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.

Development of a dosing-adjustment tool for fluoroquinolones in osteoarticular infections: The Fluo-pop study.

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.

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.

Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004.

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.