Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the 'correct' model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software 'TDMx'. The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ±8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%).In the clinical dataset the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy:-16-25%), while the MSA or MAA utilizing these models simultaneously, resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy:-5% and 0%, respectively). MSA and MAA approaches implemented in 'TDMx' might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

Objectives: Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance. Methods: Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression. Results: Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p ¼ 0.007) significantly influenced the performance of the Goti model. Conclusions: The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

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Vancomycin dosing should be accompanied by area under the concentrationtime curve (AUC)-guided dosing using model-informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single-sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one-or two-sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation-based approach in a simulation-estimation study mimicking a heterogenous patient population (n = 6000). The optimal single-sample timepoints were identified between 2-6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%-24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5-6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%-18.6% imprecision), although the difference in the two-sampling strategies were minor.The equation-based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Gram-positive anti-infective therapy using vancomycin should be supported by population pharmacokinetic models, especially in patients who are critically ill. Therefore, 1-2 plasma samples (ideally a sample from the early profile andThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.