Estimation of population pharmacokinetic parameters in the presence of non-compliance

In population pharmacokinetic (PK) studies, patients' drug plasma profiles are routinely analyzed assuming that all patients took their drug at the times and in the amounts specified. However, patient non-compliance with the prescribed drug regimen is a leading source of failure to drug therapy. It has been reported that over 30% of patients routinely skip doses regardless of their disease, prognosis, or symptoms. This brings into question the assumption regarding full compliance for population PK analyses. This paper describes the estimation of population PK parameters in the presence and absence of non-compliance while either assuming full compliance or estimating compliance using a hierarchical Bayesian approach. Assessment of compliance for a given dose was limited to one of three possibilities: no dose was taken at the prescribed time, the prescribed dose was taken at the prescribed time, or twice the prescribed dose was taken at the prescribed time. Simulated data sets based on a one-compartment pharmacokinetic model with first order elimination were analyzed using WinBUGS* (Bayesian inference Using Gibbs Sampling) software. An initial feasibility simulation experiment, using a simple, but informative PK sampling design with bolus input of drug, was performed. A second simulation study was then carried out using a more realistic sampling design and first-order input of drug. The simulated sampling design included observations after known doses as well as after uncertain doses. Results from the feasibility study revealed that when compliance was estimated instead of being assumed to be 100%, the relative prediction error for clearance (CL) decreased from 0.25 to 0.10 for 60% compliance and from 0.6 to 0.2 for 35% compliance. Estimates of the interoccasion variability of clearance were improved by compliance estimation but still had substantial positive bias. Estimated of interindividual variability were relatively insensitive to compliance estimation. Estimates for volume of distribution (V) and its associated variances were not affected by incorporation of compliance estimates, perhaps due to the specific sampling design that was used. The design was relatively uninformative regarding V. In the more realistic study, estimates for CL, V and the difference between the absorption rate constant and the elimination rate constant (KA-K) were improved by the incorporation of compliance estimation. The median relative errors were reduced from 0.51 to -0.01 for CL, from 0.49 to 0.04 for V, and from 0.49 to -0.02 for Ka-K. The bias in interoccasion variances for V and CL appeared to be reduced by compliance estimation while estimates of interindividual variability were not affected in a systematic fashion. The bias in the residual error variance was decreased from a relative error of about 2 to close to 0. The use of hierarchical Bayesian modeling with the incorporation of compliance estimation decreased the bias in the typical value parameter but the effects on variance parameters were less consistent. The encouraging results of these simulation experiments will hopefully stimulate further evaluation of this methodology for the estimation of population pharmacokinetic parameters in the presence of potential patient noncompliance.     

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.     

Predictive performance of a semiparametric method to estimate population pharmacokinetic parameters using NONMEM

Routine clinical pharmacokinetic (PK) data collected from patients receiving inulin were analyzed to estimate population PK parameters; 560 plasma concentration determinations for inulin were obtained from 90 patients. The data were analyzed using NONMEM. The population PK parameters were estimated using a Constrained Longitudinal Splines (CLS) semiparametric approach and a first-order conditional method (FOCE). The mean posterior individual clearance values were 7.73 L/hr using both parametric and semiparametric methods. This estimation was compared with clearances estimated using standard nonlinear weighted least squares approach (reference value, 7.64 L/hr). The bias was not statistically different from zero and the precision of the estimates was 0.415 L/hr using parametric method and 0.984 L/hr using semiparametric method. To evaluate the predictive performances of the population parameters, 17 new subjects were used. First, the individual inulin clearance values were estimated from drug concentration-time curve using a nonlinear weighted least-squares method then they were estimated using the NONMEM POSTHOC method obtained using parametric and CLS methods as well as an alternative method based on a Monte Carlo simulation approach. The population parameters combined with two individual inulin plasma concentrations (0.25 and 2 hr) led to an estimation of individual clearances without bias and with a good precision. This paper not only evaluates the relative performance of the parametric and the CLS methods for sparse data but also introduces a new method for individual estimation.     

Evaluation of IPPSE, an alternative method for sequential population PKPD analysis

The aim of this study is to present and evaluate an alternative sequential method to perform population pharmacokinetic-pharmacodynamic (PKPD) analysis. Simultaneous PKPD analysis (SIM) is generally considered the reference method but may be computationally burdensome and time consuming. Evaluation of alternative approaches aims at speeding up the computation time and stabilizing the estimation of the models, while estimating the model parameters with good enough precision. The IPPSE method presented here uses the individual PK parameter estimates and their uncertainty (SE) to propagate the PK information to the PD estimation step, while the IPP method uses the individual PK parameters only and the PPP&D method utilizes the PK data. Data sets (n = 200) with various study designs were simulated according to a one-compartment PK model and a direct Emax PD model. The study design of each dataset was randomly selected. The same PK and PD models were fitted to the simulated observations using the SIM, IPP, PPP&D and IPPSE methods. The performances of the methods were compared with respect to estimation precision and bias, and computation time. Estimated precision and bias for the IPPSE method were similar to that of SIM and PPP&D, while IPP had higher bias and imprecision. Compared with the SIM method, IPPSE saved more computation time (61%) than PPP&D (39%), while IPP remained the fastest method (86% run time saved). The IPPSE method is a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.     

Predictive Performance of a Semiparametric Method to Estimate Population Pharmacokinetic Parameters Using NONMEM

Routine clinical pharmacokinetic (PK) data collected from patients receiving inulin were analyzed to estimate population PK parameters; 560 plasma concentration determinations for inulin were obtained from 90 patients. The data were analyzed using NONMEM. The population PK parameters were estimated using a Constrained Longitudinal Splines (CLS) semiparametric approach and a first-order conditional method (FOCE). The mean posterior individual clearance values were 7.73 L/hr using both parametric and semiparametric methods. This estimation was compared with clearances estimated using standard nonlinear weighted least squares approach (reference value, 7.64 L/hr). The bias was not statistically different from zero and the precision of the estimates was 0.415 L/hr using parametric method and 0.984 L/hr using semiparametric method. To evaluate the predictive performances of the population parameters, 17 new subjects were used. First, the individual inulin clearance values were estimated from drug concentration–time curve using a nonlinear weighted least-squares method then they were estimated using the NONMEM POSTHOC method obtained using parametric and CLS methods as well as an alternative method based on a Monte Carlo simulation approach. The population parameters combined with two individual inulin plasma concentrations (0.25 and 2 hr) led to an estimation of individual clearances without bias and with a good precision. This paper not only evaluates the relative performance of the parametric and the CLS methods for sparse data but also introduces a new method for individual estimation.     

Population pharmacokinetic modeling of oral cyclosporin using NONMEM: comparison of absorption pharmacokinetic models and design of a Bayesian estimator

There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.     

Simplification of therapeutic drug monitoring for twice-daily regimens of lopinavir/ritonavir for HIV infection

Cost and inconvenience limit the application of full 12-hour pharmacokinetic (PK) analysis for routine therapeutic drug monitoring of antiretroviral medications. We explore whether lopinavir (LPV) and ritonavir (RTV) exposures can be estimated with limited sampling for patients taking twice-daily LPV/RTV. One hundred and one PK profiles from 81 patients, most receiving salvage therapies including twice-daily LPV/RTV, were obtained for the analysis. After a minimum of 2 weeks on a stable regimen, blood was drawn immediately before and at 1, 2, 4, 6, 8, 10, and 12 hours after a timed medication dose. Plasma drug concentrations were determined by a validated HPLC-MS-MS assay. Peak concentrations, evening troughs, and AUC0-12 h were entered into linear and log10-log10 linear regression models to determine the best correlation with LPV and RTV plasma concentrations using a maximum of 2 time points. The accuracy and precision of PK parameter estimates of the resultant models were tested on data collected for an additional 25 patients. Twelve models using various combinations of 2 timed LPV concentrations afforded accurate (maximum % bias = -6.45) and precise (relative standard deviation < 15%) estimates for the LPV peak concentration or AUC0-12h. Four sets of 2 concentrations provided simultaneous estimates of both PK parameters, with the best estimates derived from data collected at 2 and 6 hours postdose. Evening trough concentrations were the best estimators of the daily nadir; however, no adequate substitute for collecting blood 12 hours postdose emerged from this analysis.     

Prednisolone: limited sampling strategies for estimating pharmacokinetic parameters

To develop limited-sampling strategy (LSS) models for estimating prednisolone's area under plasma concentration versus time curve (AUC(0-infinity)), its maximum concentration in plasma (C(max)), and total clearance (CL/F). Healthy subjects (n = 24), enrolled in a bioequivalence study, received 20 mg PO of the prodrug prednisone as reference and test tablets, and plasma prednisolone concentrations (n = 576) were measured by a validated HPLC assay. A linear regression analysis of AUC(0-infinity), C(max), CL/F, and log(CL/F) against the plasma prednisolone concentrations for the reference formulation was carried out to develop LSS models to estimate these parameters. The LSS models were validated on the test formulation data sets and on simulated sets generated by the software ADAPT II. LSS models based on a single [1.5 hours for C(max) and 7 hours for AUC(0-infinity), CL/F, and log(CL/F)] plasma sample, accurately estimated (R2 = 0.84-0.97, mean bias < 1%; mean precision < 10%) these pharmacokinetic parameters. Validation tests indicated that the most informative single-point LSS models developed for the reference formulation provide precise estimates (R(2) > 0.83; mean bias < 3%; mean precision < 10%) of the corresponding pharmacokinetic parameters for the test formulation. LSS models based on the two most informative sampling points (1.5 and 7 hours) were required for accurate estimates (R(2) > 0.87; mean bias < 6%; mean precision < 8%) of prednisolone's C(max), AUC(0-infinity), CL/F, and log(CL/F) for the simulated data sets. Finally, bioequivalence assessment of the prednisone formulations, based on LSS-derived AUC(0-infinity) and C(max) values provided results identical to those obtained using the original values for these parameters. One- and 2-point LSS models provided accurate estimates of prednisolone's C(max), AUC(0-infinity), and CL/F, following single oral doses of prednisone, and allowed correct assessment of bioequivalence between two prednisone formulations.     

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.     

Population Pharmacokinetics and Pharmacodynamics of Ribavirin in Patients with Chronic Hepatitis C Genotype 1 Infection

We report a population pharmacokinetic (PK) and pharmacodynamic (PD) model of orally administered ribavirin in patients with chronic hepatitis C virus (HCV) infection enrolled in a multicenter clinical trial, including the estimation of covariate effects on ribavirin PK parameters and sustained viral response (SVR). Ribavirin concentrations obtained from 144 patients, consisting of n = 71 African American (AA) and n = 73 Caucasian Americans (CA), during 24 weeks of therapy were best described by a two-compartment model with first-order absorption and elimination parameterized in terms of apparent oral clearance (CL/F), apparent central volume (Vc/F), apparent peripheral volume (Vp/F), and apparent intercompartmental clearance (Q/F). The typical population parameters were CL/F (19.0 L/h), Vc/F (1,130 L), Vp/F (4,020 L), and Q/F (38.6). The Vp/F was approximately 50% greater in AA compared to CA. Significant covariates in the SVR model included IL-28B genotype, homeostasis model assessment of insulin resistance, and ribavirin exposure during the first week (AUC(0-7)). The population PK and logistic regression models both described the observed ribavirin concentration data and SVR data well. These findings suggest that optimization of ribavirin plasma concentrations during the first week of ribavirin dosing is most critical in AA patients in order to increase the rate of SVR, especially those with the IL-28B TT genotype.     

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models.     

Comparison of ED, EID, and API Criteria for the Robust Optimization of Sampling Times in Pharmacokinetics

Optimization of the sampling schedule can be used in pharmacokinetic (PK) experiments to increase the accuracy and the precision of parameter estimation or to reduce the number of samples required. Several optimization criteria that formally incorporate prior parameter uncertainty have been proposed earlier. These criteria consist in finding the sampling schedule that maximizes the expectation (over a given parameter distribution) of det F (ED-optimality) or Log(det F) (API-optimality), or minimizes the expectation of 1/det F (EID-optimality), where F is the Fisher information matrix. The precision and the accuracy of parameter estimation after having fitted a PK model to a small number of optimal data points (determined according to D, ED, EID, and API criteria) or to a naive sampling schedule were compared in a Monte Carlo simulation study. A one-compartment model with first-order absorption rate (3 parameters) and a two-compartment model with zero-order infusion rate (4 parameters) were considered. Data were simulated for 300 subjects with both structural models, combined with several residual error models (homoscedastic, heteroscedastic with constant or variable coefficient of variation). Interindividual variabilities in PK parameters ranged from 25–66%. ED-, EID-, and API-optimal sampling times were calculated using the software OSP-Fit. Three or five samples were allowed for parameter estimation by extended least-squares. Performances of each design criterion were evaluated in terms of mean prediction error, root mean squared error, and number of acceptable estimates (i.e., with a SE less than 30%). Compared to the D-optimal design, the EID and API designs reduced the bias and the imprecision of the estimation of the parameters having a large interindividual variability. Moreover, the API design resulted in some cases in a higher number of acceptable estimates.     

Limited and optimal sampling strategies for etoposide and etoposide catechol in children with leukemia

Etoposide is used to treat childhood malignancies, and its plasma pharmacokinetics have been related to pharmacodynamic endpoints. Limiting the number of samples should facilitate the assessment of etoposide pharmacokinetics in children. We compared limited sampling strategies using multiple linear regression of plasma concentrations and clearance with Bayesian methods of estimating clearance using compartmental pharmacokinetic models. Optimal sampling times were estimated in the multiple linear regression method by determining the combination of two samples which maximized the correlation coefficient, and in the Bayesian estimation approach by minimizing the variance in estimates of clearance. Clearance estimates were compared to the actual clearances from Monte Carlo-simulated data and predicted clearances estimated using all available plasma concentrations in clinical data from children with acute lymphoblastic leukemia. Multiple linear regression poorly predicted clearance (mean bias 8.3%, precision 17.5%), but improved if plasma concentrations were logarithmically transformed (mean bias 1.4%, precision 12.5%). Bayesian estimation methods with optimal samples gave the best overall prediction (mean bias 2.5%, precision 6.8%) and also performed better than regression methods for abnormally high or low clearances. We conclude that Bayesian estimation with limited sampling gives the best estimates of etoposide clearance.     

Drug–drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes

Purpose To determine the optimal sampling time design of a drug–drug interaction (DDI) study for the estimation of apparent clearances (CL/F) of two co-administered drugs (SX, a phase I compound, potentially a CYP3A4 inhibitor, and MDZ, a reference CYP3A4 substrate) without any in vivo data using physiologically based pharmacokinetic (PBPK) predictions, population PK modelling and multiresponse optimal design. Methods PBPK models were developed with AcslXtreme using only in vitro data to simulate PK profiles of both drugs when they were co-administered. Then, using simulated data, population PK models were developed with NONMEM and optimal sampling times were determined by optimizing the determinant of the population Fisher information matrix with PopDes using either two uniresponse designs (UD) or a multiresponse design (MD) with joint sampling times for both drugs. Finally, the D-optimal sampling time designs were evaluated by simulation and re-estimation with NONMEM by computing the relative root mean squared error (RMSE) and empirical relative standard errors (RSE) of CL/F. Results There were four and five optimal sampling times (=nine different sampling times) in the UDs for SX and MDZ, respectively, whereas there were only five sampling times in the MD. Whatever design and compound, CL/F was well estimated (RSE < 20% for MDZ and <25% for SX) and expected RSEs from PopDes were in the same range as empirical RSEs. Moreover, there was no bias in CL/F estimation. Since MD required only five sampling times compared to the two UDs, D-optimal sampling times of the MD were included into a full empirical design for the proposed clinical trial. A joint paper compares the designs with real data. Conclusion This global approach including PBPK simulations, population PK modelling and multiresponse optimal design allowed, without any in vivo data, the design of a clinical trial, using sparse sampling, capable of estimating CL/F of the CYP3A4 substrate and potential inhibitor when co-administered together.     

Impact of low percentage of data below the quantification limit on parameter estimates of pharmacokinetic models

The objectives of the simulation study were to evaluate the impact of BQL data on pharmacokinetic (PK) parameter estimates when the incidence of BQL data is low (e.g. ≤10%), and to compare the performance of commonly used modeling methods for handling BQL data such as data exclusion (M1) and likelihood-based method (M3). Simulations were performed by adapting the method of a recent publication by Ahn et al. (J Phamacokinet Pharmacodyn 35(4):401–421, 2008). The BQL data in the terminal elimination phase were created at frequencies of 1, 2.5, 5, 7.5, and 10% based on a one- and a two-compartment model. The impact of BQL data on model parameter estimates was evaluated based on bias and imprecision. The simulations demonstrated that for the one-compartment model, the impact of ignoring the low percentages of BQL data (≤10%) in the elimination phase was minimal. For the two-compartment model, when the BQL incidence was less than 5%, omission of the BQL data generally did not inflate the bias in the fixed-effect parameters, whereas more pronounced bias in the estimates of inter-individual variability (IIV) was observed. The BQL data in the elimination phase had the greatest impact on the volume of distribution estimate of the peripheral compartment of the two-compartment model. The M3 method generally provided better parameter estimates for both PK models than the M1 method. However, the advantages of the M3 over the M1 method varied depending on different BQL censoring levels, PK models and parameters. As the BQL percentages decreased, the relative gain of the M3 method based on more complex likelihood approaches diminished when compared to the M1 method. Therefore, it is important to balance the trade-off between model complexity and relative gain in model improvement when the incidence of BQL data is low. Understanding the model structure and the distribution of BQL data (percentage and location of BQL data) allows selection of an appropriate and effective modeling approach for handling low percentages of BQL data.     

Population pharmacokinetics of duloxetine in Japanese pediatric patients with major depressive disorder

The objectives of this analysis were to characterize the pharmacokinetics of duloxetine in Japanese pediatric patients aged 9–17 years with major depressive disorder (MDD) and to explore potential intrinsic factors affecting its pharmacokinetics. A population pharmacokinetic (PK) model was developed with plasma steady-state duloxetine concentrations from Japanese pediatric patients with MDD in an open-label long-term extension trial in Japan (ClinicalTrials.gov Identifier: NCT03395353). Duloxetine pharmacokinetics in Japanese pediatric patients was well described by a one-compartment model with first-order absorption. The population mean estimates of CL/F and V/F of duloxetine were 81.4 L/h and 1170 L, respectively. Patient intrinsic factors were assessed for their potential influence on duloxetine apparent clearance (CL/F). Only sex was identified as a statistically significant covariate of duloxetine CL/F. Duloxetine pharmacokinetic parameters and model-predicted duloxetine concentrations at steady state in the Japanese pediatric population were compared with those in Japanese adults. The mean duloxetine CL/F in pediatrics is slightly higher than adults, it is, however, expected that comparable steady-state duloxetine exposure in pediatric patients can be achieved with the approved dose regimen for adults. The population PK model provides useful information to understand the pharmacokinetic characteristics of duloxetine for Japanese pediatric patients with MDD.ClinicalTrials.gov identifierNCT03395353     

Integrating in vitro ADMET data through generic physiologically based pharmacokinetic models

Early estimation of kinetics in man currently relies on extrapolation from experimental data generated in animals. Recent results from the application of a generic physiologically based model, Cloe PK) (Cyprotex), which is parameterised for human and rat physiology, to the estimation of plasma pharmacokinetics, are summarised in this paper. A comparison with predictive methods that involve scaling from in vivo animal data can also be made from recently published data. On average, the divergence of the predicted plasma concentrations from the observed data was 0.47 log units. For the external test set, > 70% of the predicted values of the AUC were within threefold of the observed values. Furthermore, the model was found to match or exceed the performance of three published interspecies scaling methods for estimating clearance, all of which showed a distinct bias towards overprediction. It is concluded that Cloe PK, as a means of integrating readily determined in vitro and/or in silico data, is a powerful, cost-effective tool for estimating exposure and kinetics in drug discovery and risk assessment that should, if widely adopted, lead to major reductions in the need for animal experimentation.     

Population pharmacokinetics and bayesian estimator of cyclosporine in pediatric renal transplant patients

Cyclosporine A (CsA) is an immunosuppressive drug widely used in pediatric renal graft recipients. Its large interindividual pharmacokinetic variability and narrow therapeutic index render therapeutic drug monitoring necessary. However, information about CsA pharmacokinetics is scarce and no population pharmacokinetic (popPK) studies in these populations have been reported so far. to the objectives of this study were 1) to develop a PKpop model and identify the individual factors influencing the variability of CsA pharmacokinetics in pediatric kidney recipients; and 2) to build a Bayesian estimator allowing the estimation of the main PK parameters and exposure indices to CsA on the basis of a limited sampling strategy (LSS). The popPK analysis was performed using the NONMEM program. A total of 256 PK profiles of CsA collected in 98 pediatric renal transplant patients (mean age 9.7 +/- 4.5 years old) within the first year posttransplantation were studied. A 2-compartment model with first-order elimination, and Erlang distribution to describe the absorption phase, fitted the data adequately. For Bayesian estimation, the best LSS was determined based on its performance in estimating area under the concentration-time curve (AUC0-12h) and validated in an independent group of 20 patients. The popPK analysis identified body weight and posttransplant delay as individual factors influencing the apparent central volume of distribution and the apparent clearance, respectively. Bayesian estimation allowed accurate prediction of AUC0-12h using predose, C1h, and C3h blood samples with a mean bias between observed and estimated AUC of 0.5% +/- 11% and good precision (root mean square error = 10.9%). This article reports the first popPK study of CsA in pediatric renal transplant patients. It confirms the reliability and feasibility of CsA AUC estimation in this population. The body weight and the posttransplantation delay were identified to influence PK interindividual variability of CsA and were included in the Bayesian estimator developed, which could be helpful in further clinical trials.     

Prediction of human pharmacokinetics of therapeutic monoclonal antibodies from simple allometry of monkey data

Interspecies allometric scaling is a useful tool for calculating human pharmacokinetic (PK) parameters from data in animals. In this study, in order to determine the scaling exponent in a simple allometric equation that can predict human clearance (CL) and distribution volume at steady state (Vss) of monoclonal antibodies (mAbs) from monkey data alone, PK data of 24 mAbs were collected and analyzed according to the types of targeted antigens (soluble or membrane-bound antigens). Based on the observed PK data in humans (at clinical doses) and monkeys (at >1 mg/kg), where the PK is expected to be linear, the mean scaling exponents in the allometric equation for CL and Vss, respectively, against body weight were calculated to be 0.79 and 1.12 [95% confidence intervals (CIs): 0.69-0.89 and 0.96-1.28] for soluble antigens, and 0.96 and 1.00 (95% CIs: 0.83-1.09 and 0.87-1.13) for membrane-bound antigens. Using these exponents and monkey PK data (at >1 mg/kg) alone, both human CL and Vss of mAbs can be predicted with reasonable accuracy, i.e., within 2-fold of the observed values. Compared with traditional allometric scaling using PK data from three or more preclinical species, this approach is simple, quick, resource-saving, and useful in drug discovery and development.     

Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients

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