Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model-informed precision dosing trial simulations

Model-based translation of the PKPD-relationship for linezolid and vancomycin on methicillin-resistant Staphylococcus aureus: from in vitro time-kill experiments to a mouse pneumonia model

OBJECTIVES

MRSA is one of the main pathogens that cause nosocomial pneumonia. Based on longitudinal in vitro and in vivo data, a pharmacokinetic-pharmacodynamic (PKPD) model was built to quantify the effect of two control antibiotics (LZD and VAN) for Gram-positive bacteria in a standardized mouse pneumonia model.

METHODS

The PKPD model was developed for data generated on the MRSA strain 160 079 in static in vitro time-kill experiments and thereafter adjusted to fit data from lungs of neutropenic mice administered with single or multiple doses of LZD (0.5-40 mg/kg) or VAN (1-40 mg/kg). Simulations with human PK were run to predict antibacterial response in patients.

RESULTS

Bacterial regrowth observed in vitro when exposed to VAN concentrations was described by an adaptive resistance model. The selected MRSA isolate showed good virulence in the mouse pneumonia model. Bacterial load in lungs decreased up to 2-log with respect to control mice after LZD and VAN treatment. A 70%-75% lower killing rate was estimated for the in vivo data when compared with in vitro. Simulations displayed bacterial stasis at 24 h for patients infected with bacteria with MICs below the clinical breakpoint for both drugs after administering standard-of-care dosing regimens.

CONCLUSIONS

A translational workflow allowed us to build a PKPD model with both in vitro and in vivo data that characterized bacterial dynamics following LZD and VAN exposure, showing that this approach can inform the development of antibiotics. We also showcased the first successful use of the standardized mouse pneumonia model for Gram-positive bacteria.

Are All Pharmacokinetic Equations Created Equal? A Comparative Analysis of Trapezoidal and Non-Trapezoidal Methods for Estimating Day 1 Area Under the Curve in Adult Hospitalized Patients with Staphylococcus aureus Bacteremia

INTRODUCTION

This study compared the calculated vancomycin area under the curve (AUC0-24) using trapezoidal and non-trapezoidal first-order pharmacokinetic equations.

METHODS

This retrospective observational study included adult patients with documented MRSA bacteremia who received ≥ 48 h of intravenous vancomycin and had two consecutive serum levels after the first dose. AUC0-24 was calculated using trapezoidal and non-trapezoidal equations. Correlation and agreement between methods were assessed using Pearson's correlation coefficient (r) and Bland-Altman plots. Significant predictors (p < 0.05) from simple linear regression were included in a multiple linear regression model to evaluate their impact on AUC0-24 for both methods.

RESULTS

Fifty-two patients were included. The median age was 63 years (interquartile range [IQR]: 50-73), and the median vancomycin clearance was 4 l/h (IQR: 2-6). Median vancomycin AUC0-24 was 399 mg∙h/l (IQR: 257-674) for the trapezoidal method and 572 mg∙h/l (IQR: 466-807) for the non-trapezoidal method. There was a strong correlation between the methods (r = 0.87 [95% CI, 0.79-1]; P < 0.01), but Bland-Altman analysis showed poor agreement, with a bias of - 198 mg∙h/l and 95% limits of agreement from - 482 to 86 mg∙h/l. In multiple linear regression, total daily dose and vancomycin clearance were independent predictors of AUC0-24 for both methods, with a stronger impact on non-trapezoidal AUC0-24 (adjusted R2 = 0.70) than trapezoidal AUC0-24 (adjusted R2 = 0.59).

CONCLUSIONS

Trapezoidal and non-trapezoidal equations are not interchangeable for estimating vancomycin AUC0-24. The trapezoidal method consistently results in lower AUC0-24 estimates than the non-trapezoidal method.

External evaluation of intravenous vancomycin population pharmacokinetic models in adults receiving high-flux intermittent haemodialysis

AIMS

Patients undergoing haemodialysis (HD) are at greater risk of methicillin-resistant Staphylococcus aureus infections requiring intravenous vancomycin. Close vancomycin therapeutic drug monitoring is warranted in HD patients as renal clearance is the primary elimination pathway. Clinically, population pharmacokinetics (popPK) model-informed dosing is commonly used. This study aimed to perform an external evaluation of published vancomycin popPK models developed for adults undergoing high-flux intermittent HD, and to create a dosing nomogram derived from the model that performed best.

METHODS

A literature review was conducted through PubMed and EMBASE to identify relevant popPK models. an external dataset was collected retrospectively from patients of 2 healthcare centres in Quebec, Canada. Selected models were implemented in NONMEM (v7.5; ICON Development Solutions). Predictive performance was assessed through prediction and simulation-based diagnostics.

RESULTS

In total, 2386 vancomycin concentrations were collected from 274 patients and 476 antibiotic courses. Four vancomycin popPK models were selected for evaluation. None of the models demonstrated overall satisfactory or clinically acceptable predictive performance. Nonetheless, Bae et al.'s model performed best with a median prediction error of 16.25% and median absolute prediction error of 34.66%. Different predictive performance was also observed for vancomycin concentrations from samples collected during and between HD sessions.

CONCLUSION

All evaluated models presented poor overall predictive performance. Further studies are required, through existing popPK model parameter re-estimation or new model development, to adequately describe vancomycin pharmacokinetics for our high-flux intermittent HD patient cohort.

Vancomycin population pharmacokinetic models: Uncovering pharmacodynamic divergence amid clinicobiological resemblance

Vancomycin is an antibiotic used for severe infections. To ensure microbiological efficacy, a ratio of AUC/MIC ≥400 is recommended. However, there is significant interindividual variability in its pharmacokinetic parameters, necessitating therapeutic drug monitoring to adjust dosing regimens and ensure efficacy while avoiding toxicity. Population pharmacokinetic (PopPK) models enable dose personalization, but the challenge lies in the choice of the model to use among the multitude of models in the literature. We compared 18 PopPK models created from populations with the same sociodemographic and clinicobiological characteristics. Simulations were performed for a 47 years old man, weighing 70 kg, with an albumin level of 35.5 g/L, a creatinine clearance of 100 mL/min, an eGFR of 106 mL/min/1.73 m2, and receiving an intravenous infusion of 1 g × 2/day of VCM over 1 h for 48 h. Simulations of time-concentration profiles revealed differences, leading us to determine the probability of achieving microbiological efficacy (AUC/MIC ≥ 400) with each model. Depending on some models, a dose of 1 g × 2/day is required to ensure microbiological efficacy in over 90% of the population, while with the same dose other models do not exceed 10% of the population. To ensure that 90% of the patients are correctly exposed, a dose of vancomycin ranging from 0.9 g × 2/day to 2.2 g × 2/day is necessary a priori depending on the chosen model. These differences raise an issue in choosing a model for performing therapeutic drug monitoring using a PopPK model with or without Bayesian approach. Thus, it is fundamental to evaluate the impact of these differences on both efficacy/toxicity.

Identification of vancomycin exposure target in neonates: how much is enough?

OBJECTIVES

Vancomycin is commonly used in neonates with the same pharmacokinetics/pharmacodynamics (PK/PD) target as adults. However, no evidence supports this practice, and the association between trough concentrations and treatment outcomes has been widely questioned. This study aimed to identify the optimal PK/PD predictor and assess the correlation between AUC/MIC, trough concentration and the vancomycin efficacy in neonates.

METHODS

This study retrospectively collected neonates who used vancomycin and constructed a population pharmacokinetic (PPK) model to estimate the AUC. Logistic analyses were used to identify the variables related to efficacy. Classification and regression tree analysis was used to explore thresholds. The correlation between trough concentration and AUC/MIC on the first day was analysed using a linear regression model.

RESULTS

PPK modelling involved 131 neonates. Postmenstrual age and current weight were included in the covariate analysis. Forty-eight patients were included in the efficacy analysis, 13 of whom were infected with MRSA. The best-performance PK/PD target for efficacy was AUC0-24h/MIC ≥ 331. The trough concentration was correlated with AUC0-24h/MIC (r2 = 0.32), but individual differences existed. AUC0-24h/MIC ranged up to 2.5-fold for a given trough concentration.

CONCLUSIONS

AUC0-24h/MIC ≥ 331 was the optimal target of vancomycin efficacy in neonates. The trough concentration was not a reliable predictor of efficacy and AUC0-24h/MIC. AUC-guided dosage adjustments are more valuable in clinical applications.

External evaluation of neonatal vancomycin population pharmacokinetic models: Moving from first-order equations to Bayesian-guided therapeutic monitoring

INTRODUCTION

Guidelines for vancomycin therapeutic monitoring recommend using a Bayesian approach with a population pharmacokinetic model to estimate the 24 h area under the concentration-time curve over first-order equations. Thus, we performed an external evaluation of population pharmacokinetic models of vancomycin in neonates and compared Bayesian results with those observed in clinical practice via pharmacokinetic equations to improve therapeutic monitoring by proposing optimized initial dosing nomograms and assessing the feasibility of reduced blood sampling strategies using the most predictive models.

METHODS

Models were identified from the literature and evaluated via an external neonatal population. A priori predictive performance was first assessed by prediction-based diagnostics, then by simulation-based diagnostics and a posteriori analyses only if deemed satisfactory; model-informed vancomycin exposure was also compared with reference first-order pharmacokinetic equations. The best-performing models were ultimately subjected to Monte Carlo simulations to develop new initial dosing nomograms offering the highest probability of achieving therapeutic target.

RESULTS

A total of 28 population pharmacokinetic models were evaluated in the external dataset, which includes 72 neonates and 380 vancomycin concentrations. Eleven models had an adequate predictive performance with bias ≤ ± 15% and imprecision ≤ 30%, while the Bayesian approach yielded over 75% agreement with reference exposure values in most cases. Nonetheless, Capparelli et al. and Mehrotra et al. models performed the best overall, showing the lowest imprecisions of 16.8% and 16.9%, respectively; both models recommended higher dosage regimens than the theoretical nomogram currently applied to favor therapeutic target attainment.

DISCUSSION

We externally evaluated numerous neonatal population pharmacokinetic models of vancomycin and used the most predictive ones to advocate new initial dosing nomograms. Clinical implementation of the Bayesian approach could reduce the time needed to reach therapeutic target and limit the number of blood samples in newborns compared with traditional pharmacokinetic equations.

Automated calculation and reporting of vancomycin area under the concentration-time curve: a simplified single-trough concentration-based equation approach

Vancomycin, a crucial antibiotic for Gram-positive bacterial infections, requires therapeutic drug monitoring (TDM). Contemporary guidelines advocate for AUC-based monitoring; however, using Bayesian programs for AUC estimation poses challenges. We aimed to develop and evaluate a simplified AUC estimation equation using a steady-state trough concentration (Ctrough) value. Utilizing 1,034 TDM records from 580 general hospitalized patients at a university-affiliated hospital in Ulsan, we created an equation named SSTA that calculates the AUC by applying Ctrough, body weight, and single dose as input variables. External validation included 326 records from 163 patients at a university-affiliated hospital in Seoul (EWUSH) and literature data from 20 patients at a university-affiliated hospital in Bangkok (MUSI). It was compared with other AUC estimation models based on the Ctrough, including a linear regression model (LR), a sophisticated model based on the first-order equation (VancoPK), and a Bayesian model (BSCt). Evaluation metrics, such as median absolute percentage error (MdAPE) and the percentage of observations within ±20% error (P20), were calculated. External validation using the EWUSH data set showed that SSTA, LR, VancoPK, and BSCt had MdAPE values of 6.4, 10.1, 6.6, and 7.5% and P20 values of 87.1, 82.5, 87.7, and 83.4%, respectively. External validation using the MUSI data set showed that SSTA, LR, and VancoPK had MdAPEs of 5.2, 9.4, and 7.2%, and P20 of 95, 90, and 95%, respectively. Owing to its decent AUC prediction performance, simplicity, and convenience for automated calculation and reporting, SSTA could be used as an adjunctive tool for the AUC-based TDM.

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.

Optimization of Vancomycin Initial Dosing Regimen in Neonates Using an Externally Evaluated Population Pharmacokinetic Model

BACKGROUND

Vancomycin therapeutic monitoring guidelines were revised in March 2020, and a population pharmacokinetics-guided Bayesian approach to estimate the 24-hour area under the concentration-time curve to the minimum inhibitory concentration ratio has since been recommended instead of trough concentrations. To comply with these latest guidelines, we evaluated published population pharmacokinetic models of vancomycin using an external dataset of neonatal patients and selected the most predictive model to develop a new initial dosing regimen.

METHODS

The models were identified from the literature and tested using a retrospective dataset of Canadian neonates. Their predictive performance was assessed using prediction- and simulation-based diagnostics. Monte Carlo simulations were performed to develop the initial dosing regimen with the highest probability of therapeutic target attainment.

RESULTS

A total of 144 vancomycin concentrations were derived from 63 neonates in the external population. Five of the 28 models retained for evaluation were found predictive with a bias of 15% and an imprecision of 30%. Overall, the Grimsley and Thomson model performed best, with a bias of -0.8% and an imprecision of 20.9%; therefore, it was applied in the simulations. A novel initial dosing regimen of 15 mg/kg, followed by 11 mg/kg every 8 hours should favor therapeutic target attainment.

CONCLUSIONS

A predictive population pharmacokinetic model of vancomycin was identified after an external evaluation and used to recommend a novel initial dosing regimen. The implementation of these model-based tools may guide physicians in selecting the most appropriate initial vancomycin dose, leading to improved clinical outcomes.

Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study

In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC0-24 , AUC24-48 , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUCSS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC24-48 , although the AUCSS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.

Model-informed precision dosing in vancomycin treatment

Introduction: While vancomycin remains a widely prescribed antibiotic, it can cause ototoxicity and nephrotoxicity, both of which are concentration-associated. Overtreatment can occur when the treatment lasts for an unnecessarily long time. Using a model-informed precision dosing scheme, this study aims to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for vancomycin to determine the optimal dosage regimen and treatment duration in order to avoid drug-induced toxicity. Methods: The data were obtained from electronic medical records of 542 patients, including 40 children, and were analyzed using NONMEM software. For PK, vancomycin concentrations were described with a two-compartment model incorporating allometry scaling. Results and discussion: This revealed that systemic clearance decreased with creatinine and blood urea nitrogen levels, history of diabetes and renal diseases, and further decreased in women. On the other hand, the central volume of distribution increased with age. For PD, C-reactive protein (CRP) plasma concentrations were described by transit compartments and were found to decrease with the presence of pneumonia. Simulations demonstrated that, given the model informed optimal doses, peak and trough concentrations as well as the area under the concentration-time curve remained within the therapeutic range, even at doses smaller than routine doses, for most patients. Additionally, CRP levels decreased more rapidly with the higher dose starting from 10 days after treatment initiation. The developed R Shiny application efficiently visualized the time courses of vancomycin and CRP concentrations, indicating its applicability in designing optimal treatment schemes simply based on visual inspection.

The Significance of Bayesian Pharmacokinetics in Dosing for Critically Ill Patients: A Primer for Clinicians Using Vancomycin as an Example

Antibiotic use is becoming increasingly challenging with the emergence of multidrug-resistant organisms. Pharmacokinetic (PK) alterations result from complex pathophysiologic changes in some patient populations, particularly those with critical illness. Therefore, antibiotic dose individualization in such populations is warranted. Recently, there have been advances in dose optimization strategies to improve the utilization of existing antibiotics. Bayesian-based dosing is one of the novel approaches that could help clinicians achieve target concentrations in a greater percentage of their patients earlier during therapy. This review summarizes the advantages and disadvantages of current approaches to antibiotic dosing, with a focus on critically ill patients, and discusses the use of Bayesian methods to optimize vancomycin dosing. The Bayesian method of antibiotic dosing was developed to provide more precise predictions of drug concentrations and target achievement early in therapy. It has benefits such as the incorporation of personalized PK/PD parameters, improved predictive abilities, and improved patient outcomes. Recent vancomycin dosing guidelines emphasize the importance of using the Bayesian method. The Bayesian method is able to achieve appropriate antibiotic dosing prior to the patient reaching the steady state, allowing the patient to receive the right drug at the right dose earlier in therapy.

External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps

Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring

The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area-under-the-curve with Bayesian estimation for therapeutic monitoring. As this is a modern concept in the practice of clinical pharmacy, the main objective of this review is to introduce the fundamentals of Bayesian estimation and its mathematical application as it relates to vancomycin therapeutic drug monitoring. In addition, we aim to identify pharmacokinetic (PK) software programs that incorporate Bayesian estimation for vancomycin dosing and to describe the PK models utilized in those software programs for the adult population. Twelve software programs that utilize Bayesian estimation were identified, which included: Adult and Pediatric Kinetics, Best Dose, ClinCalc, DoseMeRx, ID-ODS, InsightRx, MwPharm++, NextDose, PrecisePK, TDMx, Tucuxi, and VancoCalc. The software programs varied in the population PK models used as the Bayesian a priori. With the presence of various vancomycin Bayesian software programs, it is important to choose those that utilize PK models reflective of the specific patient population.

Vancomycin area under the curve versus trough only guided dosing and the risk of acute kidney injury: Systematic review and meta-analysis

Vancomycin is commonly used to treat methicillin-resistant Staphylococcus aureus infections and is known to cause nephrotoxicity. Previous Vancomycin Consensus Guidelines recommended targeting trough concentrations but the 2020 Guidelines suggest monitoring vancomycin area under the curve (AUC) given the reduced risk of acute kidney injury (AKI) at similar levels of efficacy. This meta-analysis compares vancomycin-induced AKI incidence using AUC-guided dosing strategies versus trough-based monitoring. Literature was queried from Medline (Ovid), Web of Science, and Google Scholar from database inception through November 5, 2021. Interventional or observational studies reporting the incidence of vancomycin-induced AKI between AUC- and trough-guided dosing strategies were included. In the primary analysis, the Vancomycin Consensus Guidelines definition for AKI was used if reported; otherwise, the Risk, Injury, and Failure; and Loss, and End-stage kidney disease (RIFLE) or Kidney Disease Improving Global Outcomes (KDIGO) definitions were used. The incidence of nephrotoxicity was evaluated between the two strategies using a Mantel-Haenszel random-effects model, and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Subgroup analyses for adjusted ORs and AKI definitions were performed. Heterogeneity was identified using Cochrane's Q test and I² statistics. A total of 10 studies with 4231 patients were included. AUC-guided dosing strategies were associated with significantly less vancomycin-induced AKI than trough-guided strategies [OR 0.625, 95% CI (0.469-0.834), p = 0.001; I²  = 25.476]. A subgroup analysis of three studies reporting adjusted ORs yielded similar results [OR 0.475, 95% CI (0.261-0.863), p = 0.015]. Stratification by AKI definition showed a significant reduction in AKI with the Vancomycin Consensus Guidelines definition [OR 0.552, 95% CI (0.341-0.894), p = 0.016] but failed to find significance in the alternative definitions. Area under the curve-guided dosing strategies are associated with a lower incidence of vancomycin-induced AKI versus trough-guided dosing strategies (GRADE, low). Limitations included the variety of AKI definitions and the potential for confounding bias.