Pharmacokinetics and safety of daptomycin administered subcutaneously in healthy volunteers: a single-blinded randomized crossover trial.

BACKGROUND: Daptomycin stands as a key IV antibiotic in treating MRSA infections. However, patients facing challenges with difficult venous access require alternative administration routes. This study aimed to evaluate the pharmacokinetic (PK) profile and safety of subcutaneous (SC) daptomycin. PATIENTS AND METHODS: In a two-period, two-treatment, single-blind crossover Phase I trial (ClinicalTrials.gov NCT04434300), participants with no medical history received daptomycin (10 mg/kg) both IV and SC in a random order, with a minimum 2 week washout period together with matched placebo (NaCl 0.9%). Blood samples collected over 24 h facilitated PK comparison. Monte Carlo simulations assessed the PTA for various dosing regimens. Adverse events were graded according to Common Terminology Criteria for Adverse Events(CTCAE) v5.0. RESULTS: Twelve participants (aged 30.9 ±â€Š24.4 years; 9 male,75%) were included. SC daptomycin exhibited delayed (median Tmax 0.5 h for IV versus 4 h for SC) and lower peak concentration than IV (Cmax = 132.2 ±â€Š16.0 µg/mL for IV versus 57.3 ±â€Š8.6 µg/mL for SC; P < 0.001). SC AUC0-24 (937.3 ±â€Š102.5 µg·h/mL) was significantly lower (P = 0.005) than IV AUC0-24 (1056.3 ±â€Š123.5 µg·h/mL) but was deemed bioequivalent. PTA demonstrated target AUC0-24 attainment for 100% of simulated individuals, for both 8 and 10 mg/kg/24 h SC regimens. Adverse events (AEs) related to SC daptomycin were more frequent than for SC placebo (25 versus 13, P = 0.016). No serious AEs were reported. CONCLUSIONS: Single-dose SC daptomycin infusion proved to be safe, exhibiting a bioequivalent AUC0-24 compared with the IV route. The SC route emerges as a potential and effective alternative when IV administration is not possible.

Therapeutic drug monitoring and virological response at week 48 in a cohort of HIV-1-infected patients switching to dolutegravir/rilpivirine dual maintenance therapy (ANRS-MIE-BIRIDER study).

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.

Impact of model misspecification on model-based tests in PK studies with parallel design: real case and simulation studies.

This article evaluates the performance of pharmacokinetic (PK) equivalence testing between two formulations of a drug through the Two-One Sided Tests (TOST) by a model-based approach (MB-TOST), as an alternative to the classical non-compartmental approach (NCA-TOST), for a sparse design with a few time points per subject. We focused on the impact of model misspecification and the relevance of model selection for the reference data. We first analysed PK data from phase I studies of gantenerumab, a monoclonal antibody for the treatment of Alzheimer's disease. Using the original rich sample data, we compared MB-TOST to NCA-TOST for validation. Then, the analysis was repeated on a sparse subset of the original data with MB-TOST. This analysis inspired a simulation study with rich and sparse designs. With rich designs, we compared NCA-TOST and MB-TOST in terms of type I error and study power. With both designs, we explored the impact of misspecifying the model on the performance of MB-TOST and adding a model selection step. Using the observed data, the results of both approaches were in general concordance. MB-TOST results were robust with sparse designs when the underlying PK structural model was correctly specified. Using the simulated data with a rich design, the type I error of NCA-TOST was close to the nominal level. When using the simulated model, the type I error of MB-TOST was controlled on rich and sparse designs, but using a misspecified model led to inflated type I errors. Adding a model selection step on the reference data reduced the inflation. MB-TOST appears as a robust alternative to NCA-TOST, provided that the PK model is correctly specified and the test drug has the same PK structural model as the reference drug.

Ciprofloxacin population pharmacokinetics during long-term treatment of osteoarticular infections.

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.

Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models.

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.

Population pharmacokinetics of oxcarbazepine and its monohydroxy derivative in epileptic children.

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.

Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations.

The aim of phase I clinical trials is to obtain reliable information on safety, tolerability, pharmacokinetics (PK), and mechanism of action of drugs with the objective of determining the maximum tolerated dose (MTD). In most phase I studies, dose-finding and PK analysis are done separately and no attempt is made to combine them during dose allocation. In cases such as rare diseases, paediatrics, and studies in a biomarker-defined subgroup of a defined population, the available population size will limit the number of possible clinical trials that can be conducted. Combining dose-finding and PK analyses to allow better estimation of the dose-toxicity curve should then be considered. In this work, we propose, study, and compare methods to incorporate PK measures in the dose allocation process during a phase I clinical trial. These methods do this in different ways, including using PK observations as a covariate, as the dependent variable or in a hierarchical model. We conducted a large simulation study that showed that adding PK measurements as a covariate only does not improve the efficiency of dose-finding trials either in terms of the number of observed dose limiting toxicities or the probability of correct dose selection. However, incorporating PK measures does allow better estimation of the dose-toxicity curve while maintaining the performance in terms of MTD selection compared to dose-finding designs that do not incorporate PK information. In conclusion, using PK information in the dose allocation process enriches the knowledge of the dose-toxicity relationship, facilitating better dose recommendation for subsequent trials.

Designing a Pediatric Study for an Antimalarial Drug by Using Information from Adults.

The objectives of this study were to design a pharmacokinetic (PK) study by using information about adults and evaluate the robustness of the recommended design through a case study of mefloquine. PK data about adults and children were available from two different randomized studies of the treatment of malaria with the same artesunate-mefloquine combination regimen. A recommended design for pediatric studies of mefloquine was optimized on the basis of an extrapolated model built from adult data through the following approach. (i) An adult PK model was built, and parameters were estimated by using the stochastic approximation expectation-maximization algorithm. (ii) Pediatric PK parameters were then obtained by adding allometry and maturation to the adult model. (iii) A D-optimal design for children was obtained with PFIM by assuming the extrapolated design. Finally, the robustness of the recommended design was evaluated in terms of the relative bias and relative standard errors (RSE) of the parameters in a simulation study with four different models and was compared to the empirical design used for the pediatric study. Combining PK modeling, extrapolation, and design optimization led to a design for children with five sampling times. PK parameters were well estimated by this design with few RSE. Although the extrapolated model did not predict the observed mefloquine concentrations in children very accurately, it allowed precise and unbiased estimates across various model assumptions, contrary to the empirical design. Using information from adult studies combined with allometry and maturation can help provide robust designs for pediatric studies.

Perinatal exposure to chlordecone and infant growth.

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.

Evaluation of bootstrap methods for estimating uncertainty of parameters in nonlinear mixed-effects models: a simulation study in population pharmacokinetics.

Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, including multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and parametric bootstrap) by a simulation study and compared them to that of an asymptotic method (Asym) in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the Asym, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when applying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the Asym and the parametric bootstrap performed well and better than case bootstrap even with stratification.

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data.

BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Uncertainty Computation at Finite Distance in Nonlinear Mixed Effects Models-a New Method Based on Metropolis-Hastings Algorithm.

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration.

Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies.

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.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS).

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.

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.

Longitudinal Analysis of Natural History Progression of Rare and Ultra-Rare Cerebellar Ataxias Using Item Response Theory.

Degenerative cerebellar ataxias comprise a heterogeneous group of rare and ultra-rare genetic diseases. While disease-modifying treatments are now on the horizon for many ataxias, robust trial designs and analysis methods are lacking. To better inform trial designs, we applied item response theory (IRT) modeling to evaluate the natural history progression of several ataxias, assessed with the widely used scale for assessment and rating of ataxia (SARA). A longitudinal IRT model was built utilizing real-world data from the large autosomal recessive cerebellar ataxia (ARCA) registry. Disease progression was evaluated for the overall cohort as well as for the 10 most common ARCA genotypes. Sample sizes were calculated for simulated trials with autosomal recessive spastic ataxia Charlevoix-Saguenay (ARSACS) and polymerase gamma (POLG) ataxia, as showcased, across multiple design and analysis scenarios. Longitudinal IRT models were able to describe the changes in the latent variable underlying SARA as a function of time since ataxia onset for both the overall ARCA cohort and the common genotypes. The typical progression rates varied across genotypes between relatively high in POLG (~ 0.98 SARA points/year at SARA = 20) and very low in COQ8A ataxia (~ 0.003 SARA points/year at SARA = 20). Smaller trial sizes were required in case of faster progression, longer trials (~ 75-90% less with 5 years vs. 2 years), and larger drug effects (~ 70-80% less with 100% vs. 50% inhibition). Simulating under the developed IRT model, the longitudinal IRT model had the highest power, with a well-controlled type I error, compared to total score models or end-of-treatment analyses. The established longitudinal IRT framework allows efficient utilization of natural history data and ultimately facilitates the design and analysis of treatment trials in rare and ultra-rare genetic ataxias.

A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.

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.

Multivariate Exact Discrepancy: A New Tool for PK/PD Model Evaluation.

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.

Some alternatives to asymptotic tests for the analysis of pharmacogenetic data using nonlinear mixed effects models.

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.

Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model.

Data below the quantification limit (BQL data) are a common challenge in data analyses using nonlinear mixed effect models (NLMEM). In the estimation step, these data can be adequately handled by several reliable methods. However, they are usually omitted or imputed at an arbitrary value in most evaluation graphs and/or methods. This can cause trends to appear in diagnostic graphs, therefore, confuse model selection and evaluation. We extended in this paper two metrics for evaluating NLMEM, prediction discrepancies (pd) and normalised prediction distribution errors (npde), to handle BQL data. For a BQL observation, the pd is randomly sampled in a uniform distribution over the interval from 0 to the probability of being BQL predicted by the model, estimated using Monte Carlo (MC) simulation. To compute npde in presence of BQL observations, we proposed to impute BQL values in both validation dataset and MC samples using their computed pd and the inverse of the distribution function. The imputed dataset and MC samples contain original data and imputed values for BQL data. These data are then decorrelated using the mean and variance-covariance matrix to compute npde. We applied these metrics on a model built to describe viral load obtained from 35 patients in the COPHAR 3-ANRS 134 clinical trial testing a continued antiretroviral therapy. We also conducted a simulation study inspired from the real model. The proposed metrics show better behaviours than naive approaches that discard BQL data in evaluation, especially when large amounts of BQL data are present.