Normalized Prediction Distribution Errors Research Articles

Population pharmacokinetic analysis of tacrolimus in Chinese myasthenia gravis patients

The importance of tacrolimus in the treatment of myasthenia gravis (MG) as a substitute for corticosteroid-dependent immunosuppressive therapy is increasing. Thus far, however, no population pharmacokinetic (PopPK) analysis of tacrolimus in treating MG patients has been published. This article aimed to construct a PopPK model of tacrolimus for Chinese MG patients with the goal of improving its performance in MG treatment. A total of 253 trough concentration records were obtained from 83 Chinese MG patients. The effects of demographics, lifestyle and health status, biochemical test data, disease progression and treatment-related information (including co-administered medications) as covariates on the various parameters were investigated. The covariate selection was based on biological plausibility, clinical significance, statistical significance and reduction in inter-individual variability (IIV). Bootstrap and normalized prediction distribution error (NPDE) analysis were performed to validate the final model. A one-compartment PopPK model with first-order elimination and a fixed absorption phase was constructed. The estimated apparent oral clearance (CL/F) and apparent oral volume of distribution (V/F) were 3.6 L/h and 1700 L, respectively, in the MG patients. Hematocrit and blood urea nitrogen were identified as two covariates that significantly influenced the CL/F. Immunoglobulin treatment (PRO) also had the potential to influence V/F, which was consistent with the clinical observations and the high protein-binding property of tacrolimus. Other covariates including age, weight, gender and co-administered medications had no obvious influence on CL/F or V/F. The first PopPK model of tacrolimus in MG patients was established. The identified covariates were of biological plausibility and clinical importance to help individualize the dosing schedule in MG patients.

Phenobarbital in intensive care unit pediatric population: predictive performances of population pharmacokinetic model.

An external evaluation of phenobarbital population pharmacokinetic model described by Marsot et al. was performed in pediatric intensive care unit. Model evaluation is an important issue for dose adjustment. This external evaluation should allow confirming the proposed dosage adaptation and extending these recommendations to the entire intensive care pediatric population. External evaluation of phenobarbital published population pharmacokinetic model of Marsot et al. was realized in a new retrospective dataset of 35 patients hospitalized in a pediatric intensive care unit. The published population pharmacokinetic model was implemented in nonmem 7.3. Predictive performance was assessed by quantifying bias and inaccuracy of model prediction. Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were also evaluated. A total of 35 infants were studied with a mean age of 33.5 weeks (range: 12 days-16 years) and a mean weight of 12.6 kg (range: 2.7-70.0 kg). The model predicted the observed phenobarbital concentrations with a reasonable bias and inaccuracy. The median prediction error was 3.03% (95% CI: -8.52 to 58.12%), and the median absolute prediction error was 26.20% (95% CI: 13.07-75.59%). No trends in NPDE and VPC were observed. The model previously proposed by Marsot et al. in neonates hospitalized in intensive care unit was externally validated for IV infusion administration. The model-based dosing regimen was extended in all pediatric intensive care unit to optimize treatment. Due to inter- and intravariability in pharmacokinetic model, this dosing regimen should be combined with therapeutic drug monitoring.

Population Pharmacokinetics of Valproic Acid in Patients with Mania: Implication for Individualized Dosing Regimens

PurposeThis study characterized the population pharmacokinetic properties of valproic acid in patients with mania and determined potential factors that affect the pharmacokinetic properties of valproic acid in this population. MethodsRoutine therapeutic drug monitoring of valproic acid concentrations, demographic data, and concomitant medications from 206 hospitalized patients with mania were retrospectively collected from Somdet Chaopraya Institute of Psychiatry and Srithanya Hospital, Thailand. Nonlinear mixed-effect modeling was used for data analysis. Covariate model building was conducted using stepwise forward addition and stepwise backward elimination. The final model was evaluated using bootstrap analysis and normalized prediction distribution error. The results were compared with those previously reported in patients with epilepsy given that there is an evidence of a difference in valproic acid clearance between patients with mania and those with epilepsy. FindingsValproic acid data were adequately described by a 1-compartment model. Significant predictors for valproic acid clearance included valproic acid dose and weight. The population estimates for valproic acid CL/F and V/F were 0.464 L/h and 23.3 L, respectively. Valproic acid clearance obtained from this study did not seem to be significantly different from that of patients with epilepsy. ImplicationsA qualified population pharmacokinetic model for valproic acid in patients with mania was developed. This model could be used to optimize valproic acid therapy in patients with mania. Valproic acid clearance could be predicted from valproic acid dose and weight of patients. This predicted clearance can subsequently be used for individualization of optimum valproic acid maintenance dose.

Population pharmacokinetics analysis of intravenous busulfan in Chinese patients undergoing hematopoietic stem cell transplantation.

There are several reports describing population pharmacokinetic (popPK) models of busulfan (BU). However, limited information is available in Chinese hematopoietic stem cell transplantation (HSCT) patients. The present study aimed to establish a popPK model of intravenous BU in Chinese HSCT patients for individualized drug therapy. The popPK model of BU was developed from a total of 284 concentration-time points from 53 patients. The effects of demographic and biochemical covariates were investigated by nonlinear mixed effect model (NONMEM) software. Plots, visual predictive check (VPC), bootstrap and normalized prediction distribution error (NPDE) were performed to determine the stability and the reliability of the final model. A one-compartment model with first-order elimination process was confirmed as the final structural model for BU. For a typical patient whose body surface area (BSA) is 1.7m2 , the population typical values of CL and Vd were 11.86L/h, and 48.2L, respectively. The result suggested BSA showed significant influence on CL and Vd (P<.001). Plots revealed the final model was performing a goodness fit. The steady rate verified by bootstrap was 100%, relative deviation was less than 4.00%, estimated value of final model was in the 95% confidence interval (CI). The VPC results showed the observed values were almost all positioned within the 5th and 95th CIs. The mean and variance of the NPDE were 0.0363 (Wilcoxon signed-rank test, 0.298) and 0.877 (Fisher variance test, 0.134; SW test of normality, 0.108), respectively. The global adjusted P value was 0.305, which indicated that the prediction of the BU popPK model was adequate. A physician-friendly Microsoft Excel-base tool was implemented using the final popPK model for designing individualized dosing regimens.

Mechanistic Population Pharmacokinetics of Morphine in Neonates With Abstinence Syndrome After Oral Administration of Diluted Tincture of Opium

Conducting and analyzing clinical trials in vulnerable neonates are extremely challenging. The aim of this analysis is to develop a morphine population pharmacokinetics (PK) model using data collected during a randomized control trial in neonates with abstinence syndrome (NAS). A 3-compartment morphine structural PK model after intravenous (IV) administration from previously published work was utilized as prior, whereas an allometric scaling method with physiological consideration was used to extrapolate a PK profile from adults to pediatrics. The absorption rate constant and bioavailability were estimated in NAS after oral administration of diluted tincture of opium (DTO). Goodness-of-fit plots along with normalized prediction distribution error and bootstrap method were performed for model evaluation. We successfully extrapolated the PK profile from adults to pediatrics after IV administration. The estimated first-order absorption rate constant and bioavailability were 0.751 hour(-1) and 48.5%, respectively. Model evaluations showed that the model can accurately and precisely describe the observed data. The population pharmacokinetic model we derived for morphine after oral administration of DTO is reasonable and acceptable; therefore, it can be used to describe the PK and guide future studies. The integration of the previous population PK knowledge as prior information successfully overcomes the logistic and practical issue in vulnerable neonate population.

A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R

Background and objectiveNon-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration–time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. MethodsThe nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration–time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. ResultsThe ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration–time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. ConclusionsThe ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration–time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://cran.r-project.org/web/packages/ncappc/index.html/) and GitHub (https://github.com/cacha0227/ncappc/).

Population pharmacokinetic-pharmacodynamic (PopPK/PD) modeling of risperidone and its active metabolite in Chinese schizophrenia patients.

Risperidone is a second-generation antipsychotic agent commonly used in the treatment of ~ 31.1% of schizophrenia patients in China, it is the most commonly-prescribed antipsychotic agent. Despite the abundant use of risperidone, population pharmacokinetic-pharmacodynamic models of risperidone have not been performed in Chinese schizophrenia patients. The objective of this study was to develop a population pharmacokinetic-pharmacodynamic (PopPK/PD) model to describe the PK behavior and efficacy of risperidone and 9-hydroxy-risperidone (active metabolite) in Chinese patients. Plasma concentration data (702 measurements from 131 patients) and positive and negative syndrome scale (PANSS) scores (258 observations from 56 patients) were analyzed using a nonlinear mixed-effects modeling (NONMEM) approach with first-order conditional estimation with interaction (FOCEI). The influence of potential covariates was evaluated. Model robustness was assessed using external validation, normalized prediction distribution error, nonparametric bootstrap, and visual predictive check approaches. Risperidone concentration data were well described by a one-compartmental model incorporating an additional compartment that refers to the concentration profiles of 9-hydroxy-risperidone. A complex absorption procedure was incorporated into the model to describe the metabolism of risperidone to 9-hydroxy-risperidone in the gastrointestinal (GI) tract. A binomial distribution in the estimated clearance (CL) of risperidone has been identified in our model. Decrease in PANSS score along with total AUC (AUCtotal) of risperidone and 9-hydroxy-risperidone was best characterized by an Emax model with 3 transit compartments describing the delay of drug effect. Considerable differences in PK behavior and drug effect of risperidone have been identified among Chinese extensive metabolizing (EM) and poor metabolizing (PM) patients. This PopPK/PD model may fulfill individualized treatment in clinical practice and may potentially be transferred to other antipsychotic therapies.

PROSPECTIVE VALIDATION OF A MODEL-BASED DOSING REGIMEN FOR AMIKACIN IN NEONATES: FEASIBLE AND RELEVANT

IntroductionA neonatal amikacin dosing regimen was previously developed based on a population pharmacokinetic model. The aim of the current study was to prospectively validate this model-derived dosing regimen.MethodsFirst, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (&lt;3 mg/L) and peak (&gt;24 mg/L) levels. Secondly, observed concentrations were compared with model-predicted concentrations, whereby the results of an NPDE (normalized prediction distribution error) were considered as well. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (prescription errors and disease characteristics of outliers) were explored.ResultsIn 579 neonates [median (range) birth bodyweight 2285 (420–4850) g, postnatal age 2 (1–30) days, gestational age 34 (24–41) weeks], 90.5% of early peak levels reached 24 mg/L and 60.2% of trough levels was &lt;3 mg/L (93.4% ≤5 mg/L). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations &gt;24 mg/L were reached in almost all patients. Trough values &lt;3 mg/L were documented in 78–100% and 45–96% of simulated cases, respectively, when ibuprofen was co-administered or not. Suboptimal trough levels were found in patient subgroups with postnatal age &lt;14 days and current weight &gt;2000g.ConclusionsProspective validation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Adapted dosing for patients with suboptimal trough levels was proposed. Besides improving dosing individualization, feasibility and relevance of neonatal prospective validation studies was demonstrated.

Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice.

Based on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization.

Quantitative characterization of circadian rhythm of pulmonary function in asthmatic patients treated with inhaled corticosteroids

The aim of this study was to characterize the circadian rhythm observed for forced expiratory volume in 1s (FEV1) in patients with persistent asthma being treated with inhaled corticosteroids. The database included 3379 FEV1 measurements from 189 patients with mild to moderate asthma. A model using the sum of two Sine functions with periods of 12 and 24h and a constant component of mean circadian rhythm adequately described the circadian rhythm in FEV1 measurements over time. The model adequateness was evaluated by various approaches including visual predictive check (VPC), prediction-corrected VPC, standardized VPC and normalized prediction distribution error. Covariates tested included age, body weight, height, body mass index, baseline FEV1, and sex. Age and height were found to have significant effects on the mean FEV1 level and no covariate was found to have an effect on the magnitude and timing of circadian rhythm. The model predicted that a minimum FEV1 occurred in the early morning and maximum FEV1 occurred in the early afternoon, with a population mean fluctuation of 170mL, which is consistent with the finding that asthma symptoms usually exacerbate in the early morning for patients with persistent asthma. This developed model provides the first quantitative approach to describing FEV1 circadian rhythm with ICS background treatment and provided insight in designing future registration trials for asthma drug development.

Predictive Performance of a Vancomycin Population Pharmacokinetic Model in Neonates.

IntroductionThe pharmacokinetics of vancomycin are highly variable among neonates, which makes dosing challenging in this population. However, adequate drug exposure is critical, especially when treating methicillin-resistant Staphylococcus aureus (MRSA) infections. Utilization of population pharmacokinetic models and Bayesian methods offers the potential for developing individualized therapeutic approaches. To meet this need, a neonatal vancomycin population pharmacokinetic model was recently published. The current study sought to externally evaluate the predictive performance and generalizability of this model.MethodsA retrospective chart review of neonates who received vancomycin and had ≥1 peak and ≥1 trough concentrations at five Intermountain Healthcare neonatal intensive care units from 2006 to 2013 was performed and served as the external validation cohort. The published population pharmacokinetic model was implemented in NONMEM 7.2 with the structural and variance parameter values set equal to the estimates reported previously. The model was then used to predict the first peak and trough concentration for each neonate in the validation cohort and the model prediction error and absolute prediction error were calculated. Normalized prediction distribution errors (NPDE) were also evaluated.ResultsA total of 243 neonates were studied with a median postmenstrual age of 33 (range: 23–54) weeks and a median weight of 1.6 (range: 0.4–6.8) kg. The model predicted the observed vancomycin concentrations with reasonable precision. For all vancomycin concentrations, the median prediction error was −0.8 (95% CI: −1.4 to −0.4) mg/L and the median absolute prediction error was 3.0 (95% CI: 2.7–3.5) mg/L. No trends in NPDE across weight, postmenstrual age, serum creatinine, or time after dose were observed.ConclusionAn evaluation of a recently published neonatal vancomycin population pharmacokinetic model in a large external dataset supported the predictive performance and generalizability of the model. This model may be useful in evaluating neonatal vancomycin dosing regimens and estimating the extent of drug exposure.Electronic supplementary materialThe online version of this article (doi:10.1007/s40121-015-0067-9) contains supplementary material, which is available to authorized users.

Population pharmacokinetic analysis of bupivacaïne used in regional anaesthesia

Introduction The primary aim of this study was to establish the population pharmacokinetic (PPK) model of bupivacaine after combined lumbar plexus and sciatic nerve blocks and secondary aim is to assess the effect of patient's characteristics including age, body weight, sex and drugs co medication (Clonidine and Propofol) on pharmacokinetic parameters. Materials and method A total of 59 patients scheduled for elective lower extremity surgery with combined lumbar and sciatic nerve block using plain bupivacaine 0.5% were included. The total bupivacaine plasma concentrations were measured before injection and after two blocks placement and at selected time points. Monitoring of bupivacaine was made by high performance liquid chromatography (HPLC) with ultraviolet detection. Non-linear mixed effects modeling was used to analyze the PPK of bupivacaine. This pharmacokinetic model was validated using npde package for statistic software R. The simulations confirm that our final model was valid. Results and discussion Two compartment model with first order absorption, two input compartments, a central elimination and peripheral compartment was selected. The Shapiro-Wilks test of normality for normalized prediction distribution errors for this model ( P = 0.837) showed this as a valid model. The description of the quality of the estimate by the graphic diagnostic suggests a good fitting of the final model. The selected model predicts a population clearance of 503 mL/min (residual standard error [RSE] = 16.5%, 95% CI = [534.9–866.9]) with between subject variability of 93.16%. The central and peripheral volumes of distribution were respectively 6850 L and 120 L. The absorption of bupivacaine in two sites Ka1 and Ka2 were 0.00073/min for the lumbar site and 0.0037/min for the sciatic site. Within subject variability WSV% was 30, 3%. Drug Co medication, age, body weight and sex, have no effect on the bupivacaine pharmacokinetics in this studied population. Conclusion The developed model helps us to assess the systemic absorption of bupivacaine at two injections sites.

A population pharmacokinetic model of valproic acid in pediatric patients with epilepsy: a non-linear pharmacokinetic model based on protein-binding saturation.

Valproic acid (VPA) follows a non-linear pharmacokinetic profile in terms of protein-binding saturation. The total daily dose regarding VPA clearance is a simple power function, which may partially explain the non-linearity of the pharmacokinetic profile; however, it may be confounded by the therapeutic drug monitoring effect. The aim of this study was to develop a population pharmacokinetic model for VPA based on protein-binding saturation in pediatric patients with epilepsy. A total of 1,107 VPA serum trough concentrations at steady state were collected from 902 epileptic pediatric patients aged from 3weeks to 14years at three hospitals. The population pharmacokinetic model was developed using NONMEM(®) software. The ability of three candidate models (the simple power exponent model, the dose-dependent maximum effect [DDE] model, and the protein-binding model) to describe the non-linear pharmacokinetic profile of VPA was investigated, and potential covariates were screened using a stepwise approach. Bootstrap, normalized prediction distribution errors and external evaluations from two independent studies were performed to determine the stability and predictive performance of the candidate models. The age-dependent exponent model described the effects of body weight and age on the clearance well. Co-medication with carbamazepine was identified as a significant covariate. The DDE model best fitted the aim of this study, although there were no obvious differences in the predictive performances. The condition number was less than 500, and the precision of the parameter estimates was less than 30%, indicating stability and validity of the final model. The DDE model successfully described the non-linear pharmacokinetics of VPA. Furthermore, the proposed population pharmacokinetic model of VPA can be used to design rational dosage regimens to achieve desirable serum concentrations.

Population Pharmacokinetic/Pharmacodynamic Model of Clozapine for Characterizing the Relationship Between Accumulated Exposure and PANSS Scores in Patients With Schizophrenia

The aim of this study was to characterize the relationship between accumulated exposure of clozapine and changes in Positive and Negative Syndrome Scale (PANSS) score in Chinese patients with schizophrenia by pharmacokinetic/pharmacodynamic (PK/PD) modeling. Sparse clozapine PK data and PANSS scores were collected from 2 clinical studies of Chinese inpatients with schizophrenia. Two other rich PK data sets were included for more accurate assessment of clozapine PK characteristics. The relationship between clozapine-accumulated exposure and PANSS score was investigated using linear, log-linear, E(max), and sigmoid models, and each model was evaluated using visual predictive condition and normalized prediction distribution error methods. Simulations based on the final PK/PD model were preformed to investigate the effect of clozapine on PANSS scores under different dose regimens. A total of 1391 blood clozapine concentrations from 198 subjects (180 patients and 18 healthy volunteers) and 576 PANSS scores from 137 patients were included for PK and PK/PD analysis. A first-order 2-compartment PK model with covariates gender and smoking status influencing systemic clearance adequately described the PK profile of clozapine. The decrease in total PANSS score during treatment was best characterized using cumulated clozapine area under the curve (AUC) data in the E(max) model. The maximum decrease in PANSS during clozapine treatment (Emax) was 55.4%, and the cumulated AUC(50) (cAUC(50)) required to attain half of E(max) was 296 mg·L(-1)·h(-1)·d(-1). The simulations demonstrated that the accelerated dose titration and constant dose regimens achieved a similar maximum drug response but with a slower relief of symptoms in dose titration regimen. The PK/PD model can describe the clinical response as measured by decreasing PANSS score during treatment and may be useful for optimizing the dose regimen for individual patients.

Population pharmacokinetics of bupivacaine in combined lumbar and sciatic nerve block

Objectives:The primary aim of this study was to establish the population pharmacokinetic (PPK) model of bupivacaine after combined lumbar plexus and sciatic nerve blocks and secondary aim is to assess the effect of patient's characteristics including age, body weight and sex on pharmacokinetic parameters.Materials and Methods:A total of 31 patients scheduled for elective lower extremity surgery with combined lumbar and sciatic nerve block using plain bupivacaine 0.5% were included. The total bupivacaine plasma concentrations were measured before injection and after two blocks placement and at selected time points. Monitoring of bupivacaine was made by high performance liquid chromatography (HPLC) with ultraviolet detection. Non-linear mixed effects modeling was used to analyze the PPK of bupivacaine.Results:One compartment model with first order absorption, two input compartments and a central elimination was selected. The Shapiro-Wilks test of normality for normalized prediction distribution errors for this model (P = 0.156) showed this as a valid model. The selected model predicts a population clearance of 930 ml/min (residual standard error [RSE] = 15.48%, IC 95% = 930 ± 282.24) with inter individual variability of 75.29%. The central volume of distribution was 134 l (RSE = 12.76%, IC = 134 ± 33.51 L) with inter individual variability of 63.40%. The absorption of bupivacaine in two sites Ka1 and Ka2 were 0.00462/min for the lumbar site and 0.292/min for the sciatic site. Age, body weight and sex have no effect on the bupivacaine pharmacokinetics in this studied population.Conclusion:The developed model helps us to assess the systemic absorption of bupivacaine at two injections sites.

External evaluation of population pharmacokinetic models of vancomycin in neonates: the transferability of published models to different clinical settings

Vancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study-related factors influencing the transferability of pharmacokinetic models to different clinical settings. Published neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of 6 models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation-based diagnostics (visual predictive check [VPC] and normalized prediction distribution errors [NPDE]). Differences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for 6 evaluated models were 1.35, -0.22, -0.36, 0.24, 0.66, 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still need to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin. The importance of analytical techniques for serum creatinine concentrations and vancomycin as a predictor of vancomycin concentrations in neonates has been confirmed. Dosage individualisation of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin.

From pediatric covariate model to semiphysiological function for maturation: part I-extrapolation of a covariate model from morphine to Zidovudine.

New approaches to expedite the development of safe and effective pediatric dosing regimens and first-in-child doses are urgently needed. Model-based approaches require quantitative functions on the maturation of different metabolic pathways. In this study, we directly incorporated a pediatric covariate model for the glucuronidation of morphine into a pediatric population model for zidovudine glucuronidation. This model was compared with a reference model that gave the statistically best description of the data. Both models had adequate goodness-of-fit plots and normalized prediction distribution errors (NPDE), similar population clearance values for each individual, and a Δobjective function value of 13 points (Δ2df). This supports our hypothesis that pediatric pharmacokinetic covariate models contain system-specific information that can be used as semiphysiological functions in pediatric population models. Further research should explore the validity of the semiphysiological function for other UDP-glucuronosyltransferase 2B7 substrates and patient populations and reveal how this function can be used for pediatric physiologically based pharmacokinetic models.

Predictive Performance of a Busulfan Pharmacokinetic Model in Children and Young Adults

Recently a pediatric pharmacokinetic (PK) model was developed for busulfan to explain the wide variability in PK of busulfan in children, as this variability is known to influence the outcome of hematopoietic stem cell transplantation in terms of toxicity and event free survival. This study assesses the predictive performance of this busulfan PK model in a new, more diverse pediatric population, including data from patients with different underlying diseases, ethnicities, body weights, ages, and body mass indices, from 5 international pediatric transplant centers. The previously published (original) busulfan PK model was developed from data of 245 patients (0.1-26 years of age). To externally validate this model, data were collected from another 158 patients (0.1-35 years) who underwent hematopoietic stem cell transplantation in 5 international transplant centers. Observed versus predicted plots, normalized prediction distribution error analysis, refit of the model on the external (n = 158) and combined datasets (n = 403), and subpopulation analyses were evaluated. The original busulfan PK model was found to be stable and parameter estimates precise. Concentrations predicted by this model were in good agreement with the observed concentrations from the 5 external datasets. Plasma concentrations in patients with different underlying diseases, ethnicities, body weights, ages, and body mass indices were adequately predicted. Our pediatric busulfan PK model has been externally validated. This model predicts busulfan concentrations in pediatric and young adult patients ranging between 3 and 86 kg without bias and with good precision, regardless of transplant center, underlying disease, ethnicity, body weight age, or body mass index. This busulfan PK model forms the basis for individualized busulfan dosing.

Population pharmacokinetics of lamotrigine in Indian epileptic patients

The aim of this analysis was to describe the pharmacokinetics of oral lamotrigine (LTG) in Indian epileptic patients using a population pharmacokinetic (PPK) modeling approach to confirm that the PK is similar to that of the Caucasian population, and to evaluate and confirm the impact of covariates predictive of inter-individual variability using a simulation platform. Blood samples were obtained from 95 patients, and LTG plasma concentrations were determined. Population PK modeling was performed using NONMEM. A one-compartment PK model with first-order absorption and elimination was used to describe the LTG PK. Log-likelihood profiling and normalized prediction distribution errors (NPDE) were used for model evaluation. A simulation study was performed to investigate dose regimens. Clearance (CL) was estimated to be 2.27L/h with inter-individual variability (IIV) of 29 CV%. Volume of distribution (V) was estimated to be 53.6L (31 CV% IIV). Body weight and concurrent use of carbamazepine and valproate were identified as significant covariates on clearance. Log-likelihood profiling indicated that parameters could be estimated with adequate precision, and NPDE indicated that the model adequately described the data observed. The simulation study illustrated the impact of carbamazepine and valproate on LTG PK, and negligible differences in PK between Indian and Caucasian patients. This is the first PK analysis of LTG in Indian patients. The population PK model developed adequately described the data observed. Comparison of identified PK parameters with previous PK analyses in Caucasian patients indicates that CL of LTG is similar, while V is somewhat lower compared with Caucasian patients, but this is not expected to lead to relevant differences in PK profiles during steady state.

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV‐infected infants and toddlers

Abacavir is used to treat HIV infection in both adults and children. The recommended paediatric dose is 8 mg kg(-1) twice daily up to a maximum of 300 mg twice daily. Weight was identified as the central covariate influencing pharmacokinetics of abacavir in children. A population pharmacokinetic model was developed to describe both once and twice daily pharmacokinetic profiles of abacavir in infants and toddlers. Standard dosage regimen is associated with large interindividual variability in abacavir concentrations. A maximum a posteriori probability Bayesian estimator of AUC(0-) (t) based on three time points (0, 1 or 2, and 3 h) is proposed to support area under the concentration-time curve (AUC) targeted individualized therapy in infants and toddlers. To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration-time curve (AUC) targeted dosage and individualize therapy. The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation-estimation method. The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 () h−1 (RSE 6.3%), apparent central volume of distribution 4.94 () (RSE 28.7%), apparent peripheral volume of distribution 8.12 () (RSE14.2%), apparent intercompartment clearance 1.25 () h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t. The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC(0-) (t) was developed from the final model and can be used routinely to optimize individual dosing.