Population Pharmacokinetics with Monte Carlo Simulations of Gentamicin in a Population of Severely Ill Adult Patients from Sub-Saharan Africa

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).

Population pharmacokinetics of antibacterial agents in the older population: a literature review

INTRODUCTION

Older individuals face an elevated risk of developing bacterial infections. The optimal use of antibacterial agents in this population is challenging because of age-related physiological alterations, changes in pharmacokinetics (PK) and pharmacodynamics (PD), and the presence of multiple underlying diseases. Therefore, population pharmacokinetics (PPK) studies are of great importance for optimizing individual treatments and prompt identification of potential risk factors.

AREA COVERED

Our search involved keywords such as 'elderly,' 'old people,' and 'geriatric,' combined with 'population pharmacokinetics' and 'antibacterial agents.' This comprehensive search yielded 11 categories encompassing 28 antibacterial drugs, including vancomycin, ceftriaxone, meropenem, and linezolid. Out of 127 studies identified, 26 (20.5%) were associated with vancomycin, 14 (11%) with meropenem, and 14 (11%) with piperacillin. Other antibacterial agents were administered less frequently.

EXPERT OPINION

PPK studies are invaluable for elucidating the characteristics and relevant factors affecting the PK of antibacterial agents in the older population. Further research is warranted to develop and validate PPK models for antibacterial agents in this vulnerable population.

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.

Population Pharmacokinetics and Dosing Optimization of Gentamicin in Critically Ill Patients Undergoing Continuous Renal Replacement Therapy

Purpose

Appropriate gentamicin dosing in continuous renal replacement therapy (CRRT) patients remains undefined. This study aimed to develop a population pharmacokinetic (PK) model of gentamicin in CRRT patients and to infer the optimal dosing regimen for gentamicin.

Methods

Fourteen CRRT patients dosed with gentamicin were included to establish a population PK model to characterize the variabilities and influential covariates of gentamicin. The pharmacokinetic/pharmacodynamic (PK/PD) target attainment and risk of toxicity for different combinations of gentamicin regimens (3–7 mg/kg q24h) and CRRT effluent doses (30–50 mL/h/kg) were evaluated by Monte Carlo simulation. The probability of target attainment (PTA) was determined for the PK/PD indices of the ratio of drug peak concentration/minimum inhibitory concentration (C_max/MIC > 10) and the ratio of area under the drug concentration–time curve/MIC over 24 h (AUC_0-24h/MIC > 100), and the risk of toxicity was estimated by drug trough concentration thresholds (1 and 2 mg/L).

Results

A one-compartment model adequately described the PK characteristics of gentamicin. Covariates including body weight, age, gender, and CRRT modality did not influence the PK parameters of gentamicin based on our dataset. All studied gentamicin regimens failed to achieve satisfactory PTAs for pathogens with an MIC ≥2 mg/L. A good balance of PK/PD target attainment and risk of toxicity (>2 mg/L) was achieved under 7 mg/kg gentamicin q24h and 40 mL/kg/h CRRT dose for an MIC ≤1 mg/L. CRRT dose intensity had a significant impact on the target attainment of AUC_0-24h/MIC >100 and risk of toxicity.

Conclusion

A combination of 7 mg/kg gentamicin q24h and 40 mL/kg/h CRRT dose might be considered as a starting treatment option for CRRT patients, and drug monitoring is required to manage toxicity.

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.