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Background: Pharmacokinetic models are evaluated using three types of metrics: those based on estimating the typical pharmacokinetic parameters, those based on predicting individual pharmacokinetic parameters and those that compare data and model distributions. In the third groups of metrics, the best-known methods are Visual Predictive Check (VPC) and Normalised Prediction Distribution Error (NPDE). Despite their usefulness, these methods have some limitations, especially for the analysis of dependent concentrations, i.e., evaluated in the same patient. Objective: In this work, we propose an evaluation method that accounts for the dependency between concentrations. Methods: Thanks to the study of the distribution of simulated vectors of concentrations, the method provides one probability per individual that its observations (i.e., concentrations) come from the studied model. The higher the probability, the better the model fits the individual. By examining the distribution of these probabilities for a set of individuals, we can evaluate the model as a whole. Results: We demonstrate the effectiveness of our method through two examples. Our approach successfully detects misspecification in the structural model and identifies outlier kinetics in a set of kinetics. Conclusion: We propose a straightforward method for evaluating models during their development and selecting a model to perform therapeutic drug monitoring. Based on our preliminary results, the method is very promising but needs to be validated on a larger scale. [ABSTRACT FROM AUTHOR]

Purpose: Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on pa- rameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models. Methods: The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE. Results: Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-eters. However, all bootstraps were sensitive to estimation bias in the original datasets. Conclusions: The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process. [ABSTRACT FROM AUTHOR]

Summary Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated. [ABSTRACT FROM AUTHOR]

We address the problem of design optimization using cost functions in nonlinear mixed effects models with multiple responses. We focus on the relative feasibility of the optimized designs, in term of sampling times and of number of subjects. To do that, we extend the Fedorov-Wynn algorithm—a dedicated design optimization algorithm—to include a cost function that penalizes less feasible designs as well as to take into account multiple responses. We apply this extension to the design optimization of a joint pharmacokinetic model of infliximab and methotrexate administered in rheumatoid arthritis. We show the benefit of such an approach when substantial constraints on the design are imposed. [ABSTRACT FROM AUTHOR]

Background and objective: During the drug development process, phase I trials are the first occasion to study the pharmacokinetics of a drug. They are performed in healthy subjects, or in patients in oncology, and are designed to determine a safe and acceptable dose for the later phases of clinical trials. We performed a bibliographic survey to investigate the way pharmacokinetics are described and reported in phase I clinical trials. Methods: We performed a MEDLINE search to retrieve the list of papers published between 2005 and 2006 and reporting phase I clinical trials with a pharmacokinetic study. We used a spreadsheet to record general information concerning the study and specific information regarding the pharmacokinetics, such as the sampling times, number of subjects and method of analysis. Results: The search yielded 349 papers, of which 37 were excluded for various reasons. Nearly all of the papers in our review concerned cancer studies, although this was not a requirement in the search. Consistent with the selection process, 84% papers explicitly stated pharmacokinetics as an objective of the study. The methods section usually included a description of the pharmacokinetics (88%), but 10% of the papers provided no information concerning the methods used for the pharmacokinetics and in 2%the description was only partial. The analytical method was usually basic, with non-compartmental or purely descriptive methods. Observed concentrations and areas under the concentration-time curves were the pharmacokinetic variables most often reported. The results of the pharmacokinetic study were frequently reported in a separate paragraph of the results section, and only 22% of the studies related the pharmacokinetic findings to other results from the study, such as toxicity or efficacy. In addition, important information such as the number of subjects included in the pharmacokinetic study or the pharmacokinetic sampling scheme was sometimes not reported explicitly. Conclusion: Concerns about the decreasing cost-effectiveness of the drug development process prompted the regulatory authorities to recently recommend better integration of all available information -- including, in particular, pharmacokinetics -- in this process. In our review, we found that this information was often either missing or incomplete, which hinders that objective. We suggest several improvements in the design and the reporting of the methods and results of these studies, to ensure that all relevant information has been included. Pharmacokinetic findings should also be integrated into the broader perspective of drug development, through the study of their relationship with toxicity and/or efficacy, even in the early phase I stages. [ABSTRACT FROM AUTHOR]

We evaluate by simulation three model-based methods to test the influence of a single nucleotide polymorphism on a pharmacokinetic parameter of a drug: analysis of variance (ANOVA) on the empirical Bayes estimates of the individual parameters, likelihood ratio test between models with and without genetic covariate, and Wald tests on the parameters of the model with covariate. Analyses are performed using the FO and FOCE method implemented in the NONMEM software. We compare several approaches for model selection based on tests and global criteria. We illustrate the results with pharmacokinetic data on indinavir from HIV-positive patients included in COPHAR 2-ANRS 111 to study the gene effect prospectively. Only the tests based on the EBE obtain an empirical type I error close to the expected 5%. The approximation made with the FO algorithm results in a significant inflation of the type I error of the LRT and Wald tests. [ABSTRACT FROM AUTHOR]

Abstract: Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1. [Copyright &y& Elsevier]

We extend the methodology for designs evaluation and optimization in nonlinear mixed effects models with an illustration of the decrease of human immunodeficiency virus viral load after antiretroviral treatment initiation described by a bi-exponential model. We first show the relevance of the predicted standard errors (SEs) given by the computation of the population Fisher information matrix using the R function PFIM, in comparison to those computed with the stochastic approximation expectation-maximization algorithm, implemented in the Monolix software. We then highlight the usefulness of the Fedorov-Wynn (FW) algorithm for designs optimization compared to the Simplex algorithm. From the predicted SE of PFIM, we compute the predicted power of the Wald test to detect a treatment effect as well as the number of subjects needed to achieve a given power. Using the FW algorithm, we investigate the influence of the design on the power and show that, for optimized designs with the same total number of samples, the power increases when the number of subjects increases and the number of samples per subject decreases. A simulation study is also performed with the nlme function of R to confirm this result and show the relevance of the predicted powers compared to those observed by simulation. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study which investigated whether interferon regulatory factor 5 (IRF-5) polymorphisms are involved in the genetic predisposition to primary Sjögren's syndrome (SS). The methodology of the study is described, as well as the phenotype, allele and genotype distribution of the IRF5 gene polymosphisms. The authors said the study demonstrated a significant association between primary SS and the IRF5 rs2004640 allele.

Digoxin is a well-known probe for the activity of P-glycoprotein. The objective of this work was to apply different methods for covariate selection in non-linear mixed-effect models to study the relationship between the pharmacokinetic parameters of digoxin and the genotype for two major exons located on the multi-drug-resistance 1 (MDR1) gene coding for P-glycoprotein. Thirty-two healthy volunteers were recruited in three pharmacokinetic drug interaction studies. The data after a single oral administration of digoxin alone were pooled. All subjects were genotyped for the MDR1 C3435T and G2677T/A genotypes. The concentration-time profile of digoxin was established using 12–16 blood samples taken between 15 min and 72 h after administration. We modelled the pharmacokinetics of digoxin using non-linear mixed-effect models. Parameter estimation was performed using the stochastic approximation EM method (SAEM). We used three methods to select the covariate model: selection from a full model using Wald tests, forward inclusion using the log-likelihood ratio test and model selection using the Bayesian Information Criterion. The three covariate inclusion methods led to the same final model. Carriers of two T alleles for the C3435T polymorphism in exon 26 of MDR1 had a lower apparent volume of distribution than carriers of a C allele. The only other covariate effect was a shorter absorption time-lag in women. The apparent volume of distribution of digoxin is lower in TT subjects, probably reflecting differences in bioavailability. Non-linear mixed-effect models can be useful for detecting the influence of covariates on pharmacokinetic parameters. [ABSTRACT FROM AUTHOR]

Purpose: Intrinsic P-glycoprotein (P-gp) expression in the gut limits paclitaxel uptake and, thus, its bioavailability when administered orally. Interleukin-2 has been reported to be a P-gp modulator in vitro and in vivo in mice. In the work described here, the effects of interleukin-2 pretreatment on pharmacokinetics and toxicity of paclitaxel orally administered were investigated. Methods: For the pharmacokinetic study, 96 mice were allocated to two groups receiving either 10 mg/kg of paclitaxel by the oral route alone or 16.5 µg of human recombinant interleukin-2 (rIL2) by the intraperitoneal route twice daily for 3 days and then paclitaxel. Pharmacokinetic profiles were analysed first by the Bailer method, and then using a compartmental approach. For the toxicity study, 90 Swiss mice were allocated to three groups receiving paclitaxel (10 mg/kg orally), rIL2 alone (16.5 µg i.p. twice daily for 3 days, control group), or both treatments. Haematological parameters were measured and the three groups were compared using the Bailer method. A Bonferroni correction was applied to the test. Results: A complex absorption of paclitaxel was revealed. The Bailer method showed that the mean area under the curve (AUC) values over 0-24 h were not significantly different in the two groups, despite a trend to reduced AUC in the pretreated group. The AUC over 0-0.5 h was significantly higher in the group pretreated with rIL2, but represented only a fraction of total exposure. These results were confirmed by the compartmental analysis. The elimination rate constant remained the same across both groups. rIL2 thus increased paclitaxel absorption for the 15 mm following oral intake of the drug but did not enhance the overall exposure. Conclusion: We found that a 3-day pretreatment with rIL2 had some in vivo inhibitory effects on P-gp activity for a short period after oral dosing of paclitaxel. Those results encourage further investigation of the effect of rIL2 on the overall exposure of paclitaxel. On the other hand, it seems that the joint administration of the two drugs did not increase the risk of myelosuppression, which might be worth knowing to treat advanced cancers. [ABSTRACT FROM AUTHOR]

1: Mefloquine is a chiral neurotoxic antimalarial agent showing stereoselective brain uptake in humans and rats. It is a substrate and an inhibitor of the efflux protein P-glycoprotein. 2: We investigated the stereoselective uptake and efflux of mefloquine in mice, and the consequences of the combination with an efflux protein inhibitor, elacridar (GF120918) on its brain transport. 3: Racemic mefloquine (25?mg?kg-1) was administered intraperitoneally with or without elacridar (10?mg?kg-1). Six to seven mice were killed at each of 11 time-points between 30?min and 168?h after administration. Blood and brain concentrations of mefloquine enantiomers were determined using liquid chromatography. 4: A three-compartment model with zero-order absorption from the injection site was found to best represent the pharmacokinetics of both enantiomers in blood and brain. (-)Mefloquine had a lower blood and brain apparent volume of distribution and a lower efflux clearance from the brain, resulting in a larger brain/blood ratio compared to (+)mefloquine. Elacridar did not modify blood concentrations or the elimination rate from blood for either enantiomers. However, cerebral AUCinf of both enantiomers were increased, with a stronger effect on (+)mefloquine. The efflux clearance from the brain decreased for both enantiomers, with a larger decrease for (+)mefloquine. 5: After administration of racemic mefloquine in mice, blood and brain pharmacokinetics are stereoselective, (+)mefloquine being excreted from brain more rapidly than its antipode, showing that mefloquine is a substrate of efflux proteins and that mefloquine enantiomers undergo efflux in a stereoselective manner. Moreover, pretreatment with elacridar reduced the brain efflux clearances with a more pronounced effect on (+)mefloquine.British Journal of Pharmacology (2004) 141, 1214-1222. doi:10.1038/sj.bjp.0705721 [ABSTRACT FROM AUTHOR]

A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I–II AMEERA‐1‐2 dose escalation and expansion cohorts. A semi‐mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure‐driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression‐free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA‐3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I–II data. This provides a good modeling and simulation tool to inform early development decisions. [ABSTRACT FROM AUTHOR]

Joint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre-implemented functions (exponential, Weibull,...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. We used the saemix package, designed to fit nonlinear mixed-effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (SAEM) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed-effects model for the longitudinal submodel, with a single terminal event or a competing risk model. For all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM), some estimates of random effects variances had higher uncertainty and their SE were moderately under-estimated. Finally, type I error was controlled for each joint model. saemix is a flexible open-source package and we adapted it to fit complex parametric joint models that may not be estimated using standard tools. Code and examples to help users get started are freely available on Github. • Extension of the code of saemix package initially designed for nonlinear mixed-effects models to the case of joint models. • Integration of a recently developed stochastic algorithm having interesting properties for standard error estimation. • Good performances for parameter and standard error estimations. • New flexible tool allowing users to fit very specific joint models by defining a personalized likelihood function. [ABSTRACT FROM AUTHOR]

In preclinical investigations, for example, in in vitro, in vivo, and in silico studies, the pharmacokinetic, pharmacodynamic, and toxicological characteristics of a drug are evaluated before advancing to first-in-man trial. Usually, each study is analyzed independently and the human dose range does not leverage the knowledge gained from all studies. Taking into account all preclinical data through inferential procedures can be particularly interesting in obtaining a more precise and reliable starting dose and dose range. Our objective is to propose a Bayesian framework for multi-source data integration, customizable, and tailored to the specific research question. We focused on preclinical results extrapolated to humans, which allowed us to predict the quantities of interest (e.g. maximum tolerated dose, etc.) in humans. We build an approach, divided into four steps, based on a sequential parameter estimation for each study, extrapolation to human, commensurability checking between posterior distributions and final information merging to increase the precision of estimation. The new framework is evaluated via an extensive simulation study, based on a real-life example in oncology. Our approach allows us to better use all the information compared to a standard framework, reducing uncertainty in the predictions and potentially leading to a more efficient dose selection. [ABSTRACT FROM AUTHOR]

Aims: Dolutegravir (DTG) and rilpivirine (RPV) dual therapy is now recommended as a switch option in virologically suppressed HIV patients. Literature suggests that virological failure with dual therapy could possibly relate to subtherapeutic drug concentrations. In this study, we aimed at describing the DTG and RPV trough plasma concentrations (Cmin) and plasma HIV‐1 RNA viral load (VL) during maintenance dual therapy. Methods: We performed a retrospective analysis of DTG and RPV therapeutic drug monitoring in people living with HIV/AIDS (PLWHA) with dual therapy in 9 French centres. DTG and RPV trough plasma concentrations were estimated using a Bayesian approach to predict Cmin. The relationship between the pharmacokinetics of DTG and RPV and VL > 50 copies (cp)/mL was explored using joint nonlinear mixed models. The frequency of subtherapeutic threshold (DTG Cmin below 640 ng/mL and RPV Cmin below 50 ng/mL) were compared between PLWHA presenting VL > 50 cp/mL or not during the study. Results: At baseline, 209 PLWHA were enrolled in the study. At week 48, 19 people living with HIV/AIDS (9.1%) discontinued their treatment and 15 PLWHA (7.1%) exhibited VL > 50 cp/mL. Six PLWHA out of 15 (40.0%) with VL > 50 cp/mL during the follow‐up had at least 1 Cmin below the respective thresholds while only 26/194 patients (13.4%) without virological replication had at least 1 concentration below the threshold (P =.015). Conclusion: A majority of PLWHA receiving DTG/RPV maintenance dual therapy demonstrated VL < 50 cp/mL but virological replication was more frequent in people living with HIV/AIDS with subtherapeutic Cmin. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Standardized visual predictive check versus visual predictive check for model evaluation," by D. Wang and S. Zhang in the January 21, 2011 issue.

Background The intensive use of chlordecone (an organochlorine insecticide) in the French West Indies until 1993 resulted in a long-term soil and water contamination. Chlordecone has known hormonal properties and exposure through contaminated food during critical periods of development (gestation and early infancy) may affect growth. Objectives We aimed to assess the impact of prenatal and postnatal exposure to chlordecone on the growth of children from the TIMOUN mother–child cohort. Methods Chlordecone was determined in cord plasma at birth ( N =222) and in breast milk samples (at 3 months). Dietary chlordecone intake was estimated at 7 and 18 months, with food-frequency questionnaires and food-specific contamination data. Anthropometric measurements were taken at the 3-, 7- and 18-month visits and measurements reported in the infants' health records were noted. Structured Jenss–Bayley growth models were fitted to individual height and weight growth trajectories. The impact of exposure on growth curve parameters was estimated directly with adjusted mixed non-linear models. Weight, height and body mass index (BMI), and instantaneous height and weight growth velocities at specific ages were also analyzed relative to exposure. Results Chlordecone in cord blood was associated with a higher BMI in boys at 3 months, due to greater weight and lower height, and in girls at 8 and 18 months, mostly due to lower height. Postnatal exposure was associated with lower height, weight and BMI at 3, 8 and 18 months, particularly in girls. Conclusion Chlordecone exposure may affect growth trajectories in children aged 0 to 18 months. [ABSTRACT FROM AUTHOR]

Background: Ciprofloxacin is an antibiotic used in osteoarticular infections owing to its very good bone penetration. Very few pharmacokinetic data are available in this population.Objectives: To investigate oral ciprofloxacin population pharmacokinetics in adult patients treated for osteoarticular infections and propose guidance for more effective dosing.Methods: A retrospective population-pharmacokinetic analysis was performed on 92 consecutive hospitalized patients in the orthopaedic department. Ciprofloxacin plasma samples were obtained on one or two occasions during treatment. Plasma concentration was measured using ultra-performance liquid chromatography system coupled with tandem mass spectrometry. Data analysis was performed using a non-linear mixed-effect approach via Monolix 2019R2.Results: A total of 397 plasma samples were obtained with 11.5% and 41.6% of patients being below the therapeutic target for Gram-negative and staphylococcal infections, respectively. Ciprofloxacin pharmacokinetics were best described by a two-compartment model with a first-order absorption. Ciprofloxacin apparent plasma clearances and volumes of distribution were dependent on patients' fat-free mass according to the allometric rule. Elimination clearance was also positively related to renal function through the modification of diet in renal disease equation (MDRD) and rifampicin co-administration. When patients are co-treated with rifampicin, ciprofloxacin dosage should be increased by 50% to 60%.Conclusions: This study showed that free-fat mass was a better size predictor than total body weight for ciprofloxacin clearance and volumes terms. Moreover, both MDRD and rifampicin status were significant predictors of individual ciprofloxacin clearance. Our study suggests that individual adjustment of ciprofloxacin dose in osteoarticular infections with less-susceptible bacteria might be indicated to reach required efficacy targets. [ABSTRACT FROM AUTHOR]

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated.We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation.Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models.Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used. [ABSTRACT FROM AUTHOR]

We modeled the viral dynamics of 13 untreated patients infected with severe acute respiratory syndrome‐coronavirus 2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than two logs, drug efficacy needs to be > 90% if treatment is administered after symptom onset; an efficacy of 60% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 6–87% efficacy. They may help control virus if administered very early, but may not have a major effect in severely ill patients. [ABSTRACT FROM AUTHOR]

The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children. [ABSTRACT FROM AUTHOR]

Background and Objective Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Methods Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. Results The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. Conclusion PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr . [ABSTRACT FROM AUTHOR]

Aims Oxcarbazepine is an antiepileptic drug with an activity mostly due to its monohydroxy derivative metabolite (MHD). A parent-metabolite population pharmacokinetic model in children was developed to evaluate the consistency between the recommended paediatric doses and the reference range for trough concentration (Ctrough) of MHD (3-35 mg l-1). Methods A total of 279 plasma samples were obtained from 31 epileptic children (age 2-12 years) after a single dose of oxcarbazepine. Concentration-time data were analysed with Monolix 4.3.2. The probability to obtain Ctrough between 3-35 mg l-1 was determined by Monte Carlo simulations for doses ranging from 10 to 90 mg kg-1 day-1. Results A parent-metabolite model with two compartments for oxcarbazepine and one compartment for MHD best described the data. Typical values for oxcarbazepine clearance, central and peripheral distribution volume and distribution clearance were 140 l h-1 70 kg-1, 337 l 70 kg-1, 60.7 l and 62.5 l h-1, respectively. Typical values for MHD clearance and distribution volume were 4.11 l h-1 70 kg-1 and 54.8 l 70 kg-1 respectively. Clearances and distribution volumes of oxcarbazepine and MHD were related to body weight via empirical allometric models. Enzyme-inducing antiepileptic drugs (EIAEDs) increased MHD clearance by 29.3%. Fifty-kg children without EIAEDs may need 20-30 mg kg-1 day-1 instead of the recommended target maintenance dose (30-45 mg kg-1 day-1) to obtain Ctrough within the reference range. By contrast, 10-kg children with EIAEDs would need 90 mg kg-1 day-1 instead of the maximum recommended dose of 60 mg kg-1 day-1. Conclusion This population pharmacokinetic model of oxcarbazepine supports current dose recommendations, except for 10-kg children with concomitant EIAEDs and 50-kg children without EIAEDs. [ABSTRACT FROM AUTHOR]

The article discusses the impact of menses on variations in serum hepcidin (SH), in which topics explored include the use of SH to determine the cause of anemia, iron deficiency and iron overload, significant changes to SI and TS during menstrual cycles (MC), and methods and findings from measuring SH and iron during the menstrual cycle.

Simple Summary: Low blood calcium after calving is problematic in dairy cows, particularly for older cows and for Jerseys vs. Holsteins. Feeding acidogenic salts before calving and oral Ca supplementation post-calving help mitigate this. We evaluated: (1) a novel approach to delivering acidogenic salts prepartum applicable to small dairies and (2) the combined effects of prepartum acidogenic salts and postpartum oral Ca supplementation for both Holsteins and Jerseys of varying parity. We found this novel approach to providing acidogenic salts to be effective. We also found preliminary evidence that shortening the time of application of this technology may enhance its beneficial effects. Responses to prepartum acidogenic salts and postpartum oral calcium supplementation were very different for second vs. greater than second parity cows. Finally, we found an increased risk of low blood calcium, particularly in older Jersey cows, when either prepartum urinary calcium excretion was low or colostrum production was high. Postpartum hypocalcemia is a problem in dairy cows. Both the Jersey vs. Holstein breed and increasing parity are known risk factors. Our objectives were: (1) to evaluate a simple approach to provide dietary acidogenic salts suitable for application on small dairies and (2) to evaluate the combined effects of degree of acidification and oral Ca supplementation along with breed and parity group on periparturient Ca status of Holstein and Jersey cows. Cows were moved weekly from the far-off dry pen at 260 days pregnant to the close-up pen, where all cows received the acidogenic diets. The diet was offered as a total mixed ration and CaCl2, and our source of acidogenic salts was top-dressed in liquid form and mixed in by hand. Thirty-six cows were blocked by parity group (parity = 2 vs. parity ≥ 3) and breed (Holstein vs. Jersey) and assigned to one of two treatments (no intervention or postpartum oral Ca bolus supplementation) in an alternating fashion, based on expected date of parturition. Urinary acidification appeared complete within 3–4 days. Increased urinary Ca excretion was >93% of maximum from 7–21 days before falling to <5% of maximum by 28 days. Serum Ca concentrations 12–24 h postpartum were lower for Jerseys vs. Holsteins and for parity ≥ 3 vs. parity = 2 cows. Serum Ca over 6–48 h postpartum decreased and increased, respectively, with oral Ca supplementation for parity = 2 and parity ≥ 3 cows. Decreased prepartum urinary Ca excretion and increased colostrum yield appear to be independent risk factors of hypocalcemia for parity ≥ 3 Jerseys. [ABSTRACT FROM AUTHOR]

Objective: Aflibercept (Zaltrap) is a novel antiangiogenic agent that binds to vascular endothelial growth factor (VEGF) and inhibits VEGF-dependent tumor growth. We aimed to characterize the population pharmacokinetics (PK) of free and bound aflibercept in patients with solid tumors to examine the influence of covariates on their PK and to evaluate the proposed dosing regimens by simulation. Methods: Data from 9 clinical trials with 1,506 cancer patients receiving aflibercept (2-9 mg/kg every 2 or 3 weeks; 1 h IV infusion) as a monotherapy or in combination with various chemotherapies were included. Free and bound aflibercept concentrations were analyzed using a non-linear mixed-effects modeling approach with MONOLIX 4.1.2. Results: An approximation of a target-mediated drug disposition model with irreversible binding of free aflibercept to VEGF adequately described the PK of free and bound aflibercept. The typical estimated clearances for free (CL) and bound aflibercept (CL) were 0.88 and 0.19 L/day, respectively. The volumes of distribution for free ( V) and bound ( V) aflibercept were similar (~4 L). CL and V increased with body weight and were lower in women. Patients with low albumin (ALB) or high alkaline phosphatase (ALK) had faster CL compared to a typical patient. Pancreatic cancer may be associated with changes in binding of aflibercept to VEGF. Simulations of different dosing regimens showed that adequate saturation of circulating VEGF was achieved with a dose of 4 mg/kg every 2 weeks. Conclusions: Aflibercept kinetics was most affected by gender, body weight, ALB, ALK and pancreatic cancer. Simulations supported the rationale for the recommended dose of 4 mg/kg every 2 weeks for aflibercept. [ABSTRACT FROM AUTHOR]

A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of parameters in mixed-effects models. It is usually applied to obtain more robust estimates of the parameters and more realistic confidence intervals (CIs). Alternative bootstrap methods, such as residual bootstrap and parametric bootstrap that resample both random effects and residuals, have been proposed to better take into account the hierarchical structure of multi-level and longitudinal data. However, few studies have been performed to compare these different approaches. In this study, we used simulation to evaluate bootstrap methods proposed for linear mixed-effect models. We also compared the results obtained by maximum likelihood (ML) and restricted maximum likelihood (REML). Our simulation studies evidenced the good performance of the case bootstrap as well as the bootstraps of both random effects and residuals. On the other hand, the bootstrap methods that resample only the residuals and the bootstraps combining case and residuals performed poorly. REML and ML provided similar bootstrap estimates of uncertainty, but there was slightly more bias and poorer coverage rate for variance parameters with ML in the sparse design. We applied the proposed methods to a real dataset from a study investigating the natural evolution of Parkinson's disease and were able to confirm that the methods provide plausible estimates of uncertainty. Given that most real-life datasets tend to exhibit heterogeneity in sampling schedules, the residual bootstraps would be expected to perform better than the case bootstrap. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Anti-angiogenic drugs have been developed as an effective therapeutic strategy for inhibiting tumour growth. However, their pharmacokinetics (PK) and their ligand inhibition properties have not been well characterized. The binding to a circulating target, such as vascular endothelial growth factor (VEGF), makes the PK of these drugs more complex. WHAT THIS STUDY ADDS • The underlying mechanism of disposition of aflibercept, where a saturable and high affinity binding of aflibercept to VEGF was adequately characterized by the Michaelis-Menten approximation of a target-mediated drug distribution model. To our knowledge, this is the first published mechanism-based population PK model for an anti-VEGF drug. AIM Aflibercept (VEGF-Trap), a novel anti-angiogenic agent that binds to VEGF, has been investigated for the treatment of cancer. The aim of this study was to develop a mechanism-based pharmacokinetic (PK) model for aflibercept to characterize its binding to VEGF and its PK properties in healthy subjects. METHODS Data from two phase I clinical studies with aflibercept administered as a single intravenous infusion were included in the analysis. Free and bound aflibercept concentration−time data were analysed using a nonlinear mixed-effects modelling approach with MONOLIX 3.1. RESULTS The best structural model involved two compartments for free aflibercept and one for bound aflibercept, with a Michaelis-Menten type binding of free aflibercept to VEGF from the peripheral compartment. The typical estimated clearances for free and bound aflibercept were 0.88 l day−1 and 0.14 l day−1, respectively. The central volume of distribution of free aflibercept was 4.94 l. The maximum binding capacity was 0.99 mg day−1 and the concentration of aflibercept corresponding to half of maximum binding capacity was 2.91 µg ml−1. Interindividual variability of model parameters was moderate, ranging from 13.6% ( Vmax) to 49.8% ( Q). CONCLUSION The present PK model for aflibercept adequately characterizes the underlying mechanism of disposition of aflibercept and its nonlinear binding to VEGF. [ABSTRACT FROM AUTHOR]

Fluoroquinolones efficacy depend on both the drug exposure and the level of drug resistance of the bacteria responsible for the infection. Specifically for the Staphylococcus species, which is the microorganism mainly involved in osteoarticular infections (OAI), in-vitro data reported that an AUC/MIC ratio above 115 h maximizes drug efficacy. However, data on OAI patients are lacking and a simple approach to access AUCs is still a clinical issue. We conducted a prospective, single-center study in 30 OAI patients hospitalized in the Rennes University Hospital to model ofloxacin pharmacokinetics and to define a limited sampling strategy (LSS) suitable for ofloxacin and levofloxacin treatments. Modeling was conducted with the Monolix software. The final model was externally validated using levofloxacin data. Monte-Carlo simulations were used to evaluate the probability of target attainment (PTA) of different dosing regimens. Two hundred and ninety-seven (297) ofloxacin concentrations were available for the pharmacokinetic modeling. Ofloxacin pharmacokinetics was best described using a bicompartmental model with a first order elimination, and a transit compartment model absorption. CKD-EPI and sex explained half of ofloxacin pharmacokinetic variability. For LSS, the 0, 1 h and 3 h sampling scheme resulted in the best approach both for BID and TID dosages (R2 adjusted = 91.1% and 95.0%, outliers = 4.8% and 5.0%, respectively). PTA allows choosing the best drug and dosage according to various hypotheses. A simple 3-sample protocol (pre-dose, 1 h after intake and 3 h after intake) to estimate ofloxacin and levofloxacin AUC allows optimal drug dosage for the treatment of osteoarticular infections. [ABSTRACT FROM AUTHOR]

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development. [ABSTRACT FROM AUTHOR]

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods. [ABSTRACT FROM AUTHOR]