Assessing the predictive performance of population pharmacokinetic models for intravenous polymyxin B in critically ill patients

Population pharmacokinetics study on nebulized and intravenous administration of polymyxin B in patients with pneumonia caused by multidrug-resistant gram-negative bacteria

Polymyxin B (PMB) remains a last-line therapeutic agent for multidrug-resistant gram-negative bacteria (MDR-GNB) infections. However, reliable pharmacokinetic (PK) data to guide nebulized PMB dosing regimens in critically ill patients are limited. This study aimed to establish a population pharmacokinetic (PopPK) model for PMB in both epithelial lining fluid (ELF) and plasma of critically ill patients with MDR-GNB pneumonia and to optimize dosing regimens. A prospective PK study was conducted in 76 adult patients receiving nebulized PMB either as monotherapy or in combination with intravenous administration. PK data were analyzed using non-linear mixed-effect modeling, with PMB concentration-time profiles described by a coupled model integrating separate two-compartment models for plasma and ELF. The final model identified albumin levels and age as significant covariates influencing PK variability. Monte Carlo simulations demonstrated that nebulization therapy either alone or combined with intravenous administration significantly enhances ELF concentration and the probability of target attainment. Additionally, Pseudomonas aeruginosa requires higher nebulized doses than Klebsiella pneumoniae and Acinetobacter baumannii. This study develops a PopPK model of PMB in ELF and plasma, providing critical insights to optimize PMB treatment strategies for patients with MDR-GNB pneumonia.

A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction

Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.

Evaluation of the predictive performance of an online voriconazole dose calculator in children

BACKGROUND

The dosing of voriconazole is challenging in pediatrics. One approach to improve the dosing is through the use of Bayesian concentration-guided dosing software. Our study assessed the predictive performance of a freely available online voriconazole dose calculator in pediatric patients "NextDose" ( https://www.nextdose.org/ ).

METHODS

Per each dose calculator, we predicted voriconazole concentrations. We did both a priori and a posteriori Bayesian predictions.

RESULTS

A total of 51 patients were included in this study. For a priori predictions, bias was + 26% while imprecision was 70%. For a posteriori predictions, bias and imprecision were 0.01% and 46%.

DISCUSSION

In conclusion, the available online dose calculator was overpredicting the concentrations before voriconazole observations were available. However, with just one measured concentration, the predictions improved with minimal bias and an acceptable level of imprecision. There is a need for more prospective studies evaluating the use of voriconazole dosing calculators in the pediatric population to assess if they can improve the achievement of therapeutic target concentrations compared to standard of care.

Three methods to optimise polymyxin B dosing using estimated AUC after first dose: validation with the data generated by Monte Carlo simulation

To achieve the AUC-guided dosing, we proposed three methods to estimate polymyxin B AUC across 24 h at steady state (AUCSS,24h) using limited concentrations after its first dose.Monte Carlo simulation based on a well-established population PK model was performed to generate the PK profiles of 1000 patients with normal or abnormal renal function. Polymyxin B AUCSS,24h was estimated for each subject using three methods (two-point PK approach, three-point PK approach, and four-point PK approach) based on limited concentration data in its first dose and compared with the actual AUC at steady state calculated using the linear-trapezoidal formula.In patients with normal renal function, the mean bias of two-point PK approach, three-point PK approach, and four-point PK approach was -8.73%, 1.37%, and -0.48%, respectively. The corresponding value was -11.15%, 1.99%, and -0.28% in patients with renal impairment, respectively. The largest mean bias of two-point PK approach, three-point PK approach, and four-point PK approach was -12.63%, -6.47%, and -0.54% when the sampling time shifted.The Excel calculators designed based on the three methods can be potentially used to optimise the dosing regimen of polymyxin B in the clinic.

Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

BACKGROUND AND OBJECTIVES

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

METHODS

Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

RESULTS

Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

CONCLUSIONS

This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

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.

External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese

Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.

A Cross-sectional Comparative Analysis of Eleven Population Pharmacokinetic Models for Docetaxel in Chinese Breast Cancer Patients

OBJECTIVE

Various population pharmacokinetic (PPK) models have been established to help determine the appropriate dosage of docetaxel, however, no clear consensus on optimal dosing has been achieved. The purpose of this study is to perform an external evaluation of published models in order to test their predictive performance, and to find an appropriate PPK model for Chinese breast cancer patients.

METHODS

A systematic literature search of docetaxel PPK models was performed using PubMed, Web of Science, China National Knowledge Infrastructure, and WanFang databases. The predictive performance of eleven identified models was evaluated using prediction-based and simulation-based diagnostics on an independent dataset (112 docetaxel concentrations from 56 breast cancer patients). The -2×log (likelihood) and Akaike information criterion were also calculated to evaluate model fit.

RESULTS

The median prediction error of eight of the eleven models was less than 10%. The model fitting results showed that the three-compartment model of Bruno et al. had the best prediction performance and that the three compartment model of Wang et al. had the best simulation effect. Furthermore, although the covariates that significantly affect PK parameters were different between them, seven models demonstrated that docetaxel PK parameters were influenced by liver function.

CONCLUSIONS

Three compartment PPK models may be predictive of optimal docetaxel dosage for Chinese breast cancer patients. However, for patients with impaired liver function, the choice of which model to use to predict the blood concentration of docetaxel still requires great care.

Population pharmacokinetics of polymyxin B in patients with liver dysfunction

AIMS

Polymyxin B (PMB) is widely used to treat infections caused by multidrug-resistant Gram-negative pathogens. Currently, the pharmacokinetic data of PMB in patients with liver dysfunction are limited. This study aimed to develop a population pharmacokinetic (PopPK) model of PMB in patients with liver dysfunction and identify the factors affecting PMB pharmacokinetics.

METHODS

We conducted a retrospective pharmacokinetic study involving 136 adults with different levels of liver function. Nonlinear mixed effects modelling was used to develop a PopPK model of PMB. Monte Carlo simulation was used to design PMB dosage schedules across various liver and renal functions.

RESULTS

PMB pharmacokinetic analyses included 401 steady-state concentrations in 136 adult patients. A one-compartment pharmacokinetic model with first-order absorption and elimination was used to describe the data. The typical population value of PMB clearance was 2.43 L/h and the volume of distribution was 23.11 L. This study revealed that creatinine clearance (CrCL) and Child-Pugh class were significantly associated with PMB pharmacokinetic parameters; however, clinically relevant variations of dose-normalized drug exposure were not significant. For patients with a minimum inhibitory concentration of ≤0.5 mg/L, the appropriate dose was 40-75 mg/12-h. When the dose exceeded 100 mg/12-h, the risk of nephrotoxicity increased significantly.

CONCLUSIONS

This study provided PMB pharmacokinetic information for patients with liver dysfunction. Patients with renal and liver dysfunctions may not require an initial dose adjustment. Rather than PopPK-guided dose adjustment, therapeutic drug monitoring of PMB plays a more direct role in optimizing dosing regimens based on its therapeutic window.

Real-Time Monitoring of Antibiotics in the Critically Ill Using Biosensors

By ensuring optimal dosing, therapeutic drug monitoring (TDM) improves outcomes in critically ill patients by maximizing effectiveness while minimizing toxicity. Current methods for measuring plasma drug concentrations, however, can be challenging, time-consuming, and slow to return an answer, limiting the extent to which TDM is used to optimize drug exposure. A potentially promising solution to this dilemma is provided by biosensors, molecular sensing devices that employ biorecognition elements to recognize and quantify their target molecules rapidly and in a single step. This paper reviews the current state of the art for biosensors regarding their application to TDM of antibiotics in the critically ill, both as ex vivo point-of-care devices supporting single timepoint measurements and in vivo devices supporting continuous real-time monitoring in situ in the body. This paper also discusses the clinical development of biosensors for TDM, including regulatory challenges and the need for standardized performance evaluation. We conclude by arguing that, through precise and real-time monitoring of antibiotics, the application of biosensors in TDM holds great promise for enhancing the optimization of drug exposure in critically ill patients, offering the potential for improved outcomes.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients

OBJECTIVE

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset.

METHOD

PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance.

RESULT

A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting.

CONCLUSION

The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Optimization of polymyxin B regimens for the treatment of carbapenem-resistant organism nosocomial pneumonia: a real-world prospective study

BACKGROUND

Polymyxin B is the first-line therapy for Carbapenem-resistant organism (CRO) nosocomial pneumonia. However, clinical data for its pharmacokinetic/pharmacodynamic (PK/PD) relationship are limited. This study aimed to investigate the relationship between polymyxin B exposure and efficacy for the treatment of CRO pneumonia in critically ill patients, and to optimize the individual dosing regimens.

METHODS

Patients treated with polymyxin B for CRO pneumonia were enrolled. Blood samples were assayed using a validated high-performance liquid chromatography-tandem mass spectrometry method. Population PK analysis and Monte Carlo simulation were performed using Phoenix NLME software. Logistic regression analyses and receiver operating characteristic (ROC) curve were employed to identify the significant predictors and PK/PD indices of polymyxin B efficacy.

RESULTS

A total of 105 patients were included, and the population PK model was developed based on 295 plasma concentrations. AUCss,24 h/MIC (AOR = 0.97, 95% CI 0.95-0.99, p = 0.009), daily dose (AOR = 0.98, 95% CI 0.97-0.99, p = 0.028), and combination of inhaled polymyxin B (AOR = 0.32, 95% CI 0.11-0.94, p = 0.039) were independent risk factors for polymyxin B efficacy. ROC curve showed that AUCss,24 h/MIC is the most predictive PK/PD index of polymyxin B for the treatment of nosocomial pneumonia caused by CRO, and the optimal cutoff point value was 66.9 in patients receiving combination therapy with another antimicrobial. Model-based simulation suggests that the maintaining daily dose of 75 and 100 mg Q12 h could achieve ≥ 90% PTA of this clinical target at MIC values ≤ 0.5 and 1 mg/L, respectively. For patients unable to achieve the target concentration by intravenous administration, adjunctive inhalation of polymyxin B would be beneficial.

CONCLUSIONS

For CRO pneumonia, daily dose of 75 and 100 mg Q12 h was recommended for clinical efficacy. Inhalation of polymyxin B is beneficial for patients who cannot achieve the target concentration by intravenous administration.

Population pharmacokinetics and limited sampling strategies of polymyxin B in critically ill patients

OBJECTIVES

To characterize the pharmacokinetics (PK) of polymyxin B in Chinese critically ill patients. The factors significantly affecting PK parameters are identified, and a limited sampling strategy for therapeutic drug monitoring of polymyxin B is explored.

METHODS

Thirty patients (212 samples) were included in a population PK analysis. A limited sampling strategy was developed using Bayesian estimation, multiple linear regression and modified integral equations. Non-linear mixed-effects models were developed using Phoenix NLME software.

RESULTS

A two-compartment population PK model was used to describe polymyxin B PK. Population estimates of the volumes of central compartment distribution (V) and peripheral compartment distribution (V2), central compartment clearance (CL) and intercompartmental clearance (Q) were 7.857 L, 12.668 L, 1.672 L/h and 7.009 L/h. Continuous renal replacement therapy (CRRT) significantly affected CL, and body weight significantly affected CL and Q. The AUC0-12h of polymyxin B in patients with CRRT was significantly lower than in patients without CRRT. CL and Q increased with increasing body weight. A limited sampling strategy was suggested using a two-sample scheme with plasma at 0.5h and 8h after the end of infusion (C0.5 and C8) for therapeutic drug monitoring in the clinic.

CONCLUSIONS

A dosing regimen should be based on body weight and the application of CRRT. A two-sample strategy for therapeutic drug monitoring could facilitate individualized treatment with polymyxin B in critically ill patients.

Transferability of Published Population Pharmacokinetic Models for Apixaban and Rivaroxaban to Subjects with Obesity Treated for Venous Thromboembolism: A Systematic Review and External Evaluations

Apixaban and rivaroxaban have first-line use for many patients needing anticoagulation for venous thromboembolism (VTE). The pharmacokinetics of these drugs in non-obese subjects have been extensively studied, and, while changes in pharmacokinetics have been documented in obese patients, data remain scarce for these anticoagulants. The aim of this study was to perform an external validation of published population pharmacokinetic (PPK) models of apixaban and rivaroxaban in a cohort of obese patients with VTE. A literature search was conducted in the PubMed/MEDLINE, Scopus, and Embase databases following the PRISMA statement. External validation was performed using MonolixSuite software, using prediction-based and simulation-based diagnostics. An external validation dataset from the university hospitals of Brest and Rennes, France, included 116 apixaban pharmacokinetic samples from 69 patients and 121 rivaroxaban samples from 81 patients. Five PPK models of apixaban and 16 models of rivaroxaban were included, according to the inclusion criteria of the study. Two of the apixaban PPK models presented acceptable performances, whereas no rivaroxaban PPK model did. This study identified two published models of apixaban applicable to apixaban in obese patients with VTE. However, none of the rivaroxaban models evaluated were applicable. Dedicated studies appear necessary to elucidate rivaroxaban pharmacokinetics in this population.

Evaluation and Validation of the Limited Sampling Strategy of Polymyxin B in Patients with Multidrug-Resistant Gram-Negative Infection

Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration–time curve across 24 h at a steady state (AUC_ss,24h) of 50–100 mg·h/L. The limited sampling strategy (LSS) is useful for predicting AUC values. However, establishing an LSS is a time-consuming process requiring a relatively dense sampling of patients. Further, given the variability among different centers, the predictability of LSSs is frequently questioned when it is extrapolated to other clinical centers. Currently, limited data are available on a reliable PMB LSS for estimating AUC_ss,24h. This study assessed and validated the practicability of LSSs established in the literature based on data from our center to provide reliable and ready-made PMB LSSs for laboratories performing therapeutic drug monitoring (TDM) of PMB. The influence of infusion and sampling time errors on predictability was also explored to obtain the optimal time points for routine PMB TDM. Using multiple regression analysis, PMB LSSs were generated from a model group of 20 patients. A validation group (10 patients) was used to validate the established LSSs. PMB LSSs from two published studies were validated using a dataset of 30 patients from our center. A population pharmacokinetic model was established to simulate the individual plasma concentration profiles for each infusion and sampling time error regimen. Pharmacokinetic data obtained from the 30 patients were fitted to a two-compartment model. Infusion and sampling time errors observed in real-world clinical practice could considerably affect the predictability of PMB LSSs. Moreover, we identified specific LSSs to be superior in predicting PMB AUC_ss,24h based on different infusion times. We also discovered that sampling time error should be controlled within −10 to 15 min to obtain better predictability. The present study provides validated PMB LSSs that can more accurately predict PMB AUC_ss,24h in routine clinical practice, facilitating PMB TDM in other laboratories and pharmacokinetics/pharmacodynamics-based clinical studies in the future.

External evaluation of published population pharmacokinetic models of posaconazole

Population pharmacokinetic (PopPK) models of posaconazole have been established to promote the precision dosing. However, the performance of these models extrapolated to other centers has not been evaluated. This study aimed to conduct an external evaluation of published posaconazole PopPK models to evaluate their predictive performance. Posaconazole PopPK models screened from the PubMed and MEDLINE databases were evaluated using an external dataset of 213 trough concentration samples collected from 97 patients. Their predictive performance was evaluated by prediction-based diagnosis (prediction error), simulation-based diagnosis (visual predictive check), and Bayesian forecasting. In addition, external cohorts with and without proton pump inhibitor were used to evaluate the models respectively. Ten models suitable for the external dataset were finally included into the study. In prediction-based diagnostics, none of the models met pre-determined criteria for predictive indexes. Only M4, M6, and M10 demonstrated favorable simulations in visual predictive check. The prediction performance of M5, M7, M8, and M9 evaluated using the cohort without proton pump inhibitor showed a significant improvement compared to that evaluated using the whole cohort. Consistent with our expectations, Bayesian forecasting significantly improved the predictive per-formance of the models with two or three prior observations. In general, the applicability of these published posaconazole PopPK models extrapolated to our center was unsatisfactory. Prospective studies combined with therapeutic drug monitoring are needed to establish a PopPK model for posaconazole in the Chinese population to promote individualized dosing.

Assessing the predictive performance of population pharmacokinetic models for intravenous polymyxin B in critically ill patients

Polymyxin B (PMB) has reemerged as a last‐line therapy for infections caused by multidrug‐resistant gram‐negative pathogens, but dosing is challenging because of its narrow therapeutic window and pharmacokinetic (PK) variability. Population PK (POPPK) models based on suitably powered clinical studies with appropriate sampling strategies that take variability into consideration can inform PMB dosing to maximize efficacy and minimize toxicity and resistance. Here we reviewed published PMB POPPK models and evaluated them using an external validation data set (EVD) of patients who are critically ill and enrolled in an ongoing clinical study to assess their utility. Seven published POPPK models were employed using the reported model equations, parameter values, covariate relationships, interpatient variability, parameter covariance, and unexplained residual variability in NONMEM (Version 7.4.3). The predictive ability of the models was assessed using prediction‐based and simulation‐based diagnostics. Patient characteristics and treatment information were comparable across studies and with the EVD (n = 40), but the sampling strategy was a main source of PK variability across studies. All models visually and statistically underpredicted EVD plasma concentrations, but the two‐compartment models more accurately described the external data set. As current POPPK models were inadequately predictive of the EVD, creation of a new POPPK model based on an appropriately powered clinical study with an informed PK sampling strategy would be expected to improve characterization of PMB PK and identify covariates to explain interpatient variability. Such a model would support model‐informed precision dosing frameworks, which are urgently needed to improve PMB treatment efficacy, limit resistance, and reduce toxicity in patients who are critically ill.