Concordance between criteria for covariate model building

Covariate modeling in pharmacometrics: General points for consideration

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

An introduction to the full random effects model

The full random‐effects model (FREM) is a method for determining covariate effects in mixed‐effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed‐effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate‐parameter relation. Four relations (linear, log‐linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed‐effects counterparts. Comparisons between FREM and mathematically equivalent full fixed‐effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non‐normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.

Differences in the Pharmacokinetics of Gentamicin between Oncology and Nononcology Pediatric Patients

Dosing gentamicin in pediatric patients can be difficult due to its narrow therapeutic index. A significantly higher percentage of fat mass has been observed in children receiving oncology treatment than in those who are not. Differences in the pharmacokinetics of gentamicin between oncology and nononcology pediatric patients and individual dosage requirements were evaluated in this study, using normal fat mass (NFM) as a body size descriptor. Data from 423 oncology and 115 nononcology patients were analyzed. Differences in drug disposition were observed between the oncology and nononcology patients, with oncology patients having a 15% lower central volume of distribution and 32% lower intercompartmental clearance. Simulations based on the population pharmacokinetic model demonstrated low exposure target attainment in all individuals at the current clinical recommended starting dose of 7.5 mg/kg of body weight once daily, with 57.4% of oncology and 35.7% of nononcology subjects achieving a peak concentration (C _max) of ≥25 mg/liter and 64.3% of oncology and 65.6% of nononcology subjects achieving an area under the concentration-time curve at 24 h postdose (AUC₂₄) of ≥70 mg · h/liter after the first dose. Based on simulations, the extent of the impact of differences in drug disposition between the two cohorts appeared to be dependent on the exposure target under examination. Greater differences in achieving a C _max target of >25 mg/liter than an AUC₂₄ target of ≥70 mg · h/liter between the cohorts was observed. Further investigation into whether differences in the pharmacokinetics of gentamicin between oncology and nononcology patients are a consequence of changes in body composition is required.

A Population Pharmacokinetic Model of Gentamicin in Pediatric Oncology Patients To Facilitate Personalized Dosing

To ensure the safe and effective dosing of gentamicin in children, therapeutic drug monitoring (TDM) is recommended. TDM utilizing Bayesian forecasting software is recommended but is unavailable, as no population model that describes the pharmacokinetics of gentamicin in pediatric oncology patients exists. This study aimed to develop and externally evaluate a population pharmacokinetic model of gentamicin to support personalized dosing in pediatric oncology patients. A nonlinear mixed-effect population pharmacokinetic model was developed from retrospective data. Data were collected from 423 patients for model building and a further 52 patients for external evaluation. A two-compartment model with first-order elimination best described the gentamicin disposition. The final model included renal function (described by fat-free mass and postmenstrual age) and the serum creatinine concentration as covariates influencing gentamicin clearance (CL). Final parameter estimates were as follow CL, 5.77 liters/h/70 kg; central volume of distribution, 21.6 liters/70 kg; peripheral volume of distribution, 13.8 liters/70 kg; and intercompartmental clearance, 0.62 liter/h/70 kg. External evaluation suggested that current models developed in other pediatric cohorts may not be suitable for use in pediatric oncology patients, as they showed a tendency to overpredict the observations in this population. The final model developed in this study displayed good predictive performance during external evaluation (root mean square error, 46.0%; mean relative prediction error, -3.40%) and may therefore be useful for the personalization of gentamicin dosing in this cohort. Further investigations should focus on evaluating the clinical application of this model.

Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients

Several population pharmacokinetic models describe the dose-exposure relationship of tobramycin in pediatric patients. Before the implementation of these models in clinical practice for dosage adjustment, their predictive performance should be externally evaluated. This study tested the predictive performance of all published population pharmacokinetic models of tobramycin developed for pediatric patients with an independent patient cohort. A literature search was conducted to identify suitable models for testing. Demographic and pharmacokinetic data were collected retrospectively from the medical records of pediatric patients who had received intravenous tobramycin. Tobramycin exposure was predicted from each model. Predictive performance was assessed by visual comparison of predictions to observations, by calculation of bias and imprecision, and through the use of simulation-based diagnostics. Eight population pharmacokinetic models were identified. A total of 269 concentration-time points from 41 pediatric patients with cystic fibrosis were collected for external evaluation. Three models consistently performed best in all evaluations and had mean errors ranging from -0.4 to 1.8 mg/liter, relative mean errors ranging from 4.9 to 29.4%, and root mean square errors ranging from 47.8 to 66.9%. Simulation-based diagnostics supported these findings. Models that allowed a two-compartment disposition generally had better predictive performance than those that used a one-compartment disposition model. Several published models of the pharmacokinetics of tobramycin showed reasonable low levels of bias, although all models seemed to have some problems with imprecision. This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions.