Exploration of optimal dosing regimens of vancomycin in patients infected with methicillin-resistant Staphylococcus aureus by modeling and simulation

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.

Validation and development of population pharmacokinetic model of vancomycin using a real-world database from a nationwide free web application

INTRODUCTION

Vancomycin requires a population pharmacokinetic (popPK) model to estimate the area under the concentration-time curve (AUC), and an AUC-guided dosing strategy is necessary. This study aimed to develop a popPK model for vancomycin using a real-world database pooled from a nationwide web application (PAT).

METHODS

In this retrospective study, the PAT database between December 14, 2022 and April 6, 2023 was used to develop a popPK model. The model was validated and compared with six existing models based on the predictive performance of datasets from another PAT database and the Kumamoto University Hospital. The developed model determined the dosing strategy for achieving the target AUC.

RESULTS

The modeling populations consisted of 7146 (13,372 concentrations from the PAT database), 3805 (7540 concentrations from the PAT database), and 783 (1775 concentrations from Kumamoto University Hospital) individuals. A two-compartment popPK model was developed that incorporated creatinine clearance as a covariate for clearance and body weight for central and peripheral volumes of distribution. The validation demonstrated that the popPK model exhibited the smallest mean absolute prediction error of 5.07, outperforming others (ranging from 5.10 to 5.83). The dosing strategies suggested a first dose of 30 mg/kg and maintenance doses adjusted for kidney function and age.

CONCLUSIONS

This study demonstrated the updating of PAT through the validation and development of a popPK model using a vast amount of data collected from anonymous PAT users.

Selecting the Best Pharmacokinetic Models for a Priori Model-Informed Precision Dosing with Model Ensembling

BACKGROUND AND OBJECTIVE

When utilizing population pharmacokinetic (popPK) models for a priori dosage individualization, selecting the best model is crucial to obtain adequate doses. We developed and evaluated several model-selection and ensembling methods, using external evaluation on the basis of therapeutic drug monitoring (TDM) samples to identify the best (set of) models per patient for a priori dosage individualization.

METHODS

PK data and models describing both hospitalized patients (n = 134) receiving continuous vancomycin (26 models) and patients (n = 92) receiving imatinib in an outpatient setting (12 models) are included. Target attainment of four model-selection methods was compared with standard dosing: the best model based on external validation, uninformed model ensembling, model ensembling using a weighting scheme on the basis of covariate-stratified external evaluation, and model selection using covariates in decision trees that were subsequently ensembled.

RESULTS

Overall, the use of PK models improved the proportion of patients exposed to concentrations within the therapeutic window for both cohorts. Relative improvement of proportion on target for best model, unweighted, weighted, and decision trees were - 7.0%, 2.3%, 11.4%, and 37.0% (vancomycin method-development); 23.2%, 7.9%, 15.6%, and, 77.2% (vancomycin validation); 40.7%, 50.0%, 59.5%, and 59.5% (imatinib method-development); and 19.0%, 28.5%, 38.0%, and 23.8% (imatinib validation), respectively.

CONCLUSIONS

The best (set of) models per patient for a priori dosage individualization can be identified using a relatively small set of TDM samples as external evaluation. Adequately performing popPK models were identified while also excluding poor-performing models. Dose recommendations resulted in more patients within the therapeutic range for both vancomycin and imatinib. Prospective validation is necessary before clinical implementation.

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.

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data

Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations. The increasing availability of electronic health record (EHR) data offers an opportunity to develop TDM models for real-world patient populations. Here, we present a deep learning-based pharmacokinetic prediction model for vancomycin (PK-RNN-V E) using a large EHR dataset of 5,483 patients with 55,336 vancomycin administrations. PK-RNN-V E takes the patient's real-time sparse and irregular observations and offers dynamic predictions. Our results show that RNN-PK-V E offers a root mean squared error (RMSE) of 5.39 and outperforms the traditional Bayesian model (VTDM model) with an RMSE of 6.29. We believe that PK-RNN-V E can provide a pharmacokinetic model for vancomycin and other antimicrobials that require TDM.

Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review

Pharmacokinetics and pharmacodynamics are areas in pharmacology related to different themes in the pharmaceutical sciences, including therapeutic drug monitoring and different stages of drug development. Although the knowledge of these disciplines is essential, they have historically been treated separately. While pharmacokinetics was limited to describing the time course of plasma concentrations after administering a drug-dose, pharmacodynamics describes the intensity of the response to these concentrations. In the last decades, the concept of pharmacokinetic/pharmacodynamic modeling (PK/PD) emerged, which seeks to establish mathematical models to describe the complete time course of the dose-response relationship. The integration of these two fields has had applications in optimizing dose regimens in treating antibacterial and antifungals. The anti-infective PK/PD models predict the relationship between different dosing regimens and their pharmacological activity. The reviewed studies show that PK/PD modeling is an essential and efficient tool for a better understanding of the pharmacological activity of antibacterial and antifungal agents.

Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring

Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a classifier was created to choose suitable models among them for patients. The classifier was trained using 900,000 virtual patients, and its performance was evaluated using 9000 and 4000 virtual patients for internal and external validation, respectively. The accuracy of the classifier ranged from 20.8% to 71.6% in the simulation scenarios. TDM using the ML classifier showed stable results compared with that using single models without the ML classifier. Based on these results, we have discussed further development of TDM using ML. In conclusion, we developed and evaluated a new method for selecting a PK model for TDM using ML. With more information, such as on additional PK model reporting and ML model improvement, this method can be further enhanced.

Population pharmacokinetics of vancomycin in critically ill adult patients receiving extracorporeal membrane oxygenation (an ASAP ECMO study)

Our study aimed to describe the population pharmacokinetics (PK) of vancomycin in critically ill patients receiving extracorporeal membrane oxygenation (ECMO), including those receiving concomitant renal replacement therapy (RRT). Dosing simulations were used to recommend maximally effective and safe dosing regimens. Serial vancomycin plasma concentrations were measured and analysed using a population PK approach on Pmetrics®. The final model was used to identify dosing regimens that achieved target exposures of area under the curve (AUC_0-24) of 400 - 700 mg·h/L at steady state. Twenty-two patients were enrolled, of which 11 patients received concomitant RRT. In the non-RRT patients, the median creatinine clearance (CrCL) was 75 mL/min and the mean daily dose of vancomycin was 25.5 mg/kg. Vancomycin was well described in a two-compartment model with CrCL, the presence of RRT and total body weight found as significant predictors of clearance and central volume of distribution (V_c). The mean vancomycin renal clearance and V_c were 3.20 L/h and 29.7 L respectively, while the clearance for patients on RRT was 0.15 L/h. ECMO variables did not improve the final covariate model. We found that recommended dosing regimens for critically ill adult patients not on ECMO can be safely and effectively used in those on ECMO. Loading doses of at least 25 mg/kg followed by maintenance doses of 12.5 - 20 mg/kg 12-hourly are associated with a 97 - 98% probability of efficacy and 11 - 12% probability of toxicity, in patients with normal renal function. Therapeutic drug monitoring along with reductions in dosing are warranted for patients with renal impairment and those with concomitant RRT.

A Robust Statistical Approach to Analyse Population Pharmacokinetic Data in Critically Ill Patients Receiving Renal Replacement Therapy

BACKGROUND AND AIM

Current approaches to antibiotic dose determination in critically ill patients requiring renal replacement therapy are primarily based on the assessment of highly heterogeneous data from small number of patients. The standard modelling approaches limit the scope of constructing robust confidence boundaries of the distribution of pharmacokinetics (PK) parameters, especially when the evaluation of possible association of demographic and clinical factors at different levels of the distribution of drug clearance is of interest. Commonly used compartmental models generally construct the inferences through a linear or non-linear mean regression, which is inadequate when the distribution is skewed, multi-modal or effected by atypical observation. In this study, we discuss the statistical challenges in robust estimation of the confidence boundaries of the PK parameters in the presence of highly heterogenous patient characteristics.

METHODS

A novel stepwise approach to evaluate the confidence boundaries of PK parameters is proposed by combining PK modelling with mixed-effects quantile regression (MEQR) methods.

RESULTS

This method allows the assessment demographic and clinical factors' effects at any arbitrary quantiles of the outcome of interest, without restricting assumptions on the distributions. The MEQR approach allows us to investigate if the levels of association of the covariates are different at low, medium or high concentration.

CONCLUSIONS

This methodological assessment is deemed as a background initial approach to support the development of a class of statistical algorithm in constructing robust confidence intervals of PK parameters which can be used for developing an optimised antibiotic dosing guideline for critically ill patients requiring renal replacement therapy.

Pharmacokinetics and Extracorporeal Membrane Oxygenation in Adults: A Literature Review

Extracorporeal membrane oxygenation is a rapidly emerging treatment for respiratory or cardiac failure and is used as a bridge to recovery, transplant, or destination therapy. Adult patients receiving extracorporeal membrane oxygenation also receive significant amounts of pharmacotherapy. Although the body of literature on extra-corporeal membrane oxygenation in general is extensive, only a few publications focus on pharmacokinetic changes related to extracorporeal membrane oxygenation in adults. Understanding pharmacokinetics in adult patients receiving extracorporeal membrane oxygenation is important to correctly select and dose medications in this patient population. This article reviews published studies of the effects of extracorporeal membrane oxygenation on pharmacokinetics in adults.

Using a Vancomycin PBPK Model in Special Populations to Elucidate Case-Based Clinical PK Observations

Simultaneous changes in several physiological factors may contribute to the large pharmacokinetic (PK) variability of vancomycin. This study was designed to systematically characterize the effects of multiple physiological factors to the altered PK of vancomycin observed in special populations. A vancomycin physiologically based pharmacokinetic (PBPK) model was developed as a PK simulation platform to quantitatively assess the effects of changes in physiologies to the PK profiles. The developed model predicted the concentration-time profiles in healthy adults and diseased patients. The implementation of developmental changes in both renal and non-renal elimination pathways to the pediatric model improved the predictability of vancomycin clearance. Simulated PK profiles with a 50% decrease in cardiac output (peak plasma concentration (C_max ), 59.9 ng/mL) were similar to those observed in patients before bypass surgery (C_max , 55.1 ng/mL). The PBPK modeling of vancomycin demonstrated its potential to provide mechanistic insights into the altered disposition observed in patients who have changes in multiple physiological factors.

Antibiotic consumption in laboratory confirmed vs. non-confirmed bloodstream infections among very low birth weight neonates in Poland

BACKGROUND

Newborns are a population in which antibiotic consumption is extremely high. Targeted antibiotic therapy should help to reduce antibiotics consumption. The aim of this study was an assessment of antibiotic usage in bloodstream infections treatment in the Polish Neonatology Surveillance Network (PNSN) and determining the possibility of applying this kind of data in infection control, especially for the evaluation of standard methods of microbiological diagnostics.

METHODS

Data were collected between 01.01.2009 and 31.12.2013 in five teaching NICUs from the PNSN. The duration of treatment in days (DOT) and the defined daily doses (DDD) were used for the assessment of antibiotics consumption.

RESULTS

The median DOT for a single case of BSI amounted to 8.0 days; whereas the median consumption expressed in DDD was 0.130. In the case of laboratory confirmed BSI, median DOT was 8 days, and consumption-0.120 DDD. Median length of therapy was shorter for unconfirmed cases: 7 days, while the consumption of antibiotics was higher-0.140 DDD (p < 0.0001). High consumption of glycopeptides expressed in DOTs was observed in studied population, taking into account etiology of infection.

CONCLUSIONS

Even application of classical methods of microbiological diagnostics significantly reduces the consumption of antibiotics expressed by DDD. However, the high consumption of glycopeptides indicates the necessity of applying rapid diagnostic assays. Nevertheless, the assessment of antibiotic consumption in neonatal units represents a methodological challenge and requires the use of different measurement tools.

A Population Pharmacokinetic Model for Vancomycin in Adult Patients Receiving Extracorporeal Membrane Oxygenation Therapy

The literature on the pharmacokinetics of vancomycin in patients undergoing extracorporeal membrane oxygenation (ECMO) therapy is sparse. A population pharmacokinetic (PK) model for vancomycin in ECMO patients was developed using a nonlinear mixed effects modeling on the concentration–time profiles of 14 ECMO patients who received intravenous vancomycin. Model selection was based on log‐likelihood criterion, goodness of fit plots, and scientific plausibility. Identification of covariates was done using a full covariate model approach. The pharmacokinetics of vancomycin was adequately described with a two‐compartment model. Parameters included clearance of 2.83 L/hr, limited central volume of distribution 24.2 L, and low residual variability 0.67%. Findings from the analysis suggest that standard dosing recommendations for vancomycin in non‐ECMO patients are adequate to achieve therapeutic trough concentrations in ECMO patients. This further shows that ECMO minimally affects the PK of vancomycin in adults including in higher‐weight patients.

Towards Rational Dosing Algorithms for Vancomycin in Neonates and Infants Based on Population Pharmacokinetic Modeling

Because of the recent awareness that vancomycin doses should aim to meet a target area under the concentration-time curve (AUC) instead of trough concentrations, more aggressive dosing regimens are warranted also in the pediatric population. In this study, both neonatal and pediatric pharmacokinetic models for vancomycin were externally evaluated and subsequently used to derive model-based dosing algorithms for neonates, infants, and children. For the external validation, predictions from previously published pharmacokinetic models were compared to new data. Simulations were performed in order to evaluate current dosing regimens and to propose a model-based dosing algorithm. The AUC/MIC over 24 h (AUC24/MIC) was evaluated for all investigated dosing schedules (target of >400), without any concentration exceeding 40 mg/liter. Both the neonatal and pediatric models of vancomycin performed well in the external data sets, resulting in concentrations that were predicted correctly and without bias. For neonates, a dosing algorithm based on body weight at birth and postnatal age is proposed, with daily doses divided over three to four doses. For infants aged <1 year, doses between 32 and 60 mg/kg/day over four doses are proposed, while above 1 year of age, 60 mg/kg/day seems appropriate. As the time to reach steady-state concentrations varies from 155 h in preterm infants to 36 h in children aged >1 year, an initial loading dose is proposed. Based on the externally validated neonatal and pediatric vancomycin models, novel dosing algorithms are proposed for neonates and children aged <1 year. For children aged 1 year and older, the currently advised maintenance dose of 60 mg/kg/day seems appropriate.

Epidemiology, antibiotic consumption and molecular characterisation of Staphylococcus aureus infections--data from the Polish Neonatology Surveillance Network, 2009-2012

Background

Our aim was to determine and characterize S. aureus (SA) isolated from infections in newborns for antibiotic resistance, virulence factors, genotypes, epidemiology and antibiotic consumption.

Methods

Prospective surveillance of infections was conducted. Data about antibiotic treatment were analyzed. Antimicrobial susceptibility was assessed. PCR amplification was used to detect resistance and virulence genes. Typing methods such as PFGE, spa-typing and SCCmec were used.

Results

SA was found to be associated with 6.5% of infections. Methicillin-Resistant Staphylococcus aureus accounted for 32.8% of SA-infections. An incidence of MRSA-infections was 1.1/1000 newborns. MRSA-infections were diagnosed significantly earlier than MSSA-infections in these newborns (14th day vs. 23rd day (p = 0.0194)). MRSA-infections increased the risk of newborn’s death. Antibiotic consumption in both group was similar, but a high level of glycopeptides-usage for MSSA infections was observed.

In the MRSA group, more strains were resistant to erythromycin, clindamycin, gentamicin and amikacin than in the MSSA group. Hla gene was present in 93.9% of strains, and seg and sei in 65.3% of strains, respectively. One dominant clone was found among the 14 MRSA isolates. Fifteen strains belonging to SCCmec type IV were spa-t015 and one strain belonging to SCCmec type V was spa-t011.

Conclusions

Results obtained in the study point at specific epidemiological situation in Polish NICU (more detailed studies are recommended).

High usage of glycopeptides in the MSSA infections treatment indicates the necessity of antimicrobial stewardship improvement and introducing molecular screening for early identification of infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-0890-3) contains supplementary material, which is available to authorized users.