipients

OBJECTIVES: The objectives of the present study were: (i) to analyse the population pharmacokinetics of sirolimus in renal transplant recipients co-administered mycophenolate mofetil, but no calcineurin inhibitor over the first 3 months post-transplantation and study the influence of different potential covariates, including genetic polymorphisms of cytochrome P450 (CYP) metabolic enzymes and active transporters, on pharmacokinetic parameters; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and exposure indices in this population. METHODS: Twenty-two adult renal transplant patients treated with sirolimus participated in this study. Ninety concentration-time profiles (938 sirolimus whole blood samples) were collected at days 7 and 14, and months 1 and 3 post-transplantation. The population pharmacokinetic study was conducted using the nonlinear mixed effects model software, NONMEM, and validated using both the bootstrap and the cross-validation approaches. Finally, a Bayesian estimator based on a limited sampling strategy was built using the post hoc option. RESULTS: A two-compartment open model with first-order elimination and Erlang's distribution (to describe the absorption phase) best fitted the data. The mean pharmacokinetic parameter estimates were 5.25 h(-1), 218L and 292L for the transfer rate constant, the apparent volume of the central and peripheral compartments, respectively. The CYP3A5*1/*3 polymorphism significantly influenced the apparent oral clearance: mean oral clearance = 14.1 L/h for CYP3A5 non expressers (CYP3A5*3/*3 genotype) versus 28.3 L/h for CYP3A5 expressers (CYP3A5*1/*3 and *1/*1 genotypes). The standard errors of all the parameter estimates were <15%. Maximum a posteriori Bayesian forecasting allowed accurate prediction of sirolimus area under the concentration-time curve from 0 to 24 hours using a combination of only three sampling times (0, 1 and 3 hours post-dose), with a non-significant bias of -2.1% (range -22.2% to +25.9%), and a good precision (root mean square error = 10.3%). This combination is also easy to implement in clinical practice. CONCLUSION: This study presents an accurate population pharmacokinetic model showing the significant influence of the CYP3A5*1/*3 polymorphism on sirolimus apparent oral clearance, and a Bayesian estimator accurately predicting sirolimus pharmacokinetics in patients co-administered mycophenolate mofetil, but no calcineurin inhibitor.     

A limited sampling strategy for estimating individual pharmacokinetic parameters of coagulation factor VIII in patients with hemophilia A

Therapeutic drug monitoring of factor VIII is well established in the treatment of patients with hemophilia attributable to important interindividual variability. The individual initial factor VIII dosage is usually calculated according to individual pharmacokinetic parameters obtained after a dose test administered before the surgery, using at least five-concentration data. The authors proposed a limited sampling strategy to estimate individual pharmacokinetic parameters from one- or two-concentration data in patients with hemophilia A before surgery. The mean population pharmacokinetic parameters and the interindividual variability (CV) were obtained from a group of 33 patients according to a two-compartment model using NONMEM. Eighteen additional patients were used to estimate the predictive performances of the population parameters and to evaluate the limited sampling strategies. Population parameters were clearance 2.6 mL/h per kilogram (CV 45.4%), initial volume of distribution 2.8 L (CV 21.1%). From two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion), the estimation of pharmacokinetic parameters was precise and not biased. Until now, in the hemophilic center of Lyon, the factor VIII dosage before surgery was based on the determination of the clearance, estimated from five- to nine-concentration data and on the target concentration (infusion rate = clearance x target). Ruffo et al proposed a limited sampling strategy (two-stage method) to estimate pharmacokinetic parameters from two concentration measurements drawn 3 and 9 hours after the dose. No information was given on the bias and precision of the estimation. This paper reports a one-stage method for a population pharmacokinetic study of factor VIII. The Bayesian estimation of individual pharmacokinetic parameters based on only two sampling times (0.5 and 6 hours or 0.5 and 8 hours after the end of infusion) is useful to define the best factor VIII dosage in hemophilic patients before surgery.     

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 (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.     

Utility of sparse concentration sampling for citalopram in elderly clinical trial subjects

The objective of this study was to evaluate whether the disposition of the selective serotonin reuptake inhibitor, citalopram, could be robustly captured using 1 to 2 concentration samples per subject in 106 patients participating in 2 clinical trials. Nonlinear mixed-effects modeling was used to evaluate the pharmacokinetic parameters describing citalopram's disposition. Both a prior established 2-compartment model and a de novo 1-compartment pharmacokinetic model were used. Covariates assessed were concomitant medications, race, sex, age (22-93 years), and weight. Covariates affecting disposition were assessed separately and then combined in a stepwise manner. Pharmacokinetic characteristics of citalopram were well captured using this sparse sampling design. Two covariates (age and weight) had a significant effect on the clearance and volume of distribution in both the 1- and 2-compartment pharmacokinetic models. Clearance decreased 0.23 L/h for every year of age and increased 0.14 L/h per kilogram body weight. It was concluded that hyper-sparse sampling designs are adequate to support population pharmacokinetic analysis in clinically treated populations. This is particularly valuable for populations such as the elderly, who are not typically available for pharmacokinetic studies.     

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.     

High dose methotrexate population pharmacokinetics and Bayesian estimation in patients with lymphoid malignancy

The purpose of present study was to develop a population pharmacokinetic model of high dose methotrexate (HD‐MTX) infusion in patients with lymphoid malignancy, to investigate the biological and clinical covariates related to the drug distribution and elimination. It is also the purpose to propose a limited sampling strategy (LSS) for the estimation of the time above the threshold (0.2 µmol·L^?1). A total 82 patients with lymphoid malignancy were involved in the study. A pharmacokinetic model was developed using nonlinear mixed‐effect model. The influence of demographic characteristics, biological factors, and concurrent administration were investigated. The final predictive performance was validated by bootstrap and cross‐validation. Bayesian estimation was evaluated. The pharmacokinetics of HD‐MTX was described by a two‐compartment model. The pharmacokinetic parameters and the inter‐individual variability were as follows: the clearance CL, 7.45 L·h^?1 (inter‐individual variability 50.6%), the volume of the central and peripheral compartment V₁, 25.9 L (22.5%), V₂, 9.23 L (97.8%), respectively, and the intercompartmental clearance Q, 0.333 L·h^?1 (70.4%). The influence of serum creatinine on CL and weight on V₁ was retained in the final model. The protocol involved one sampling time at 44 h after the start of the infusion, allowing one to predict the time at which the MTX concentration reached the expected threshold (0.2 µmol·L^?1). Serum creatinine and weight showed significant influence on methotrexate CL and V₁, respectively. Furthermore, a Bayesian estimation based on the covariates and 44 h sample was developed, allowing prediction of the individual methotrexate pharmacokinetic parameters and the time to 0.2 µmol·L^?1. Copyright © 2009 John Wiley & Sons, Ltd.     

Dose adjustment strategy for oral microemulsion formulation of cyclosporine: population pharmacokinetics-based analysis in kidney transplant patients

The present study aims to determine the population pharmacokinetic parameters of cyclosporine (CsA) after multiple oral administration of the microemulsion formulation, Neoral, in kidney transplant patients and to propose a limited sampling strategy to predict AUC(0-4h) using them and the Bayesian method. The AUC(0-4h) is a parameter that has recently been recommended as an index for the dose adjustment in therapeutic drug monitoring of CsA. Blood samples were obtained at the trough level and at hourly intervals up to 5 hours from 125 patients (78 male and 47 female) who were receiving Neoral twice daily, and whole-blood concentrations of CsA were measured. The population pharmacokinetic parameters were estimated using the NONMEM computer program and a linear two-compartment model with first-order absorption. The observed AUC0-4h and concentrations at different sampling times were compared with those computer-predicted by the Bayesian method, using the population pharmacokinetic parameters and 2 or 3 concentrations from those at 0 h (C(0)), 1 h (C(1)), and 2 h (C(2)) after administration. Typical values for the absorption rate constant (k(a)), elimination rate constant (k(el)), apparent volume of distribution for the central compartment (Vd/F), and oral clearance (CL/F) calculated by population pharmacokinetic analysis were 2.16 hours(-1), 0.547 hours(-1), 43.3 L, and 23.7 L/h, respectively. The CsA concentrations predicted using either the 2-point or 3-point sampling strategy exhibited an excellent correlation with the observed values (R(2) > 0.81), and accordingly, the predicted AUC(0-4h) values were in excellent agreement with those observed. The best predictability of AUC(0-4h) was found for the 3-point sampling strategy using C(0), C(1), and C(2), closely followed by a 2-point sampling strategy using C(1) and C(2). The present findings suggest that a simplified strategy based on population pharmacokinetics can accurately predict AUC(0-4h) from concentrations at 2 or 3 sampling time points, providing an excellent method for the daily dose adjustment of Neoral in routine clinical use for kidney transplant patients.     

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.     

A pharmacokinetic model for alpha interferon administered subcutaneously

AIMS: To model the pharmacokinetic profiles of alpha interferon (alphaIFN) after a single subcutaneous (s.c.) injection of 3 million units of alpha 2b interferon, to correlate the pharmacokinetic parameters with patient demographic covariates, and to develop a limiting sampling strategy for determining the alphaIFN plasma area under the curve of concentration vs time (AUC). METHODS: The plasma alphaIFN pharmacokinetics were determined in 27 patients with chronic hepatitis C virus infection after the first s.c. injection of the drug. Ten patients had normal renal function and 17 were chronic haemodialysis patients. Plasma samples were assayed by an Elisa method. Concentration-time data was analysed by a population approach using NONMEM. RESULTS: The pharmacokinetic model which better described the concentration vs time data was a one-compartment model with two processes of absorption: a zero-order followed by a first-order process. The mean clearance of dialysis patients represented 37% (with 95% confidence interval: 30% -44%) of the mean value of the patients with normal renal function. The volume of distribution was significantly correlated to the body surface area. Bayesian analysis using NONMEM allowed determination of the individual plasma AUC from three samples within the 24 h period post s.c. injection. CONCLUSIONS: The present pharmacokinetic model will allow one to obtain individual parameters such as, the area under the curve of concentration vs time from a limited-sampling strategy, and to perform pharmacokinetic-pharmacodynamic analysis of combined alphaIFN plasma concentrations and viraemic data.     

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 Sibrotuzumab, A Novel Therapeutic Monoclonal Antibody, in Cancer Patients

Population pharmacokinetics of sibrotuzumab, a humanized monoclonal antibody directed against fibroblast activation protein, were determined after multiple intravenous infusions of dosages ranging from 5 mg/m(2) to an absolute dose of 100 mg, in patients with advanced or metastatic carcinoma. In total, 1844 serum concentrations from 60 patients in three Phase I and II clinical studies were analyzed. The structural model incorporated two disposition compartments and two parallel elimination pathways from the central compartment, one linear and one nonlinear. Finally estimated pharmacokinetic parameters (%RSE) were: linear clearance CLL 22.1 ml/h (9.6), central distribution volume V1 4.13l (3.7), peripheral volume V2 3.19l (8.8), inter-compartmental clearance Q 37.6 ml/h (9.6); for the nonlinear clearance Vmax was 0.0338 mg/h (25) and Km 0.219 microg/ml (57). At serum concentrations between approximately 20 ng/ml and 7 microg/ml, the effect of the nonlinear clearance on pharmacokinetics was marked. Only at >7 microg/ml did CLL dominate overall clearance. Interindividual variability was 57% for CLL, 20% for V1 and V2, and 29% for Vmax and was larger than the inter-occasional variability of 13%. Of the many investigated patient covariates, only body weight was found to contribute to the population model. It significantly affected CLL, V1, V2 and Vmax resulting in marked differences in the model-predicted concentration-time profiles after multiple dosing in patients with low and high body weights. In conclusion, a robust population pharmacokinetic model was developed and evaluated for sibrotuzumab, which identified a possible need to consider body weight when designing dosage regimen for future clinical cancer trials.     

Population pharmacokinetics of mycophenolic acid in kidney transplant pediatric and adolescent patients

Current data on mycophenolate mofetil (MMF) suggest that there is a pharmacokinetic/pharmacodynamic relationship between the mycophenolic acid (MPA) area under the curve (AUC) during treatment and both the risk of acute rejection and the occurrence of side effects. The aim of this study was to characterize the population pharmacokinetics of MPA in kidney transplant patients between the ages of 2 and 21 years and to propose a limited sampling strategy to estimate individual MPA AUCs. Forty-one patients received long-term oral MMF continuous therapy as part of a triple immunosuppressive regimen, which also included cyclosporine or tacrolimus (n=3) and corticosteroids. Therapy was initiated at a dose of 600 mg/m twice daily. The population parameters were calculated from an initial group of 32 patients. The data were analyzed by nonlinear mixed-effect modeling using a 2-compartment structural model with first-order absorption and a lag time. The interindividual variability in the initial volume of distribution was partially explained by the fact that this parameter was weight-dependent. Fifteen concentration-time profiles from 13 patients were used to evaluate the predictive performance of the Bayesian approach and to devise a limited sampling strategy. The protocol, involving two sampling times, 1 and 4 hours after oral administration, allows the precise and accurate determination of MPA AUCs (bias -0.9 microg.h/mL; precision 6.02 microg.h/mL). The results of this study combine the relationships between the pharmacokinetic parameters of MPA and patient covariates, which may be useful for dose adjustment, with a convenient sampling procedure that may aid in optimizing pediatric patient care.     

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.     

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 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.