Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations

Monitoring strategies and vancomycin-associated acute kidney injury in patients treated at home

OBJECTIVES

The 2020 vancomycin consensus guidelines recommend AUC-guided dosing over trough-based dosing to decrease nephrotoxicity. This study was performed to add data comparing these dosing methods in the outpatient setting.

METHODS

This retrospective cohort study compared trough-guided versus AUC-guided dosing in patients receiving vancomycin through two home infusion pharmacies (HIPs). Multivariate analysis was performed to report adjusted relative risks, adjusting for patient demographics and clinical characteristics. Eligible patients were ≥18 years old, had an absolute neutrophil count of ≥1000 cells/mm3, a baseline serum creatinine of <2.0 mg/dL at HIP intake, and ≥7 days of IV vancomycin at home. Primary outcome was rate of acute kidney injury (AKI) events, defined as the number of AKI events per treatment days. Secondary outcomes were rate of 30 day hospital readmission and number of HIP interventions (vancomycin dose changes).

RESULTS

Six hundred and sixty patients were included (303 trough, 357 AUC). The mean number of AKI events was 0.84 per treatment day for trough-guided versus 0.63 for AUC-guided dosing (P = 0.11). In adjusted models, there were no significant associations between the exposure and AKI events [relative risk (RR) = 0.8, 95% CI 0.5-1.2, P = 0.26], 30 day hospital readmissions (RR 1.0, 95% CI 0.8-1.3, P = 0.71) or number of pharmacy interventions (RR = 1.0, 95% CI 0.9-1.2, P = 0.67).

CONCLUSIONS

There was no significant difference in AKI rates among patients receiving vancomycin via trough- or AUC-guided monitoring and dosing through a HIP. Further evaluation is needed to determine how to improve AKI rates using AUC-guided monitoring and dosing among patients receiving vancomycin therapy at home.

Exploring Health Care Professionals' Engagement With a Precision Dosing Calculator and Supporting Clinical Information: Insights From an Eye-Tracking and Usability Study

BACKGROUND

Pharmacokinetic-based dosing calculators for individualized drug dosing remain underutilized in clinical practice, often due to poor usability and a lack of user-centered design. Understanding how health care professionals interact with these tools can inform design strategies and enhance usability.

METHODS

Health care professionals wore eye-tracking glasses while using a codesigned vancomycin dosing calculator with supporting clinical information to complete example clinical scenarios. Eye-tracking data were collected for 23 predefined areas of interest, and fixation sequences were analyzed. A Post-Study System Usability Questionnaire was administered to assess the tool's perceived usability.

RESULTS

Eleven pharmacists and three doctors participated in the study. The highest average dwell times were recorded for the pharmacokinetic plot, dosage regimen selection, dosing history, drug concentrations, and the area under the concentration-time curve and dose visualization area. Participants generally viewed patient demographic information first and pharmacokinetic and dosage regimen information last. Considerable heterogeneity was observed among participants' fixation sequences, with frequent eye movements between key areas, particularly between the pharmacokinetic plot and dosage regimen selection, and between dosing history and drug concentrations. Participants expressed a preference for these elements to be positioned close together.

CONCLUSIONS

Understanding how health care professionals interact with decision support systems is essential for developing user-friendly tools that align with clinical workflows. Eye-tracking data provided valuable insights into user engagement patterns with the dosing calculator and clinical information interface. These insights will guide future design strategies to address usability barriers that limit the utilization of dosing calculators in clinical practice and promote the implementation of individualized drug dosing.

A Pooled Pharmacokinetic Analysis for Piperacillin/Tazobactam Across Different Patient Populations: From Premature Infants to the Elderly

BACKGROUND AND OBJECTIVES

The pharmacokinetics (PK) of piperacillin/tazobactam (PIP/TAZ) is highly variable across different patient populations and there are controversies regarding non-linear elimination as well as the fraction unbound of PIP (fUNB_PIP). This has led to a plethora of subgroup-specific models, increasing the risk of misusing published models when optimising dosing regimens. In this study, we aimed to develop a single model to simultaneously describe the PK of PIP/TAZ in diverse patient populations and evaluate the current dosing recommendations by predicting the PK/pharmacodynamics (PD) target attainment throughout life.

METHODS

Population PK models were separately built for PIP and TAZ based on data from 13 studies in various patient populations. In the development of those single-drug models, postnatal age (PNA), postmenstrual age (PMA), total body weight (TBW), height, and serum creatinine (SCR) were tested as covariates. Subsequently, a combined population PK model was established and the correlations between the PK of PIP and TAZ were tested. Monte Carlo simulations were performed based on the final combined model to evaluate the current dosing recommendations.

RESULTS

The final combined model for PIP/TAZ consisted of four compartments (two for each drug), with covariates including TBW, PMA, and SCR. For a 70-kg, 35-year-old patient with SCR of 0.83 mg L-1, the PIP values for V1, CL, V2 and Q2 were 10.4 L, 10.6 L h-1, 11.6 L and 15.2 L h-1, respectively, and the TAZ values were 10.5 L, 9.58 L h-1, 13.7 L and 16.8 L h-1, respectively. The CL for both drugs show maturation in early life, reaching 50% at 54.2 weeks PMA. With advancing age, CL of TAZ declines to 50% at 61.6 years PMA, whereas CL of PIP declines more slowly, reaching 50% at 89.1 years PMA. The fUNB_PIP was estimated as 64.5% and non-linear elimination was not supported by our data. The simulation results indicated considerable differences in PK/PD target attainment for different patient populations under current recommended dosing regimens.

CONCLUSIONS

We developed a combined population PK model for PIP/TAZ across a broad range of patients covering the extremes of patient characteristics. This model can be used as a robust a priori model for Bayesian forecasting to achieve individualised dosing. The simulations indicate that adjustments based on the allometric theory as well as maturation and decline of CL of PIP may help the current dosing recommendations to provide consistent target attainment across patient populations.

Therapeutic Monitoring of Vancomycin Implemented by Eremomycin ELISA

BACKGROUND/OBJECTIVES

Due to a narrow therapeutic window, side-effects, toxicities, and individual pharmacokinetics (PK) variability, WHO classifies vancomycin (VCM) as a "watch antibiotic" whose use should be monitored to improve clinical effectiveness. Availability and ease of use have made the immunoassay technique the basic tool for the therapeutic drug monitoring (TDM) of VCM concentrations.

METHODS

The present study describes the development of a TDM tool for VCM based on anti-eremomycin (ERM) antibody enzyme-linked immunosorbent assay (ELISA).

RESULTS

The optimized assay format based on coating a BSA-VCM conjugate allowed for the equal recognition of both VCM and ERM (100 and 104%) and was not influenced by concomitant antibiotics. Among the sample pretreatments studied, acetonitrile deproteinization was preferred to effectively remove the most likely matrix interferences and to provide 75-96% VCM recovery in the range of 3-30 mg/L, ensuring reliable determination of the key PK parameter, Ctrough. Higher peak concentrations were measured in more diluted samples. Several inflammatory indices, biochemical markers, and key proteins significantly different from normal in critically ill patients were investigated as assay interferers and were found not to interfere with VCM analysis. Serum samples (n = 108) from patients (n = 4) with extensive burn injuries treated with combined antibiotic therapy were analyzed for VCM using the developed assay and confirmed by LC-MS/MS, demonstrating good agreement.

CONCLUSIONS

The approach used shows that the same analytical instrument is suitable for measuring structurally related analytes and is fully adequate for their therapeutic monitoring. Suboptimal exposure based on Ctrough values obtained with standard dosing regimens supports the use of TDM in these patients.

Pharmacokinetics of Vancomycin in Healthy Korean Volunteers and Monte Carlo Simulations to Explore Optimal Dosage Regimens in Patients with Normal Renal Function

Background/Objectives: To date, population pharmacokinetic (PK) studies of vancomycin on healthy Korean adults have not been conducted. This study aimed to investigate the PK properties of vancomycin in healthy volunteers and to identify optimal dosing regimens based on the area under the concentration-time curve (AUC) in adult patients with normal renal function. Methods: We conducted a prospective clinical study, analysing PK samples from 12 healthy participants using noncompartmental analysis and non-linear mixed-effects modelling. The population PK parameters derived were employed in Monte Carlo simulations to evaluate the adequacy of the current dosing regimen and to formulate dosing recommendations. Results: The PK profiles were optimally described by a two-compartment model, with body weight and age as significant covariates affecting total clearance. The simulations indicated that to achieve a therapeutic target-defined as an AUC at steady-state over 24 h of 400-600 mg·h/L-daily doses ranging from 60 to 70 mg/kg are necessary in adults with normal renal function. Conclusions: This study underscores the need to actively adjust dosage and administration based on a vancomycin PK model that adequately reflects the demographic characteristics of patients to meet both safety and efficacy standards.

A multicentric, randomized, controlled clinical trial to study the impact of bedside model-informed precision dosing of vancomycin in critically ill children-BENEFICIAL trial

BACKGROUND

Vancomycin is a commonly prescribed antibiotic to treat serious Gram-positive infections in children. The efficacy of vancomycin is known to be directly related to the pharmacokinetic/pharmacodynamic (PK/PD) index of the area under the concentration-time curve (AUC) divided by the minimal inhibitory concentration (MIC) of the pathogen. In most countries, steady-state plasma concentrations are used as a surrogate parameter for this target AUC/MIC, but this practice has some drawbacks. Hence, AUC-based dosing using model-informed precision dosing (MIPD) tools has been proposed for increasing the target attainment rate and reducing vancomycin-related nephrotoxicity. Solid scientific evidence for these claimed benefits is lacking in children. This randomized controlled trial aims to investigate the large-scale utility of MIPD dosing of vancomycin in critically ill children.

METHODS

Participants from 14 neonatal intensive care, pediatric intensive care, and pediatric hemo-oncology ward units from 7 hospitals are randomly allocated to the intervention or standard-of-care comparator group. In the intervention group, a MIPD dosing calculator is used for AUC-based dosing, in combination with extra sampling for therapeutic drug monitoring in the first hours of treatment, as compared to standard-of-care. An AUC24h between 400 and 600 is targeted, assuming an MIC of 1 mg/L. Patients in the comparator group receive standard-of-care dosing and monitoring according to institutional guidelines. The primary endpoint is the proportion of patients reaching the target AUC24h/MIC of 400-600 between 24 and 48 h after the start of vancomycin treatment. Secondary endpoints are the proportion of patients with (worsening) acute kidney injury during vancomycin treatment, the proportion of patients reaching target AUC24h/MIC of 400-600 between 48 and 72 h after the start of vancomycin treatment, time to clinical cure, ward unit length-of-stay, hospital length-of-stay, and 30-day all-cause mortality.

DISCUSSION

This trial will clarify the propagated benefits and provide new insights into how to optimally monitor vancomycin treatment in critically ill children.

TRIAL REGISTRATION

Eudract number: 2019-004538-40. Registered on 2020-09-08 ClinicalTrials.gov NCT046666948. Registered on 2020-11-28.

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.

Impacts of age and BMI on vancomycin model choice in a Bayesian software: Lessons from a very large multi-site retrospective study

BACKGROUND

Model-informed precision dosing (MIPD) optimizes drug doses based on pharmacokinetic (PK) model predictions, necessitating careful selection of models tailored to patient characteristics. This study evaluates the predictive performance of various vancomycin PK models across diverse age and BMI categories, drawing insights from a large multi-site database.

METHODS

Adults receiving vancomycin intravenous therapy at United States health systems between January 1, 2022, and December 31, 2023, were included. Patient demographics, vancomycin administration records, and therapeutic drug monitoring levels (TDMs) were collected from the InsightRX database. Age and body mass index (BMI)-based subgroups were formed to assess model performance, with predictions made iteratively. The optimal model for each age-BMI subgroup was chosen based on predefined criteria: models were filtered for mean percentage error (MPE) ≤ 20% and normalized root mean squared error (RMSE) < 8 mg/L, and then the most accurate among them was selected.

RESULTS

A total of 384,876 treatment courses across 155 US health systems were analyzed, contributing 841,604 TDMs. Eleven models were compared, showing varying accuracy across age-BMI categories (41%-73%), with higher accuracy observed once TDMs were available for Bayesian estimates of individual PK parameters. Models performed more poorly in younger adults compared to older adults, and the optimal model differed depending on age-BMI categories and prediction methods. Notably, in the a priori period, the Colin model performed best in adults aged 18-64 years across most BMI categories; the Goti/Tong model performed best in the older, non-obese adults; and the Hughes model performed best in many of the obese categories.

CONCLUSION

Our study identifies specific vancomycin PK models that demonstrate superior predictions across age-BMI categories in MIPD applications. Our findings underscore the importance of tailored model selection for vancomycin management, especially highlighting the need for improved models in younger adult patients. Further research into the clinical implications of model performance is warranted to enhance patient care outcomes.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity

BACKGROUND

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity.

METHODS

Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset.

RESULTS

In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients).

CONCLUSIONS

Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Determination of a vancomycin nephrotoxicity threshold and assessment of target attainment in hematology patients

An area-under-the-curve (AUC24)-based approach is recommended to guide vancomycin therapeutic drug monitoring (TDM), yet trough concentrations are still commonly used despite associated risks. A definitive toxicity target is lacking, which is important for hematology patients who have a higher risk of nephrotoxicity. The aims were to (1) assess the impact of trough-based TDM on acute kidney injury (AKI) incidence, (2) establish a vancomycin nephrotoxicity threshold, and (3) evaluate the proportion of hematology patients achieving vancomycin therapeutic targets. Retrospective data was collected from 100 adult patients with a hematological malignancy or aplastic anemia who received vancomycin between April 2020 and January 2021. AKI occurrence was determined based on serum creatinine concentrations, and individual pharmacokinetic parameters were estimated using a Bayesian approach. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of pharmacokinetic indices to predict AKI occurrence. The proportion of patients who achieved target vancomycin exposure was evaluated based on an AUC24/MIC ≥400 and the determined toxicity threshold. The incidence of AKI was 37%. ROC curve analysis indicated a maximum AUC24 of 644 mg.h/L over the treatment period was an important predictor of AKI. By Day 4 of treatment, 29% of treatment courses had supratherapeutic vancomycin exposure, with only 62% of courses achieving AUC24 targets. The identified toxicity threshold supports an AUC24 target range of 400-650 mg.h/L, assuming an MIC of 1 mg/L, to optimize vancomycin efficacy and minimize toxicity. This study highlights high rates of AKI in this population and emphasizes the importance of transitioning from trough-based TDM to an AUC-based approach to improve clinical outcomes.

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.

Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates

BACKGROUND AND OBJECTIVE

Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital.

METHODS

Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero.

RESULTS

A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) - 7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias - 0.23% (95% CI - 1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI - 8.2, 19.1%), and rRMSE 38.3%) approaches.

CONCLUSIONS

The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.

Impact of model-informed precision dosing in adults receiving vancomycin via continuous infusion: a randomized, controlled clinical trial

BACKGROUND

Vancomycin is a commonly prescribed antibiotic to treat gram-positive infections. The efficacy of vancomycin is known to be directly related to the pharmacokinetic/pharmacodynamic (PK/PD) index of the area under the concentration-time curve (AUC) divided by the minimal inhibitory concentration (MIC) of the pathogen. However, in most countries, steady-state plasma concentrations are used as a surrogate parameter of target AUC/MIC, but this practice has some drawbacks. Hence, direct AUC-guided monitoring of vancomycin using model-informed precision dosing (MIPD) tools has been proposed for earlier attainment of target concentrations and reducing vancomycin-related nephrotoxicity. However, solid scientific evidence for these benefits in clinical practice is still lacking. This randomized controlled trial (RCT) aims to investigate the clinical utility of MIPD dosing of vancomycin administered via continuous infusion in hospitalized adults.

METHODS

Participants from 11 wards at two Belgian hospitals are randomly allocated to the intervention group or the standard-of-care comparator group. In the intervention group, clinical pharmacists perform dose calculations using CE-labeled MIPD software and target an AUC24h of 400 to 600 mg × h/L, whereas patients in the comparator group receive standard-of-care dosing and monitoring according to the institutional guidelines. The primary endpoint is the proportion of patients reaching the target AUC24h/MIC of 400-600 between 48 and 72 h after start of vancomycin treatment. Secondary endpoints are the proportion of patients with (worsening) acute kidney injury (AKI) during and until 48 h after stop of vancomycin treatment, the proportion of patients reaching target AUC24h/MIC of 400-600 between 72 and 96 h after start of vancomycin treatment, and the proportion of time within the target AUC24h/MIC of 400-600.

DISCUSSION

This trial will clarify the propagated benefits and provide new insights into how to optimally monitor vancomycin treatment.

TRIAL REGISTRATION

EudraCT number: 2021-003670-31. Registered June 28, 2021.

CLINICALTRIALS

gov identifier: NCT05535075. Registered September 10, 2022. Protocol version 3, protocol date: April 21, 2023.

Model-Informed Vancomycin Dosing Optimization to Address Delayed Renal Maturation in Infants and Young Children with Critical Congenital Heart Disease

Ensuring safe and effective drug therapy in infants and young children often requires accounting for growth and organ development; however, data on organ function maturation are scarce for special populations, such as infants with congenital diseases. Children with critical congenital heart disease (CCHD) often require multiple staged surgeries depending on their age and disease severity. Vancomycin (VCM) is used to treat postoperative infections; however, the standard pediatric dose (60-80 mg/kg/day) frequently results in overexposure in children with CCHD. In this study, we characterized the maturation of VCM clearance in pediatric patients with CCHD and determined the appropriate dosing regimen using population pharmacokinetic (PK) modeling and simulations. We analyzed 1,254 VCM serum concentrations from 152 postoperative patients (3 days-13 years old) for population PK analysis. The PK model was developed using a two-compartment model with allometrically scaled body weight, estimated glomerular filtration rate (eGFR), and postmenstrual age as covariates. The observed clearance in patients aged ≤ 1 year and 1-2 years was 33% and 40% lower compared with that of non-CCHD patients, respectively, indicating delayed renal maturation in patients with CCHD. Simulation analyses suggested VCM doses of 25 mg/kg/day (age ≤ 3 months, eGFR 40 mL/min/1.73 m2 ) and 35 mg/kg/day (3 months < age ≤ 3 years, eGFR 60 mL/min/1.73 m2 ). In conclusion, this study revealed delayed renal maturation in children with CCHD, could be due to cyanosis and low cardiac output. Model-informed simulations identified the lower VCM doses for children with CCHD compared with standard pediatric guidelines.

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.

Analytical and Non-Analytical Variation May Lead to Inappropriate Antimicrobial Dosing in Neonates: An In Silico Study

BACKGROUND

Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and nonanalytical factors potentially influence dosing recommendations. This study aimed to determine the impact of analytical variation (imprecision and bias) and nonanalytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation, and dosing errors) on neonatal antimicrobial dosing recommendations.

METHODS

Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.

RESULTS

Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).

CONCLUSIONS

Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.

Vancomycin Clearance in Obese Adults is not Predictive of Clearance in Obese Adolescents

BACKGROUND AND OBJECTIVE

Contradictory pharmacokinetic (PK) results have been observed between obese adults and obese adolescents, with absolute clearance (CL) reported to be either unaltered, lower, or higher in obese adolescents compared to obese adults. This study investigates the PK of vancomycin in adolescents and adults who are overweight or obese.

METHODS

Data from 125 overweight and obese adolescents (aged 10-18 years, weight 28.3-188 kg) and 81 overweight and obese adults (aged 29-88 years, weight 66.7-143 kg) were analysed using population PK modelling. In addition to age, sex, renal function estimates, and regular weight descriptors, we evaluated standard weight (WT_standard, defined as weight for length, age, and sex in adolescents and weight for length in adults) and excess weight (WT_excess, defined as total body weight (TBW) minus WT_standard) as covariates in order to distinguish between weight resulting from length versus weight resulting from obesity.

RESULTS

Analyzing adolescents and adults together, vancomycin CL was found to increase with TBW and decrease with increasing age (p < 0.001). A covariate analysis investigating adolescents and adults separately found that vancomycin CL increased with WT_standard in adolescents and adults, albeit with different functions, with adolescents having a higher CL per WT_standard than adults. Moreover, in this separate model, adolescent males had 21% higher CL than adolescent females of the same WT_standard, while in adults, CL decreased with increasing age (p < 0.001).

CONCLUSION

There are apparent differences in vancomycin CL in overweight and obese adults versus overweight and obese adolescents, implying that dosing of vancomycin cannot be directly extrapolated between these populations.

Predicting the Area under the Plasma Concentration-Time Curve (AUC) for First Dose Vancomycin Using First-Order Pharmacokinetic Equations

To treat critically ill patients, early achievement of the target area under the plasma concentration-time curve/minimum inhibitory concentration (AUC/MIC) in the first 24 h is recommended. However, accurately calculating the AUC before steady state is an obstacle to this goal. A first-order pharmacokinetic equation to calculate vancomycin AUC after a first dose of vancomycin has never been studied. We sought to estimate AUC using two first-order pharmacokinetic equations, with different paired concentration time points, and to compare these to the actual first dose vancomycin AUC calculated by the linear-log trapezoid rule as a reference. The equations were validated using two independent intensive first dose vancomycin concentration time data sets, one from 10 adults and another from 14 children with severe infection. The equation with compensation for the alpha distribution phase using a first vancomycin serum concentration from 60 to 90 min and the second concentration from 240 to 300 min after the completed infusion showed good agreement and low bias of calculated AUC, with mean differences <5% and Lin's correlation coefficient >0.96. Moreover, it gave an excellent correlation with Pearson's r > 0.96. Estimating the first dose vancomycin AUC calculated using this first-order pharmacokinetic equation is both reliable and reproducible in clinical practice settings.

Penetration of Vancomycin into Noninfected Bone in Patients Undergoing Total Joint Arthroplasty Evaluated by a Minimal Physiologically Based Population Pharmacokinetic Modeling Approach

Arthroplasty is a healthcare priority and represents high volume, high cost surgery. Periprosthetic joint infection (PJI) results in significant mortality, thus it is vital that the risk for PJI is minimized. Vancomycin is recommended for surgical prophylaxis in total joint arthroplasty (TJA) by current clinical practice guidelines endorsed by the Infectious Diseases Society of America. This study aimed to develop a new assay to determine vancomycin concentrations in serum and bone, and a minimal physiologically based population PK (mPBPK) model to evaluate vancomycin bone penetration in noninfected patients. Eleven patients undergoing TJA received 0.5-2.0 g intravenous vancomycin over 12-150 min before surgery. Excised bone specimens and four blood samples were collected per patient. Bone samples were pulverized under liquid nitrogen using a cryogenic mill. Vancomycin concentrations in serum and bone were analyzed by liquid chromatography-tandem mass spectrometry and subjected to mPBPK modeling. Vancomycin serum and bone concentrations ranged from 9.30 to 86.6 mg/L, and 1.94-37.0 mg/L, respectively. Average bone to serum concentration ratio was 0.41 (0.16-1.0) based on the collected samples. The population mean total body clearance was 2.12L/h/kg^0.75. Inclusion of total body weight as a covariate substantially decreased interindividual variability in clearance. The bone/blood partition coefficient (K_pbone) was estimated at 0.635, reflecting the average bone/blood concentration ratio at steady-state. The model predicted median ratio of vancomycin area under the curve (AUC) for bone/AUC for serum was 44%. Observed vancomycin concentrations in bone were overall consistent with perfusion-limited distribution from blood to bone. An mPBPK model overall well described vancomycin concentrations in serum and bone.

Evaluation of Neonatal and Paediatric Vancomycin Pharmacokinetic Models and the Impact of Maturation and Serum Creatinine Covariates in a Large Multicentre Data Set

BACKGROUND AND OBJECTIVE

Infants and neonates present a clinical challenge for dosing drugs with high interindividual variability due to these patients' rapid growth and the interplay between maturation and organ function. Model-informed precision dosing (MIPD), which can account for interindividual variability via patient characteristics and Bayesian forecasting, promises to improve individualized dosing strategies in this complex population. Here, we assess the predictive performance of published population pharmacokinetic models describing vancomycin in neonates and infants, and analyze the robustness of these models in the face of clinical uncertainty surrounding covariate values.

METHODS

The predictive precision and bias of nine pharmacokinetic models were compared in a large multi-site data set (N = 2061 patients, 5794 drug levels, 28 institutions) of patients aged 0-365 days. The robustness of model predictions to errors in serum creatinine measurements and gestational age was assessed by using recorded values or by replacing covariate values with 0.3, 0.5 or 0.8 mg/dL or with 40 weeks, respectively.

RESULTS

Of the nine models, two models (Dao and Jacqz-Aigrain) resulted in predicted concentrations within 2.5 mg/L or 15% of the measured values for at least 60% of population predictions. Within individual models, predictive performance often 2 differed in neonates (0-4 weeks) versus older infants (15-52 weeks). For preterm neonates, imputing gestational age as 40 weeks reduced the accuracy of model predictions. Measured values of serum creatinine improved model predictions compared to using imputed values even in neonates ≤1 week of age.

CONCLUSIONS

Several available pharmacokinetic models are suitable for MIPD in infants and neonates. Availability and accuracy of model covariates for patients will be important for guiding dose decision-making.

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.

Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill

Purpose

To assess the agreement in 24-hour area under the curve (AUC₂₄) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill.

Materials and Methods

Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC₂₄ values were determined using Tucuxi (revision cd7bd7a8) for dosing intervals with a vancomycin concentration using three models (Goti 2018, Colin 2019, and Thomson 2009) previously evaluated in the critically ill. AUC₂₄ values were categorized as subtherapeutic (<400 mg·h/L), therapeutic (400-600 mg·h/L), or toxic (>600 mg·h/L), assuming a minimum inhibitory concentration of 1 mg/L. AUC₂₄ value categorization was compared across the three models and reported as percent agreement.

Results

Overall, 466 AUC₂₄ values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC₂₄ values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC₂₄ values between all three models was 48% (223/466), Goti-Colin 59% (193/466), Goti-Thomson 68% (318/466), and Colin-Thomson 67% (314/466).

Conclusion

In critically ill patients, vancomycin AUC₂₄ values obtained from different pharmacokinetic models are often discordant, potentially contributing to differences in dosing decisions. This highlights the importance of selecting the optimal model.

Association between Vancomycin Pharmacokinetic Parameters and Clinical and Microbiological Efficacy in a Cohort of Neonatal Patients

Vancomycin pharmacokinetic/pharmacodynamic (PK/PD) targets have not been validated in the neonatal population as no specifically designed studies are available. The main goal of this study was to analyze the therapeutic vancomycin regimen, the 24-h area under the curve (AUC₂₄), and the trough plasma concentration (C_t) obtained that achieved clinical and microbiological effectiveness in a cohort of neonates. This was an observational, prospective, single-center study covering a period of 2 years. Eligible patients were neonates and young infants who were undergoing treatment with intravenous vancomycin for ≥72 h with ≥1 C_t available. The primary outcome was the association of C_t and AUC₂₄ with clinical and microbiological efficacy at the beginning (early clinical evolution [ECE]) and the end (late clinical evolution [LCE]) of treatment with vancomycin. A total of 43 patients were included, 88.4% of whom were cured. In ECE, the cutoff points of the receiver operating characteristic (ROC) curve were 238 mg · h/L (sensitivity of 61% and specificity of 88%) for AUC₂₄ and 6.8 μg/mL (sensitivity of 61% and specificity of 92%) for C_t. In LCE, the C_t value was 11 μg/mL, with a sensitivity of 80% and a specificity of 92%. In this analysis, AUC₂₄ was not considered a good predictor. Logistic regression showed that a vancomycin C_t of ≤6.8 μg/mL was associated with an unfavorable ECE (P = 0.001), being 18 times more likely to progress poorly compared to those with higher levels. AUC₂₄ and C_t are good predictors of ECE in this population. Concentrations close to 7 μg/mL and an AUC₂₄ of around 240 mg · h/L 48 h after antibiotic initiation seem to be sufficient to achieve clinical cure in most cases.

Dosing for Personalized Prophylaxis in Hemophilia A Highly Varies on the Underlying Population Pharmacokinetic Models

BACKGROUND

Model-informed personalized prophylaxis with factor VIII (FVIII) replacement therapy aimed at higher trough levels is becoming indispensable for patients with severe hemophilia A. This study aimed to identify the most suitable population pharmacokinetic (PK) models for personalized prophylaxis using various FVIII products and 2 clinical assays and to implement the most suitable one in open-access software.

METHODS

Twelve published population PK models were systematically compared to predict the time above target (TaT) for a reference dosing occasion. External validation was performed using a 5-point PK data from 39 adult patients with hemophilia A with FVIII measured by chromogenic substrate (CSA) and 1-stage assays (OSAs) using NONMEM under 3 different conditions: a priori (with all FVIII samples blinded), a posteriori (with 1 trough sample), and general model fit (with all FVIII samples including the reference dosing occasion provided).

RESULTS

On average, the baseline covariate models overpredicted TaT (a priori; bias -3.8 hours to 49.6 hours). When additionally including 1 previous trough FVIII sample before the reference dosing occasion (a posteriori), only 50% of the models improved in bias (-1.0 hours to 36.5 hours) and imprecision (22.4 hours and 60.7 hours). Using all the time points (general model fit), the models accurately predicted (individual TaT less than ±12 hours compared with the reference) 62%-90% and 33%-74% of the patients using CSA and OSA data, respectively. Across all scenarios, predictions using CSA data were more accurate than those using the OSA data.

CONCLUSIONS

One model performed best across the population (bias: -3.8 hours a priori, -1.0 hours a posteriori , and 0.6 hours general model fit ) and acceptably predicted 44% (a priori) to 90% ( general model fit ) of the patients. To allow the community-based evaluation of patient-individual FVIII dosing, this model was implemented in the open-access model-informed precision dosing software "TDMx."

Population pharmacokinetic model of vancomycin in postoperative neurosurgical patients

Objective: Vancomycin is commonly used in postoperative neurosurgical patients for empirical anti-infective treatment due to the low success rate of bacterial culture in cerebrospinal fluid (about 20%) and the high mortality of intracranial infection. At conventional doses, the rate of target achievement for vancomycin trough concentration is low and the pharmacokinetics of vancomycin varies greatly in these patients, which often leads to treatment failure. The objective of this study was to establish a population pharmacokinetic (PPK) model of vancomycin in postoperative neurosurgical patients for precision medicine.

Method: A total of 895 vancomycin plasma concentrations from 560 patients (497 postoperative neurosurgical patients) were retrospectively collected. The model was analyzed by nonlinear mixed effects modeling method. One-compartment model and mixed residual model was employed. The influence of covariates on model parameters was tested by forward addition and backward elimination. Goodness-of-fit, bootstrap and visual predictive check were used for model evaluation. Monte Carlo simulations were employed for dosing strategies with AUC₂₄ targets 400–600.

Result: Estimated glomerular filtration rate (eGFR), body weight (BW) and mannitol had significant influence on vancomycin clearance (CL). eGFR(mL/min)=144×(Scr/a)b×0.993age, for female, a = 0.7, Scr ≤ 0.7 mg/dl, b = −0.329, Scr > 0.7 mg/dl, b = −1.209; for male, a = 0.9, Scr ≤ 0.9 mg/dl, b = −0.411, Scr > 0.9 mg/dl, b = −1.210. Vancomycin clearance was accelerated when co-medicated with mannitol and increased with eGFR and BW. In the final model, the population typical value is 7.98 L/h for CL and 60.2 L for apparent distribution volume, CL (L/h)=7.98×(eGFR/115.2)0.8×(BW/70)0.3×eA, where A = 0.13 when co-medicated with mannitol, otherwise A = 0. The model is stable and effective, with good predictability.

Conclusion: In postoperative neurosurgical patients, a higher dose of vancomycin may be required due to the augmented renal function and the commonly used mannitol, especially in those with high body weight. Our vancomycin PPK model could be used for individualized treatment in postoperative neurosurgical patients.

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients

BACKGROUND

Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice.

METHODS

Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.).

RESULTS

The best performing model was the Catboost model with an RMSE and [Formula: see text] of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications.

CONCLUSIONS

Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice.

Systematic Comparison of Hospital-Wide Standard and Model-Based Therapeutic Drug Monitoring of Vancomycin in Adults

We aimed to evaluate the predictive performance and predicted doses of a single-model approach or several multi-model approaches compared with the standard therapeutic drug monitoring (TDM)-based vancomycin dosing. We performed a hospital-wide monocentric retrospective study in adult patients treated with either intermittent or continuous vancomycin infusions. Each patient provided two randomly selected pairs of two consecutive vancomycin concentrations. A web-based precision dosing software, TDMx, was used to evaluate the model-based approaches. In total, 154 patients contributed 308 pairs. With standard TDM-based dosing, only 48.1% (148/308) of all of the second concentrations were within the therapeutic range. Across the model-based approaches we investigated, the mean relative bias and relative root mean square error varied from -5.36% to 3.18% and from 24.8% to 28.1%, respectively. The model averaging approach according to the squared prediction errors showed an acceptable bias and was the most precise. According to this approach, the median (interquartile range) differences between the model-predicted and prescribed doses, expressed as mg every 12 h, were 113 [-69; 427] mg, -70 [-208; 120], mg and 40 [-84; 197] mg in the case of subtherapeutic, supratherapeutic, and therapeutic exposure at the second concentration, respectively. These dose differences, along with poor target attainment, suggest a large window of opportunity for the model-based TDM compared with the standard TDM-based vancomycin dosing. Implementation studies of model-based TDM in routine care are warranted.

Review and evaluation of vancomycin dosing guidelines for obese individuals

INTRODUCTION

Vancomycin dosing decisions are informed by factors such as body weight and renal function. It is important to understand the impact of obesity on vancomycin pharmacokinetics and how this may influence dosing decisions. Vancomycin dosing guidelines use varied descriptors of body weight and renal function. There is uncertainty whether current dosing guidelines result in attainment of therapeutic targets in obese individuals.

AREAS COVERED

Literature was explored using PubMed, Embase and Google Scholar for articles from January 1980 to July 2021 regarding obesity-driven physiological changes, their influence on vancomycin pharmacokinetics and body size descriptors and renal function calculations in vancomycin dosing. Pharmacokinetic simulations reflective of international vancomycin dosing guidelines were conducted to evaluate the ability of using total, ideal and adjusted body weight, as well as Cockcroft-Gault and CKD-EPI equations to attain an area-under-the-curve to minimum inhibitory concentration ratio (AUC₂₄/MIC) target (400-650) in obese individuals.

EXPERT OPINION

Vancomycin pharmacokinetics in obese individuals remains debated. Guidelines that determine loading doses using total body weight, and maintenance doses adjusted based on renal function and adjusted body weight, may be most appropriate for obese individuals. Use of ideal body weight leads to subtherapeutic vancomycin exposure and underestimation of renal function.

The Effect of Pregnancy and Inflammatory Bowel Disease on the Pharmacokinetics of Drugs Related to Inflammatory Bowel Disease-A Systematic Literature Review

Due to ethical and practical reasons, a knowledge gap exists on the pharmacokinetics (PK) of inflammatory bowel disease (IBD)-related drugs in pregnant women with IBD. Before evidence-based dosing can be proposed, insight into the PK has to be gained to optimize drug therapy for both mother and fetus. This systematic review aimed to describe the effect of pregnancy and IBD on the PK of drugs used for IBD. One aminosalicylate study, two thiopurine studies and twelve studies with biologicals were included. Most drugs within these groups presented data over multiple moments before, during and after pregnancy, except for mesalazine, ustekinumab and golimumab. The studies for mesalazine, ustekinumab and golimumab did not provide enough data to demonstrate an effect of pregnancy on concentration and PK parameters. Therefore, no evidence-based dosing advice was given. The 6-thioguanine nucleotide levels decreased during pregnancy to 61% compared to pre-pregnancy levels. The potentially toxic metabolite 6-methylmercaptopurine (6-MMP) increased to maximal 209% of the pre-pregnancy levels. Although the PK of the thiopurines changed throughout pregnancy, no evidence-based dosing advice was provided. One study suggested that caution should be exercised when the thiopurine dose is adjusted, due to shunting 6-MMP levels. For the biologicals, infliximab levels increased, adalimumab stayed relatively stable and vedolizumab levels tended to decrease during pregnancy. Although the PK of the biologicals changed throughout pregnancy, no evidence-based dosing advice for biologicals was provided. Other drugs retrieved from the literature search were mesalazine, ustekinumab and golimumab. We conclude that limited studies have been performed on PK parameters during pregnancy for drugs used in IBD. Therefore, more extensive research to determine the values of PK parameters is warranted. After gathering the PK data, evidence-based dosing regimens can be developed.

Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration-time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration-time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

A General Biphasic Bodyweight Model for Scaling Basal Metabolic Rate, Glomerular Filtration Rate, and Drug Clearance from Birth to Adulthood

The objective of this study is to propose a unified, continuous, and bodyweight-only equation to quantify the changes of human basal metabolic rate (BMR), glomerular filtration rate (GFR), and drug clearance (CL) from infancy to adulthood. The BMR datasets were retrieved from a comprehensive historical database of male and female subjects (0.02 to 64 years). The CL datasets for 17 drugs and the GFR dataset were generated from published maturation and growth models with reported parameter values. A statistical approach was used to simulate the model-generated CL and GFR data for a hypothetical population with 26 age groups (from 0 to 20 years). A biphasic equation with two power-law functions of bodyweight was proposed and evaluated as a general model using nonlinear regression and dimensionless analysis. All datasets universally reveal biphasic curves with two distinct linear segments on log-log plots. The biphasic equation consists of two reciprocal allometric terms that asymptotically determine the overall curvature. The fitting results show a superlinear scaling phase (asymptotic exponent >1; ca. 1.5-3.5) and a sublinear scaling phase (asymptotic exponent <1; ca. 0.5-0.7), which are separated at the phase transition bodyweight ranging from 5 to 20 kg with a mean value of 10 kg (corresponding to 1 year of age). The dimensionless analysis generalizes and offers quantitative realization of the maturation and growth process. In conclusion, the proposed mixed-allometry equation is a generic model that quantitatively describes the phase transition in the human maturation process of diverse human functions.

Augmented Renal Clearance in Severe Infections-An Important Consideration in Vancomycin Dosing: A Narrative Review

Vancomycin is a hydrophilic antibiotic widely used in severe infections, including bacteremia and central nervous system (CNS) infections caused by Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), coagulase-negative staphylococci and enterococci. Appropriate antimicrobial dosage regimens can help achieve the target exposure and improve clinical outcomes. However, vancomycin exposure in serum and cerebrospinal fluid (CSF) is challenging to predict due to rapidly changing pathophysiological processes and patient-specific factors. Vancomycin concentrations may be decreased for peripheral infections due to augmented renal clearance (ARC) and increased distribution caused by systemic inflammatory response syndrome (SIRS), increased capillary permeability, and aggressive fluid resuscitation. Additionally, few studies on vancomycin’s pharmacokinetics (PK) in CSF for CNS infections. The relationship between exposure and clinical response is unclear, challenging for adequate antimicrobial therapy. Accurate prediction of vancomycin pharmacokinetics/pharmacodynamics (PK/PD) in patients with high interindividual variation is critical to increase the likelihood of achieving therapeutic targets. In this review, we describe the interaction between ARC and vancomycin PK/PD, patient-specific factors that influence the achievement of target exposure, and recent advances in optimizing vancomycin dosing schedules for severe infective patients with ARC.

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

The recently released revised vancomycin consensus guideline endorsed area under the concentration-time curve (AUC) guided monitoring. Means to AUC-guided monitoring include pharmacokinetic (PK) equations and Bayesian software programs, with the latter approach being preferable. We aimed to evaluate the predictive performance of these two methods when monitoring using troughs or peaks and troughs at varying single or mixed dosing intervals (DIs), and evaluate the significance of satisfying underlying assumptions of steady-state and model transferability. Methods included developing a vancomycin population PK model and conducting model-informed precision dosing clinical trial simulations. A one-compartment PK model with linear elimination, exponential between-subject variability, and mixed (additive and proportional) residual error model resulted in the best model fit. Conducted simulations demonstrated that Bayesian-guided AUC can, potentially, outperform that of equation-based AUC predictions depending on the quality of model diagnostics and met assumptions. Ideally, Bayesian-guided AUC predictive performance using a trough from the first DI was equivalent to that of PK equations using two measurements (peak and trough) from the fifth DI. Model transferability diagnostics can guide the selection of Bayesian priors but are not strong indicators of predictive performance. Mixed versus single fourth and/or fifth DI sampling seems indifferent. This study illustrated cases associated with the most reliable AUC predictions and showed that only proper Bayesian-guided monitoring is always faster and more reliable than equations-guided monitoring in pre-steady-state DIs in the absence of a loading dose. This supports rapid Bayesian monitoring using data as sparse and early as a trough at the first DI.

Implementation of Vancomycin Therapeutic Monitoring Guidelines: Focus on Bayesian Estimation Tools in Neonatal and Pediatric Patients

BACKGROUND

The 2020 consensus guidelines for vancomycin therapeutic monitoring recommend using Bayesian estimation targeting the ratio of the area under the curve over 24 hours to minimum inhibitory concentration as an optimal approach to individualize therapy in pediatric patients. To support institutional guideline implementation in children, the objective of this study was to comprehensively assess and compare published population-based pharmacokinetic (PK) vancomycin models and available Bayesian estimation tools, specific to neonatal and pediatric patients.

METHODS

PubMed and Embase databases were searched from January 1994 to December 2020 for studies in which a vancomycin population PK model was developed to determine clearance and volume of distribution in neonatal and pediatric populations. Available Bayesian software programs were identified and assessed from published articles, software program websites, and direct communication with the software company. In the present review, 14 neonatal and 20 pediatric models were included. Six programs (Adult and Pediatric Kinetics, BestDose, DoseMeRx, InsightRx, MwPharm++, and PrecisePK) were evaluated.

RESULTS

Among neonatal models, Frymoyer et al and Capparelli et al used the largest PK samples to generate their models, which were externally validated. Among the pediatric models, Le et al used the largest sample size, with multiple external validations. Of the Bayesian programs, DoseMeRx, InsightRx, and PrecisePK used clinically validated neonatal and pediatric models.

CONCLUSIONS

To optimize vancomycin use in neonatal and pediatric patients, clinicians should focus on selecting a model that best fits their patient population and use Bayesian estimation tools for therapeutic area under the -curve-targeted dosing and monitoring.

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

BACKGROUND

Model-informed precision dosing (MIPD) 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

Therefore, we aimed to identify suitable popPK models for a priori prediction and a posteriori forecasting of vancomycin in continuous infusion. Additionally, a model averaging (MAA) and a model selection approach (MSA) were compared with the identified popPK models.

METHODS

. Clinical PK 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 was evaluated for the a priori and a posteriori scenarios using bias, root mean square errors, normalized 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 model (bias < -0.1 mg/L), followed by the Colin 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 good as the individual popPK models. Both approaches could therefore be used in clinical practice to guide dosing decisions.

Clinical Practice Guidelines for Therapeutic Drug Monitoring of Vancomycin in the Framework of Model-Informed Precision Dosing: A Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring

Background: To promote model-informed precision dosing (MIPD) for vancomycin (VCM), we developed statements for therapeutic drug monitoring (TDM). Methods: Ten clinical questions were selected. The committee conducted a systematic review and meta-analysis as well as clinical studies to establish recommendations for area under the concentration-time curve (AUC)-guided dosing. Results: AUC-guided dosing tended to more strongly decrease the risk of acute kidney injury (AKI) than trough-guided dosing, and a lower risk of treatment failure was demonstrated for higher AUC/minimum inhibitory concentration (MIC) ratios (cut-off of 400). Higher AUCs (cut-off of 600 μg·h/mL) significantly increased the risk of AKI. Although Bayesian estimation with two-point measurement was recommended, the trough concentration alone may be used in patients with mild infections in whom VCM was administered with q12h. To increase the concentration on days 1–2, the routine use of a loading dose is required. TDM on day 2 before steady state is reached should be considered to optimize the dose in patients with serious infections and a high risk of AKI. Conclusions: These VCM TDM guidelines provide recommendations based on MIPD to increase treatment response while preventing adverse effects.

Allometric Scaling in Pharmacokinetic Studies in Anesthesiology

A clinical review is presented of basic allometric scaling theory and its application to pharmacokinetic models in anesthesia and other fields in the biologic sciences.

Optimal Practice for Vancomycin Therapeutic Drug Monitoring: Position Statement From the Anti-infectives Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

ABSTRACT

Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM.

Pharmacokinetics of Antibiotics in Pediatric Intensive Care: Fostering Variability to Attain Precision Medicine

Children show important developmental and maturational changes, which may contribute greatly to pharmacokinetic (PK) variability observed in pediatric patients. These PK alterations are further enhanced by disease-related, non-maturational factors. Specific to the intensive care setting, such factors include critical illness, inflammatory status, augmented renal clearance (ARC), as well as therapeutic interventions (e.g., extracorporeal organ support systems or whole-body hypothermia [WBH]). This narrative review illustrates the relevance of both maturational and non-maturational changes in absorption, distribution, metabolism, and excretion (ADME) applied to antibiotics. It hereby provides a focused assessment of the available literature on the impact of critical illness—in general, and in specific subpopulations (ARC, extracorporeal organ support systems, WBH)—on PK and potential underexposure in children and neonates. Overall, literature discussing antibiotic PK alterations in pediatric intensive care is scarce. Most studies describe antibiotics commonly monitored in clinical practice such as vancomycin and aminoglycosides. Because of the large PK variability, therapeutic drug monitoring, further extended to other antibiotics, and integration of model-informed precision dosing in clinical practice are suggested to optimise antibiotic dose and exposure in each newborn, infant, or child during intensive care.

Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations

Introduction:Severe infections continue to impose a major burden on critically ill children and mortality rates remain stagnant. Outcomes rely on accurate and timely delivery of antimicrobials achieving target concentrations in infected tissue. Yet, developmental aspects, disease-related variables, and host factors may severely alter antimicrobial pharmacokinetics in pediatrics. The emergence of antimicrobial resistance increases the need for improved treatment approaches.Areas covered:This narrative review explores why optimization of antimicrobial therapy in neonates, infants, children, and adolescents is crucial and summarizes the possible dosing approaches to achieve antimicrobial individualization. Finally, we outline a roadmap toward scientific evidence informing the development and implementation of precision antimicrobial dosing in critically ill children.The literature search was conducted on PubMed using the following keywords: neonate, infant, child, adolescent, pediatrics, antimicrobial, pharmacokinetic, pharmacodynamic target, Bayes dosing software, optimizing, individualizing, personalizing, precision dosing, drug monitoring, validation, attainment, and software implementation. Further articles were sought from the references of the above searched articles.Expert opinion:Recently, technological innovations have emerged that enabled the development of individualized antimicrobial dosing approaches in adults. More work is required in pediatrics to make individualized antimicrobial dosing approaches widely operationalized in this population.

Population Pharmacokinetic Models of Vancomycin in Paediatric Patients: A Systematic Review

BACKGROUND

Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients.

METHODS

Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies.

RESULTS

Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m². Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (V_d) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for V_d, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for V_d. Variable dosing recommendations were suggested.

CONCLUSION

Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.

Predictive Performance of Bayesian Vancomycin Monitoring in the Critically Ill

OBJECTIVES

It is recommended that therapeutic monitoring of vancomycin should be guided by 24-hour area under the curve concentration. This can be done via Bayesian models in dose-optimization software. However, before these models can be incorporated into clinical practice in the critically ill, their predictive performance needs to be evaluated. This study assesses the predictive performance of Bayesian models for vancomycin in the critically ill.

DESIGN

Retrospective cohort study.

SETTING

Single-center ICU.

PATIENTS

Data were obtained for all patients in the ICU between 1 January, and 31 May 2020, who received IV vancomycin. The predictive performance of three Bayesian models were evaluated based on their availability in commercially available software. Predictive performance was assessed via bias and precision. Bias was measured as the mean difference between observed and predicted vancomycin concentrations. Precision was measured as the SD of bias, root mean square error, and 95% limits of agreement based on Bland-Altman plots.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 466 concentrations from 188 patients were used to evaluate the three models. All models showed low bias (-1.7 to 1.8 mg/L), which was lower with a posteriori estimate (-0.7 to 1.8 mg/L). However, all three models showed low precision in terms of SD (4.7-8.8 mg/L) and root mean square error (4.8-8.9 mg/L). The models underpredicted at higher observed vancomycin concentrations (bias 0.7-3.2 mg/L for < 20 mg/L; -5.1 to -2.3 for ≥ 20 mg/L) and the Bland-Altman plots showed a great deviation between observed and predicted concentrations.

CONCLUSIONS

Bayesian models of vancomycin show not only low bias, but also low precision in the critically ill. Thus, Bayesian-guided dosing of vancomycin in this population should be used cautiously.

Vancomycin dosing and therapeutic drug monitoring practices: guidelines versus real-life

Background Correct dosing and therapeutic drug monitoring (TDM) practices are essential when aiming for optimal vancomycin treatment. Objective To assess target attainment after initial dosing and dose adjustments, and to determine compliance to dosing and TDM guidelines. Setting Tertiary care university hospital in Belgium. Method A chart review was performed in 150 patients, ranging from preterm infants to adults, treated intravenously with vancomycin. Patient characteristics, dosing and TDM data were compared to evidence-based hospital guidelines. Main outcome measures Target attainment of vancomycin after initial dosing and dose adjustments. Results Subtherapeutic concentrations were measured in 68% of adults, in 76% of children and in 52% of neonates after treatment initiation. Multiple dose adaptations (median 2, Q1 1-Q3 2) were required for target attainment, whilst more than 20% of children and neonates never reached targeted concentrations. Regarding compliance to the hospital guideline, some points of improvement were identified: omitted dose adjustment in adults with decreased renal function (53%), delayed sampling (16% in adults, 31% in children) and redundant sampling (34% of all samples in adults, 12% in children, 13% in neonates). Conclusion Target attainment for vancomycin with current dosing regimens and TDM is poor in all age groups. Besides, human factors should not be ignored when aiming for optimal treatment. This study reflects an ongoing challenge in clinical practice and highlights the need for optimization of vancomycin dosing strategies and improvement of awareness of all health care professionals involved.

Prediction of vancomycin dose on high-dimensional data using machine learning techniques

OBJECTIVES

Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods.

METHODS

This study proposed a variable engineering process that automatically generates second-order variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables.

RESULTS

Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups.

CONCLUSION

The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.

External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers

Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.

Weight-based vancomycin loading strategy may not improve achievement of optimal vancomycin concentration in patients with preserved renal function

We performed a retrospective study to evaluate clinical effectiveness of vancomycin loading strategy and factors associated with achieving optimal C min. Patients administered vancomycin for ≥72 h from January to June 2018 were enrolled. Patients were divided into two groups: loading (LD) and non-loading (NLD). LD was defined as initial vancomycin dose ≥20 mg/kg and ≥120% of maintenance dose. During study period, 70 and 71 received initial LD (24.2 ± 2.5 mg/kg) and NLD (17.3 ± 3.3 mg/kg) doses of vancomycin, respectively (p < .001). Achievement of optimal C min was not different before administration of the third dose (24.4% in LD versus 18.2% in NLD, p = .484) and within 72 h (22.9% versus 28.2%, p = .759). Risk factors for failure to achieve optimal C min before administration of the third dose were higher creatinine clearance and higher level of serum albumin. Therefore, more sufficient loading or patient-specific dose strategies should be used to achieve optimal serum vancomycin C min.