Vancomycin: An analysis and evaluation of eight population pharmacokinetic models for clinical application in general adult population.

INTRODUCTION: Based on the recent guidelines for vancomycin therapeutic drug monitoring (TDM), the area under the curve to minimum inhibitory concentration ratio was to be employed combined with the usage of population pharmacokinetic (popPK) model for dosing adaptation. Yet, deploying these models in a clinical setting requires an external evaluation of their performance. OBJECTIVES: This study aimed to evaluate existing vancomycin popPK models from the literature for the use in TDM within the general patient population in a clinical setting. METHODS: The models under external evaluation were chosen based on a review of literature covering vancomycin popPK models developed in general adult populations. Patients' data were collected from Charles-Le Moyne Hospital (CLMH). The external evaluation was performed with NONMEM® (v7.5). Additional analyses such as evaluating the impact of number of samples on external evaluation, Bayesian forecasting, and a priori dosing regimen simulations were performed on the best performing model. RESULTS: Eight popPK models were evaluated with an independent dataset that included 40 patients and 252 samples. The model developed by Goti and colleagues demonstrated superior performance in diagnostic plots and population predictive performance, with bias and inaccuracy values of 0.251% and 22.7%, respectively, and for individual predictive performance, bias and inaccuracy were -4.90% and 12.1%, respectively. When limiting the independent dataset to one or two samples per patient, the Goti model exhibited inadequate predictive performance for inaccuracy, with values exceeding 30%. Moreover, the Goti model is suitable for Bayesian forecasting with at least two samples as prior for the prediction of the next trough concentration. Furthermore, the vancomycin dosing regimen that would maximize therapeutic targets of area under the curve to minimum inhibitory concentration ratio (AUC24/MIC) and trough concentrations (Ctrough) for the Goti model was 20 mg/kg/dose twice daily. CONCLUSION: Considering the superior predictive performance and potential for Bayesian forecasting in the Goti model, future research aims to test its applicability in clinical settings at CLMH, both in a priori and a posteriori scenario.

Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models.

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

Tobramycin a Priori Dosing Regimens Based on PopPK Model Simulations in Critically Ill Patients: Are They Transferable?

BACKGROUND: In recent years, multiple population pharmacokinetic models have been developed for drugs such as tobramycin that need therapeutic drug monitoring. Some of these models have been used to develop a priori dosing regimens for their respective populations. However, these dosing regimens may not apply to other populations. Therefore, this study aimed to evaluate tobramycin population pharmacokinetic models in critically ill patients and establish an adequate dosing regimen. METHODS: Evaluated models were identified from a literature review of aminoglycoside population pharmacokinetic models in critically ill patients. After retrospective data collection in 2 Quebec hospitals, external evaluation and model re-estimation were performed with NONMEM (v7.5) to assess imprecision and bias values. Dosing regimens were simulated and compared between the best-performing model and its re-estimated counterparts. RESULTS: None of the 3 evaluated models showed acceptable imprecision or bias values in the data sets of the 19 patients. Similar percentages of target attainment were obtained for the original and re-estimated models after the dosing regimen simulations. CONCLUSION: Although the predictive performance evaluation criteria were inadequate, the original and re-estimated models yielded similar results. This raises the question of what a priori bias and imprecision thresholds should be defined as acceptable for the external evaluation of models to be applied in clinical practice. Studies evaluating the impact of these thresholds are needed.

Model Re-Estimation: An Alternative for Poor Predictive Performance during External Evaluations? Example of Gentamicin in Critically Ill Patients.

BACKGROUND: An external evaluation is crucial before clinical applications; however, only a few gentamicin population pharmacokinetic (PopPK) models for critically ill patients included it in the model development. In this study, we aimed to evaluate gentamicin PopPK models developed for critically ill patients. METHODS: The evaluated models were selected following a literature review on aminoglycoside PopPK models for critically ill patients. The data of patients were retrospectively collected from two Quebec hospitals, the external evaluation and model re-estimation were performed with NONMEM® (v7.5) and the population bias and imprecisions were estimated. Dosing regimens were simulated using the best performing model. RESULTS: From the datasets of 39 and 48 patients from the two Quebec hospitals, none of the evaluated models presented acceptable values for bias and imprecision. Following model re-estimations, all models showed an acceptable predictive performance. An a priori dosing nomogram was developed with the best performing re-estimated model and was consistent based on recommended dosing regimens. CONCLUSION: Due to the poor predictive performance during the external evaluations, the latter must be prioritized during model development. Model re-estimation may be an alternative to developing a new model, especially when most known models display similar covariates.

Aminoglycosides' dosing and monitoring practices in critically ill patients in Quebec hospitals.

Considering the aminoglycosides' characteristics in terms of efficacy and toxicity, multiple dosing recommendations and nomograms have been suggested over several decades. The objective is to describe the dosing and monitoring practices of amikacin, gentamicin, and tobramycin in critically ill patients across health care institutions in the province of Quebec.This survey was developed with multiple-choices and short answers and targeted the lead pharmacist responsible of antimicrobial stewardship in each health care institution.Gentamicin and tobramycin dosing regimens were in-line with guidelines from different countries. Amikacin was not commonly used in Quebec. Therapeutic targets were generally consistent with the literature.Dosing adaptation were mostly done based on clinician judgment or with homemade software. Given the variability seen across practices in Quebec institutions, standardization and optimization of aminoglycosides therapeutic drug monitoring may be considered.

Aminoglycosides in the Intensive Care Unit: What Is New in Population PK Modeling?

BACKGROUND: Although aminoglycosides are often used as treatment for Gram-negative infections, optimal dosing regimens remain unclear, especially in ICU patients. This is due to a large between- and within-subject variability in the aminoglycoside pharmacokinetics in this population. OBJECTIVE: This review provides comprehensive data on the pharmacokinetics of aminoglycosides in patients hospitalized in the ICU by summarizing all published PopPK models in ICU patients for amikacin, gentamicin, and tobramycin. The objective was to determine the presence of a consensus on the structural model used, significant covariates included, and therapeutic targets considered during dosing regimen simulations. METHOD: A literature search was conducted in the Medline/PubMed database, using the terms: 'amikacin', 'gentamicin', 'tobramycin', 'pharmacokinetic(s)', 'nonlinear mixed effect', 'population', 'intensive care', and 'critically ill'. RESULTS: Nineteen articles were retained where amikacin, gentamicin, and tobramycin pharmacokinetics were described in six, 11, and five models, respectively. A two-compartment model was used to describe amikacin and tobramycin pharmacokinetics, whereas a one-compartment model majorly described gentamicin pharmacokinetics. The most recurrent significant covariates were renal clearance and bodyweight. Across all aminoglycosides, mean interindividual variability in clearance and volume of distribution were 41.6% and 22.0%, respectively. A common consensus for an optimal dosing regimen for each aminoglycoside was not reached. CONCLUSIONS: This review showed models developed for amikacin, from 2015 until now, and for gentamicin and tobramycin from the past decades. Despite the growing challenges of external evaluation, the latter should be more considered during model development. Further research including new covariates, additional simulated dosing regimens, and external validation should be considered to better understand aminoglycoside pharmacokinetics in ICU patients.