Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools’ clinical and cost benefits.

Children show important developmental and maturational changes, which may contribute greatly to pharmacokinetic (PK) variability observed in pediatric patients. These PK alterations are further enhanced by disease-related, non-maturational factors. Specific to the intensive care setting, such factors include critical illness, inflammatory status, augmented renal clearance (ARC), as well as therapeutic interventions (e.g., extracorporeal organ support systems or whole-body hypothermia [WBH]). This narrative review illustrates the relevance of both maturational and non-maturational changes in absorption, distribution, metabolism, and excretion (ADME) applied to antibiotics. It hereby provides a focused assessment of the available literature on the impact of critical illness—in general, and in specific subpopulations (ARC, extracorporeal organ support systems, WBH)—on PK and potential underexposure in children and neonates. Overall, literature discussing antibiotic PK alterations in pediatric intensive care is scarce. Most studies describe antibiotics commonly monitored in clinical practice such as vancomycin and aminoglycosides. Because of the large PK variability, therapeutic drug monitoring, further extended to other antibiotics, and integration of model-informed precision dosing in clinical practice are suggested to optimise antibiotic dose and exposure in each newborn, infant, or child during intensive care.

Existing evidence is inconclusive whether meropenem dosing should be adjusted in patients receiving extracorporeal membrane oxygenation (ECMO). Therefore, the aim of this observational matched cohort study was to evaluate the effect of ECMO on pharmacokinetic (PK) variability and target attainment (TA) of meropenem. Patients admitted to the intensive care unit (ICU) simultaneously treated with meropenem and ECMO were eligible. Patients were matched 1:1, based on renal function and body weight, with non-ECMO ICU patients. Meropenem blood sampling was performed over one or two dosing intervals. Population PK modelling was performed using NONMEM7.5. TA was defined as free meropenem concentrations >2 or 8 mg/L (i.e., 1 or 4× minimal inhibitory concentration, respectively) throughout the whole dosing interval. In total, 25 patients were included, contributing 27 dosing intervals. The overall TA was 56% and 26% for the 2 mg/L and 8 mg/L target, respectively. Population PK modelling identified estimated glomerular filtration rate according to the Chronic Kidney Disease Epidemiology equation and body weight, but not ECMO, as significant predictors. In conclusion, TA of meropenem was confirmed to be poor under standard dosing in critically ill patients but was not found to be influenced by ECMO. Future studies should focus on applying dose optimisation strategies for meropenem based on renal function, regardless of ECMO.

Background: Fluconazole is one of the oldest antifungal drugs. Previous studies have raised concerns considering variability in exposure and inadequate target attainment in critically ill patients. The current study aims to define variability and target attainment for fluconazole exposure in a large group of critically ill patients. Methods: In this pharmacokinetic study, daily plasma trough samples and, if possible, 24 h urine samples were collected to determine fluconazole concentration. A minimum target trough concentration of 10–15 mg/L was selected, corresponding to a free area under the concentration–time curve above the minimum inhibitory concentration (fAUC/MIC) of at least 100 for an MIC of 4 mg/L. Covariates that significantly influenced fluconazole exposure were identified. Results: In total, 288 plasma samples from 43 patients, with a median age of 66 years, were included. The median fluconazole trough concentration was 22.9 mg/L. A notable component of the measured concentrations was below the target trough concentrations (13% <10 mg/L and 27% <15 mg/L). The intra- and intersubject variability were 28.3% and 50.5%, respectively. The main covariates determining fluconazole exposure were the administered dose (mg/kg), augmented renal clearance, and renal replacement therapy. Conclusions: Fluconazole trough concentrations are variable in critically ill patients and a considerable number of these concentrations was below the predefined target trough concentrations.

Purpose: Critically ill patients with preserved or increased renal function have been shown to be at risk of underexposure to meropenem. Although many meropenem population pharmacokinetic (PK) models have been published, there is no large prospective population PK study with rich sampling focusing on patients most at risk of suboptimal pharmacokinetic/pharmacodynamic (PK/PD) target attainment. Therefore, the aim of the present study was to evaluate PK/PD target attainment and to perform a thorough covariate screening using population PK modelling of meropenem in septic patients with preserved or increased renal function. Patients and Methods: A single-centre prospective observational PK study was performed in the intensive care unit (ICU) of the University Hospitals Leuven. Patients with severe sepsis or septic shock and treated with meropenem in the ICU were screened for inclusion. Patients were excluded if they received renal replacement therapy or had an estimated glomerular filtration rate according to the Chronic Kidney Disease Epidemiology collaboration equation <70 mL/min/1.73m(2) on the day of PK sampling. Successful PK/PD target attainment was defined as an unbound meropenem trough concentration above 2 mg/L or 8 mg/L. Population PK modelling was performed with NONMEM7.4. Results: In total, 58 patients were included, contributing 345 plasma samples over 70 dosing intervals. The 2 mg/L and 8 mg/L targets were successfully attained in 46% and 11% of all dosing intervals, respectively. A two-compartment population PK model with linear elimination and interindividual variability on clearance best described meropenem PK. The estimated creatinine clearance according to the Cockcroft-Gault equation was the only covariate retained during population PK analysis. Conclusion: This study provided detailed insight into meropenem PK in critically ill patients with preserved or increased renal function. We observed poor PK/PD target attainment, for which renal function was the only significant covariate.