Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing.

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood. Using busulfan as an example application and area under the concentration-time curve (AUC) as a target exposure metric, these estimation methods were compared using retrospective patient data (N = 246) and simulated precision dosing treatment courses. NCA was performed with or without peak extension, and MAP Bayesian estimation was performed using either the one-compartment Shukla model or the two-compartment McCune model. All methods showed good agreement on real-world data (correlation coefficients of 0.945-0.998) as assessed by Bland-Altman plots, although agreement between NCA and MAP methods was higher during the first dosing interval (0.982-0.994) compared to subsequent dosing intervals (0.918-0.938). In dose adjustment simulations, both NCA and MAP estimated high target attainment (> 98%) although true simulated target attainment was lower for NCA (63-66%) versus MAP (91-93%). The largest differences in AUC estimation were due to different assumptions for the shape of the concentration curve during the infusion phase, followed by how the methods considered time-dependent clearance and concentration-time points collected in earlier intervals. In conclusion, although AUC estimates between the two methods showed good correlation, in a simulated study, MAP lead to higher target attainment. When changing from one method to another, or changing infusion duration and other factors, optimum estimated exposure targets may require adjusting to maintain a consistent exposure.

Systematic Evaluation of Pharmacokinetic Models for Model-Informed Precision Dosing of Meropenem in Critically Ill Patients Undergoing Continuous Renal Replacement Therapy.

The altered pharmacokinetics of renally cleared drugs such as meropenem in critically ill patients receiving continuous renal replacement therapy (CRRT) might impact target attainment. Model-informed precision dosing (MIPD) is applied to individualize meropenem dosing. However, most population pharmacokinetic (PopPK) models developed to date have not yet been evaluated for MIPD. Eight PopPK models based on adult CRRT patients were identified in a systematic literature research and encoded in NONMEM 7.4. A data set of 73 CRRT patients from two different study centers was used to evaluate the predictive performance of the models using simulation and prediction-based diagnostics for i) a priori dosing based on patient characteristics only and ii) Bayesian dosing by including the first measured trough concentration. Median prediction error (MPE) for accuracy within |20%| (95% confidence intervals including zero) and median absolute prediction error (MAPE) for precision ≤ 30% were considered clinically acceptable. For a priori dosing, most models (n = 5) showed accuracy and precision MPE within |20%| and MAPE <35%. The integration of the first measured meropenem concentration improved the predictive performance of all models (median MAPE decreased from 35.4 to 25.0%; median MPE decreased from 21.8 to 4.6%). The best predictive performance for intermittent infusion was observed for the O'Jeanson model, including residual diuresis as covariate (a priori and Bayesian dosing MPE within |2%|, MAPE <30%). Our study revealed the O'Jeanson model as the best-predicting model for intermittent infusion. However, most of the selected PopPK models are suitable for MIPD in CRRT patients when one therapeutic drug monitoring sample is available.

Reducing emissions of atmospheric pollutants along major dry bulk and tanker routes through autonomous shipping.

The present study investigates the environmental benefits of phasing-in autonomous ships in global maritime transportation along major dry bulk and tanker routes using Bayesian probabilistic forecasting algorithm. The focus is on the simulations and calibrations on the navigational behavior of autonomous ships at both port and high-sea, as well as the potential emission abatement of atmospheric pollutants compared to the conventional fleet along the sailing routes. We use historical data on major international tanker and dry bulk trade routes to characterize the ship movements and trends in ship emission. Different scenarios are evaluated with a combination of autonomous ship phase-in rates (25, 75, 100%) and cleaner fuel choices in Years 2030 and 2050 (from the baseline Year, 2020). The results show that the magnitude of the emission reduction generally increases with a higher level of autonomous ships in the fleet as expected, and the magnitude ranges from small increments to major reductions of 37-64% along the different routes. Overall, we hope that our findings can contribute towards the realization of environmental benefits with the adoption of autonomous shipping along the major shipping routes in the future.

Limited sampling strategies for accurate determination of extended half-life factor VIII pharmacokinetics in severe haemophilia A patients.

BACKGROUND: Extended half-life (EHL) factor VIII (FVIII) products may decrease the burden of prophylactic treatment in haemophilia A by reducing infusion frequency. However, these products still exhibit wide inter-patient variability and benefit from pharmacokinetic (PK) tailoring. OBJECTIVE: Identify limited sampling strategies for rFVIIIFc, an EHL FVIII product, that produce accurate estimates of PK parameters and relevant troughs. METHODS: We performed a limited sampling analysis on simulated populations of adults, adolescents, and children based on published population PK data. Sampling strategies were evaluated by comparing the error in estimates of half-life, clearance, and trough levels, to a full 6-sample design. Furthermore, we assessed the impact of incorporating knowledge about prior doses, and the day of the PK study within the regimen. We also evaluated the potential inappropriate dose adjustment rate (IDAR) among the modelled sampling strategies. RESULTS: Many sampling strategies, including several 2-sample designs, accurately predicted the PK and exposure measures (median absolute error <10%). When samples are only collected during a single visit (i.e., predose + peak), inclusion of prior dose information reduces median half-life error from >20% to ~5% for adults/adolescents. In this same scenario, appropriate scheduling of the PK study decreases likelihood of unmeasurable predose samples, reducing median error on the 72-h trough from 25% to <12% in the youngest population. CONCLUSIONS: The PK of rFVIIIFc can be accurately estimated using only peak and trough samples, provided that knowledge of prior doses is incorporated and the PK study is planned on an appropriate day within the dosing regimen.

Population pharmacokinetics of mycophenolic acid in paediatric patients.

Mycophenolic acid (MPA) is widely used in paediatric kidney transplant patients and sometimes prescribed for additional indications. Population pharmacokinetic or pharmacodynamic modelling has been frequently used to characterize the fixed, random and covariate effects of MPA in adult patients. However, MPA population pharmacokinetic data in the paediatric population have not been systematically summarized. The objective of this narrative review was to provide an up-to-date critique of currently available paediatric MPA population pharmacokinetic models, with emphases on modelling techniques, pharmacological findings and clinical relevance. PubMed and EMBASE were searched from inception of database to May 2020, where a total of 11 studies have been identified representing kidney transplant (n = 4), liver transplant (n = 1), haematopoietic stem cell transplant (n = 1), idiopathic nephrotic syndrome (n = 2), systemic lupus erythematosus (n = 2), and a combined population consisted of kidney, liver and haematopoietic stem cell transplant patients (n = 1). Critical analyses were provided in the context of MPA absorption, distribution, metabolism, excretion and bioavailability in this paediatric database. Comparisons to adult patients were also provided. With respect to clinical utility, Bayesian estimation models (n = 6) with acceptable accuracy and precision for MPA exposure determination have also been identified and systematically evaluated. Overall, our analyses have identified unique features of MPA clinical pharmacology in the paediatric population, while recognizing several gaps that still warrant further investigations. This review can be used by pharmacologists and clinicians for improving MPA pharmacokinetic-pharmacodynamic modelling and patient care.

Evaluation of amikacin use and comparison of the models implemented in two Bayesian forecasting software packages to guide dosing.

AIMS: Bayesian forecasting software can assist in guiding therapeutic drug monitoring (TDM)-based dose adjustments for amikacin to achieve therapeutic targets. This study aimed to evaluate amikacin prescribing and TDM practices, and to determine the suitability of the amikacin model incorporated into the DoseMeRx® software as a replacement for the previously available software (Abbottbase®). METHODS: Patient demographics, pathology, amikacin dosing history, amikacin concentrations and Abbottbase® predicted TDM targets (area under the curve up to 24 hours, maximum concentration and trough concentration) were collected for adults receiving intravenous amikacin (2012-2017). Concordance with the Australian Therapeutic Guidelines was assessed. Observed and predicted amikacin concentrations were compared to determine the predictive performance (bias and precision) of DoseMeRx®. Amikacin TDM targets were predicted by DoseMeRx® and compared to those predicted by Abbottbase®. RESULTS: Overall, guideline compliance for 63 courses of amikacin in 47 patients was suboptimal. Doses were often lower than recommended. For therapy >48 h, TDM sample collection timing was commonly discordant with recommendations, therapeutic target attainment low and 34% of dose adjustments inappropriate. DoseMeRx® under-predicted amikacin concentrations by 0.9 mg/L (95% confidence interval [CI] -1.4 to -0.5) compared with observed concentrations. However, maximum concentration values (n = 19) were unbiased (-1.7 mg/L 95%CI -5.8 to 0.8) and precise (8.6% 95%CI 5.4-18.1). Predicted trough concentration values (n = 7) were, at most, 1 mg/L higher than observed. Amikacin area under the curve values estimated using Abbottbase® (181 mg h/L 95%CI 161-202) and DoseMeRx® (176 mg h/L 95%CI 152-199) were similar (P = .59). CONCLUSION: Amikacin dosing and TDM practice was suboptimal compared with guidelines. The model implemented by DoseMeRx® is satisfactory to guide amikacin dosing.

Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.

PURPOSE: Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM. METHODS: We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days. RESULTS: The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were - 7.7%, -4.5% and - 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors. CONCLUSIONS: The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.

Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model.

We examine issues of prior sensitivity in a semi-parametric hierarchical extension of the INAR(p) model with innovation rates clustered according to a Pitman-Yor process placed at the top of the model hierarchy. Our main finding is a graphical criterion that guides the specification of the hyperparameters of the Pitman-Yor process base measure. We show how the discount and concentration parameters interact with the chosen base measure to yield a gain in terms of the robustness of the inferential results. The forecasting performance of the model is exemplified in the analysis of a time series of worldwide earthquake events, for which the new model outperforms the original INAR(p) model.

An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting.

AIMS: To evaluate 3 Bayesian forecasting (BF) programs-TDMx, InsightRx and DoseMe-on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists. METHODS: Clinical pharmacists across 3 Australian hospitals that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialled. Participants were given 4 case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a χ2 test of equal or given proportions to identify significant differences in response. RESULTS: Twenty-seven pharmacists, from hospitals, participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (P = .02) of participants recording a Likert score of 4 or 5 for DoseMe, TDMx and InsightRx, respectively. Participants found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision-making with >50% of participants scoring a 4 or 5 across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display. CONCLUSION: Although differences exist between programs, all 3 programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

Development and evaluation of a generic population pharmacokinetic model for standard half-life factor VIII for use in dose individualization.

Hemophilia A is a rare bleeding disorder resulting from a lack of functional factor VIII (FVIII). Therapy consists of replacement with exogenous FVIII, but is complicated by high inter-patient variability. A population pharmacokinetics (PopPK) approach can facilitate the uptake of an individualized approach to hemophilia therapy. We developed a PopPK model using data from seven brands of standard half-life FVIII products. The final model consists of a 2-compartment structure, with a proportional residual error model and between-subject variability on clearance and central volume. Fat-free mass, age, and brand were found to significantly affect pharmacokinetic (PK) parameters. Internal and external evaluations found that the model is fit for Bayesian forecasting and capable of predicting PK for brands not included in the modelling dataset, and useful for determining individualized prophylaxis regimens for hemophilia A patients.

Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients.

Fanhdi/Alphanate is a plasma derived factor VIII concentrate used for treating hemophilia A, for which there has not been any dedicated model describing its pharmacokinetics (PK). A population PK model was developed using data extracted from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project. WAPPS-Hemo provided individual PK profiles for hemophilia patients using sparse observations as provided in routine clinical care by hemophilia centers. Plasma factor activity measurements and covariate data from hemophilia A patients on Fanhdi/Alphanate were extracted from the WAPPS-Hemo database. A population PK model was developed using NONMEM and evaluated for suitability for Bayesian forecasting using prediction-corrected visual predictive check (pcVPC), cross validation, limited sampling analysis and external evaluation against a population PK model developed on rich sampling data. Plasma factor activity measurements from 92 patients from 12 centers were used to derive the model. The PK was best described by a 2-compartment model including between subject variability on clearance and central volume, fat free mass as a covariate on clearance, central and peripheral volumes, and age as covariate on clearance. Evaluations showed that the developed population PK model could predict the PK parameters of new individuals based on limited sampling analysis and cross and external evaluations with acceptable precision and bias. This study shows the feasibility of using real-world data for the development of a population PK model. Evaluation and comparison of the model for Bayesian forecasting resulted in similar results as a model developed using rich sampling data.

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.

The balanced discrete Burr-Hatke model and mixing INAR(1) process: properties, estimation, forecasting and COVID-19 applications.

The main concern of this paper is providing a flexible discrete model that captures every kind of dispersion (equi-, over- and under-dispersion). Based on the balanced discretization method, a new discrete version of Burr-Hatke distribution is introduced with the partial moment-preserving property. Some statistical properties of the new distribution are introduced, and the applicability of proposed model is evaluated by considering counting series. A new integer-valued autoregressive (INAR) process based on the mixing Pegram and binomial thinning operators with discrete Burr-Hatke innovations is introduced, which can model contagious data properly. The different estimation approaches of parameters of the new process are provided and compared through the Monte Carlo simulation scheme. The performance of the proposed process is evaluated by four data sets of the daily death counts of the COVID-19 in Austria, Switzerland, Nigeria and Slovenia in comparison with some competitor INAR(1) models, along with the Pearson residual analysis of the assessing model. The goodness of fit measures affirm the adequacy of the proposed process in modeling all COVID-19 data sets. The fundamental prediction procedures are considered for new process by classic, modified Sieve bootstrap and Bayesian forecasting methods for all COVID-19 data sets, which is concluded that the Bayesian forecasting approach provides more reliable results.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring.

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Bayesian monitoring of COVID-19 in Sweden.

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Setting course for a translational pharmacology and a predictive toxicology based on the numerical probability of clinical relevance.

For a significant share of the chemicals, current bioassays mispredicted the outcomes in the reference methods they simulate. For any drug or chemical, and depending on the regulatory or corporate situation, three different approaches calculate the numerical probability by which agreement (or discrepancy) can be statistically expected between (1) the result of a predictive bioassay, and (2) the outcome on its reference method. If such concordance is expected with enough confidence based on a sufficient percentage probability, then specific results from that bioassay can be considered as correctly predictive. The statistical approaches analyzed in this article assist in valuable tasks, including (1) a better translation of the clinical relevance (or insignificance) of specific preclinical findings; (2) waiving unnecessary animal testing (or any other unpredictive testing; e.g., a given in vitro bioassay), and (3) in advancing only the most promising candidates in the pharmaceutical, pesticide, or chemical development process.

External Evaluation of Population Pharmacokinetic Models to Inform Precision Dosing of Meropenem in Critically Ill Patients.

Routine clinical meropenem therapeutic drug monitoring data can be applied to model-informed precision dosing. The current study aimed to evaluate the adequacy and predictive capabilities of the published models with routine meropenem data and identify the dosing adaptations using a priori and Bayesian estimation. For this, 14 meropenem models for the external evaluation carried out on an independent cohort of 134 patients with 205 meropenem concentrations were encoded in NONMEM 7.3. The performance was determined using: 1) prediction-based and simulation-based diagnostics; and 2) predicted meropenem concentrations by a priori prediction using patient covariates only; and Bayesian forecasting using previous observations. The clinical implications were assessed according to the required dose adaptations using the meropenem concentrations. All assessments were stratified based on the patients with or without continuous renal replacement therapy. Although none of the models passed all tests, the model by Muro et al. showed the least bias. Bayesian forecasting could improve the predictability over an a priori approach, with a relative bias of -11.63-68.89% and -302.96%-130.37%, and a relative root mean squared error of 34.99-110.11% and 14.78-241.81%, respectively. A dosing change was required in 40.00-68.97% of the meropenem observation results after Bayesian forecasting. In summary, the published models couldn't adequately describe the meropenem pharmacokinetics of our center. Although the selection of an initial meropenem dose with a priori prediction is challenging, the further model-based analysis combining therapeutic drug monitoring could be utilized in the clinical practice of meropenem therapy.

Precision dosing software to optimize antimicrobial dosing: a systematic search and follow-up survey of available programs.

BACKGROUND: Precision dosing programs are promising tools for optimising antimicrobial dosing. Selecting the ideal program for local application may be challenging due to the large variety of available programs with differing characteristics. OBJECTIVES: The objectives of this study were to systematically identify available precision dosing software programs to optimize antimicrobial dosing and describe the characteristics of each program. Details on the ability of programs to provide beta-lactam dosing support was also gathered. SOURCES: A systematic review search strategy was used to identify candidate software programs described in the literature in Embase and PubMed. A detailed survey was then developed to identify characteristics of programs, including details on the underlying methodology driving dosing software recommendations, interface characteristics, costs and regulatory affairs. Software developers from all identified programs were invited to participate in the survey. CONTENT: The systematic search results identified 18 programs. Fifteen developers responded to the survey (83%) and 11 programs provide dosing support for at least one beta-lactam. Fourteen programs can utilize measured drug concentrations to generate dosing recommendations, with 13 able to generate empiric dosing recommendations. Six programs integrate with local electronic health records and four are registered with at least one regulatory agency. Pharmacokinetic models in combination with Bayesian statistics is the most common methodology used to generate dosing recommendations, with 14 programs utilizing this method. IMPLICATIONS: There was significant variability in the available antimicrobial profiles and characteristics among dosing software programs. As healthcare providers will differ in their requirements within their local settings, clinicians should use these findings to identify potential candidate programs and, if feasible, trial these to ensure they meet their specific requirements.

Model-informed precision dosing of vancomycin via continuous infusion: a clinical fit-for-purpose evaluation of published PK models.

BACKGROUND: Model-informed precision dosing is an innovative approach used to guide bedside vancomycin dosing. The use of Bayesian software requires suitable and externally validated population pharmacokinetic (popPK) models. OBJECTIVES: This study aimed to identify suitable popPK models for a priori prediction and a posteriori forecasting of vancomycin in continuous infusion. Additionally, model averaging (MAA) and model selection approach (MSA) were compared with the identified popPK models. METHODS: Clinical pharmacokinetic data were retrospectively collected from patients receiving continuous vancomycin therapy and admitted to a general ward of three large Belgian hospitals. The predictive performance of the popPK models, identified in a systematic literature search, as well as the MAA/MSA were evaluated for the a priori and a posteriori scenarios using bias, root mean square errors, normalised prediction distribution errors and visual predictive checks. RESULTS: The predictive performance of 23 popPK models was evaluated based on clinical data from 169 patients and 923 therapeutic drug monitoring samples. Overall, the best predictive performance was found using the Okada et al. model (bias < -0.1 mg/L) followed by the Colin et al. MODEL: The MAA/MSA predicted with a constantly high precision and low inaccuracy and were clinically acceptable in the Bayesian forecasting. CONCLUSION: This study identified the two-compartmental models of Okada et al. and Colin et al. as most suitable for non-ICU patients to forecast individual exposure profiles after continuous vancomycin infusion. The MAA/MSA performed equally as well as the individual popPK models; therefore, both approaches could be used in clinical practice to guide dosing decisions.