• acute kidney injury
• area under the curve
• nephrotoxicity
• obese
• vancomycin

• Population pharmacokinetic
• vancomycin; non-critically patients.

• Humans
• Vancomycin/pharmacokinetics
• Vancomycin/therapeutic use
• Adult
• Anti-Bacterial Agents/pharmacokinetics
• Models, Biological

• kidney injury
• model informed precision dosing
• precision dosing
• therapeutic drug monitoring
• therapeutic window

• NONMEM
• external evaluation
• haemodialysis
• population pharmacokinetics
• therapeutic drug monitoring
• vancomycin

• Humans
• Vancomycin/pharmacokinetics
• Vancomycin/administration & dosage
• Anti-Bacterial Agents/pharmacokinetics
• Anti-Bacterial Agents/administration & dosage
• Renal Dialysis/methods
• Renal Dialysis/adverse effects
• Retrospective Studies
• Models, Biological
• Drug Monitoring/methods
• Middle Aged
• Male
• Methicillin-Resistant Staphylococcus aureus/drug effects
• Female
• Aged
• Adult
• Staphylococcal Infections/prevention & control
• Staphylococcal Infections/drug therapy
• Nomograms
• Administration, Intravenous

• Amélie Marsot acknowledges support from the Fonds de Recherche du Québec-Santé (FRQS) Research Scholars-Junior 1 (Young Researcher Establishment) Career
• Faculty of Pharmacy of the University of Montreal

• Drug transporters
• Hepatic clearance
• Obesity
• Pharmacokinetics
• Renal clearance

• Humans
• Liver/metabolism
• Obesity/metabolism
• Obesity/genetics
• Male
• Kidney/metabolism
• Female
• Adult
• Middle Aged
• Multidrug Resistance-Associated Proteins/genetics
• Multidrug Resistance-Associated Proteins/metabolism
• Overweight/metabolism
• Membrane Transport Proteins/metabolism
• Membrane Transport Proteins/genetics
• Body Mass Index
• Gene Expression Regulation

• R01 GM124046 NIGMS NIH HHS
• R01GM124046 NIGMS NIH HHS

• Humans
• Models, Biological
• Vancomycin/pharmacokinetics
• Vancomycin/administration & dosage
• Imatinib Mesylate/pharmacokinetics
• Imatinib Mesylate/administration & dosage
• Female
• Male
• Drug Monitoring/methods
• Middle Aged
• Adult
• Aged
• Anti-Bacterial Agents/pharmacokinetics
• Anti-Bacterial Agents/administration & dosage
• Dose-Response Relationship, Drug
• Precision Medicine/methods

• Bayesian software
• model‐informed precision dosing
• obese population
• vancomycin

• Humans
• Vancomycin/pharmacokinetics
• Vancomycin/administration & dosage
• Bayes Theorem
• Body Mass Index
• Retrospective Studies
• Middle Aged
• Adult
• Anti-Bacterial Agents/pharmacokinetics
• Anti-Bacterial Agents/administration & dosage
• Female
• Male
• Aged
• Age Factors
• Young Adult
• Drug Monitoring/methods
• Software
• Models, Biological
• Adolescent
• United States
• Aged, 80 and over

• Humans
• Vancomycin/pharmacokinetics
• Vancomycin/administration & dosage
• Middle Aged
• Female
• Adult
• Male
• Aged
• Obesity/drug therapy
• Anti-Bacterial Agents/pharmacokinetics
• Anti-Bacterial Agents/administration & dosage
• Aged, 80 and over
• Body Mass Index
• Quality Improvement
• Young Adult
• Models, Biological

• Vancomycin
• acute kidney injury
• prediction model
• risk factors
• therapeutic drug monitoring

• Area under the curve (AUC)
• Clearance
• Loading dose
• Pharmacokinetics
• Vancomycin
• Volume of distribution

• K12 HD113189 NICHD NIH HHS

• Monte Carlo simulation
• clinical pharmacy
• glomerular filtration rate
• lean body mass
• non-linear mixed-effects modeling
• obesity
• therapeutic drug monitoring

• Ministerstvo Zdravotnictví Ceské Republiky
• Univerzita Karlova v Praze

• critical care
• external validation
• model-informed precision dosing
• obesity
• pharmacokinetics
• therapeutic drug monitoring
• vancomycin

• Adult
• Humans
• Vancomycin/pharmacokinetics
• Critical Illness
• Retrospective Studies
• Obesity/drug therapy
• Anti-Infective Agents
• Anti-Bacterial Agents/pharmacokinetics

• Humans
• Adult
• Vancomycin/pharmacokinetics
• Anti-Bacterial Agents/pharmacokinetics
• Body Weight
• Area Under Curve
• Microbial Sensitivity Tests

• Humans
• Algorithms
• Machine Learning
• Learning

• AUC
• area under curve
• pharmacokinetics
• vancomycin

• antibiotic
• antimicrobial
• critically ill
• dosing
• obesity
• pharmacokinetics
• therapeutic drug monitoring

• Obesity
• actual total body weight
• antineoplastics
• body-surface area
• cancer chemotherapy
• dose adjustment
• ideal body weight
• pediatrics
• pharmacokinetics

• adults
• dose optimization
• obese population
• pediatrics
• vancomycin

• Ministry of Education – Kingdom of Saudi Arabi

• Bayes theorem
• pharmacokinetics
• therapeutic drug monitoring
• vancomycin

• clinical pharmacokinetics
• obesity
• pharmacokinetics and pharmacodynamics
• therapeutic drug monitoring
• vancomycin
• vancomycin dosing

• Bayesian forecasting
• model averaging
• model selection
• population pharmacokinetics
• precision dosing
• predictive performance
• therapeutic drug monitoring
• vancomycin

• PDMT2/21/077 KU Leuven

• BMI
• dosing
• guidelines
• obese
• obesity
• pharmacokinetics
• renal function
• vancomycin
• weight

Vancomycin is the most commonly used drug for methicillin-resistant Staphylococcus aureus. The empirical clinical doses of vancomycin based on non-obese patients may not be optimal for obese ones.

This study reports a case of vancomycin dosing adjustment in an obese patient (body mass index 78.4 kg/m²) with necrotizing fasciitis of the scrotum and left lower extremity accompanied with acute renal failure. Dosing adjustment was performed based on literature review and factors that influence pharmacokinetic parameters are analyzed. The results of the blood drug concentration monitoring confirmed the successful application of our dosing adjustment strategy in this obese patient. Total body weight is an important consideration for vancomycin administration in obese patients, which affects the volume of distribution and clearance of vancomycin. The alterations of pharmacokinetic parameters dictate that vancomycin should be dose-adjusted when applied to obese patients. At the same time, the pathophysiological status of patients, such as renal function, which also affects the dose adjustment of the patient, should be considered.

Monitoring vancomycin blood levels in obese patients is critical to help adjust the dosing regimen to ensure that vancomycin concentrations are within the effective therapeutic range and to reduce the incidence of renal injury.

• Vancomycin
• Obesity
• Acute renal failure
• Pharmacokinetics
• Case report

Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.

• Anti-Bacterial Agents
• Area Under Curve
• Bayes Theorem
• Drug Monitoring/methods
• Humans
• Software
• Vancomycin/pharmacokinetics

• Pfizer

It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin.

The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.

The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters.

A total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34.

We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

• Bayesian prediction
• clinical data
• digital health tools
• drug monitoring
• medical informatics
• therapeutic drug monitoring
• tool development
• vancomcyin

• Bayesian forecasting
• model averaging
• population pharmacokinetics
• precision dosing
• therapeutic drug monitoring
• vancomycin

• Anti-Bacterial Agents
• Bayes Theorem
• Humans
• Models, Biological
• Retrospective Studies
• Vancomycin

• clinical medicine
• drug monitoring
• education
• hospital
• microbiology
• pharmacy
• pharmacy service

• NONMEM
• Pakistan
• dose tailoring
• population pharmacokinetics
• vancomycin

• Staphylococcus aureus
• AUC
• MAP-Bayesian dosing
• therapeutic drug monitoring
• vancomycin

• Adult
• Bayes Theorem
• Drug Monitoring
• Humans
• Obesity/drug therapy
• Precision Medicine
• Vancomycin/administration & dosage

• P41 EB001978 NIBIB NIH HHS

• DOACs
• apixaban
• atrial fibrillation
• dabigatran
• direct oral anticoagulants
• edoxaban
• obesity
• rivaroxaban

The revised vancomycin monitoring guidelines recommend targeting an area under the curve (AUC) of 400–600 mg*hr/L for serious methicillin‐resistant Staphylococcus aureus (MRSA) infections. An AUC can be measured by checking a peak and trough concentration at steady state; however, this requires obtaining an additional blood sample. The most practical way to perform AUC‐guided dosing is by estimating an AUC from a steady‐state trough. The purpose of this study was to compare AUCs estimated from trough‐only data to AUCs calculated from peak and trough concentrations.

Steady‐state peak and trough data were collected from an open‐access clinical calculator VancoPK.com. Patients were included who had (1) peaks drawn ≥60 min after the end of infusion, (2) peak and trough levels drawn ≥4 h apart and (3) troughs drawn ≤4 h early or late. The population was randomized and divided into a model group and test group. A population equation for vancomycin volume of distribution (Vd) was derived and compared to other general adult Vd models. Accuracy and precision of estimated AUCs were measured with bias, root mean square error (RMSE) and Lin's concordance correlation.

A total of 2,500 adult patients were included in the model group and 1,843 were included in the test group. The derived Vd equation, Vd (L) = 0.29(age) +0.33(total BW in kg) +11, produced accurate and precise AUC estimates from trough‐only data. The mean actual AUC and estimated AUC were 504 and 503, respectively, with a correlation of 0.926. The RMSE between estimated and actual AUCs was 47.7, meaning that over 95% of estimated AUCs were within 100 points of actual AUCs with the study's Vd model. Other Vd models performed well for certain types of patients, depending on their body weight and age.

There is limited evidence from large, robust populations regarding how to estimate Vd for general adult patients. Accuracy and precision of estimated AUCs depend on the applied population Vd model. The Vd model from the present study can be used for AUC‐guided dosing with trough‐only data which requires less blood work than peak‐trough monitoring. AUC calculations are practical with the use of open‐access websites.

• area under the curve
• clinical calculator
• population pharmacokinetics
• vancomycin
• website

Vancomycindosing error and inappropriate monitoring is a common problem in hospital daily practice. In King Abdulaziz Medical City (KAMC) in Jeddah, a high percentage of abnormal vancomycin trough levels is still detected despite using the recommended dose. Therefore, the current research objective is to study the major causes of vancomycin dosing errors. This retrospective, single-center, cross-sectional study was carried out at KAMC hospital in Jeddah from January 1st until December 31st 2019. All adult patients (≥15 years) who received vancomycin and had an initial abnormal trough level at the measured steady-state were included in this study. 472 patients have met the study inclusion criteria.

The current study evaluated the factors that play a role in causing vancomycin trough level abnormalities such as sampling time, vancomycin dosing, and patient’s pharmacokinetic and pharmacodynamic variations.

In this study, we found that pharmacokinetic and pharmacodynamic variability was attributed to 65% of vancomycin's abnormal trough level. Also, the result showed a significantly increased odds of the low trough in the non-elderly group (OR 6, 95% CI 2.48 – 14.9, P < 0.001) and febrile neutropenic patients (OR 2.21, 95% CI 1.119 – 4.365, P < 0.05). However, the odds of high trough levels were significantly elevated among patients who have CrCl < 50 ml/min (OR 5, 95% CI 1.262–20.539, P < 0.05). In addition, the present investigation revealed that the occurrence of abnormal vancomycin levels was not affected by daily duty time or working days (p > 0.05). The current study indicated that vancomycin dosing errors were common in KAMC patients; thus, there is an unmet need to evaluate the causes of vancomycin abnormal trough level and optimize a strategy that would enhance the therapeutic effectiveness and minimize the potential toxicity.

• Abnormal Trough
• Duty time
• Number of interventions
• Sampling time
• Vancomycin
• Weekend's effect

• nephrotoxicity
• pharmacokinetics and pharmacodynamics
• target attainment
• vancomycin
• vancomycin consensus guideline

Management of vancomycin administration for intensive care units (ICU) patients remains a challenge. The aim of this study was to describe a population pharmacokinetic model of vancomycin for optimizing the dose regimen for ICU patients. We prospectively enrolled 466 vancomycin-treated patients hospitalized in the ICU, collected trough or approach peak blood samples of vancomycin and recorded corresponding clinical information from July 2015 to December 2017 at Tai Zhou Hospital of Zhejiang Province. The pharmacokinetics of vancomycin was analyzed by nonlinear mixed effects modeling with Kinetica software. Internal and external validation was evaluated by the maximum likelihood method. Then, the individual dosing regimens of the 92 patients hospitalized in the ICU whose steady state trough concentrations exceeded the target range (10–20 μg/ml) were adjusted by the Bayes feedback method. The final population pharmacokinetic model show that clearance rate (CL) of vancomycin will be raised under the conditions of dopamine combined treatment, severe burn status (Burn-S) and increased total body weight (TBW), but reduced under the conditions of increased serum creatinine (Cr) and continuous renal replacement therapy status; Meanwhile, the apparent distribution volume (V) of vancomycin will be enhanced under the terms of increased TBW, however decreased under the terms of increased age and Cr. The population pharmacokinetic parameters (CL and V) according to the final model were 3.16 (95%CI 2.83, 3.40) L/h and 60.71 (95%CI 53.15, 67.46). The mean absolute prediction error for external validation by the final model was 12.61% (95CI 8.77%, 16.45%). Finally, the prediction accuracy of 90.21% of the patients’ detected trough concentrations that were distributed in the target range of 10–20 μg/ml after dosing adjustment was found to be adequate. There is significant heterogeneity in the CL and V of vancomycin in ICU patients. The constructed model is sufficiently precise for the Bayesian dose prediction of vancomycin concentrations for the population of ICU Chinese patients.

Over the past decade, numerous obesity-specific pharmacokinetic (PK) models and dosage regimens have been developed. However, it is unclear whether vancomycin PKs differ between obese and other patients after accounting for weight, age, and kidney function. In this study, the authors investigated whether using obesity-specific population PK models for vancomycin offers any advantage in accuracy and precision over using a recently developed general-purpose model.

Depending on the renal function of patients and many other influencing factors, studies on vancomycin pharmacokinetics show significant inter- and intra-individual variability. The present study was conducted using a population pharmacokinetics method to investigate the pharmacokinetic parameters and identified their influencing covariates for intravenous vancomycin in adult kidney transplant recipients.

The drug monitoring data included 56 adult renal transplant recipients who received intravenous vancomycin as prophylactic medication. The analysis was performed by a population approach with NONMEM. Data were collected mainly during the first week after transplantation. Monitoring of vancomycin trough concentration in blood was initiated mainly 3–5 days after the initial administration.

The one-compartment open model was optimal and adequately described the data. Body weight (WT) and estimated glomerular filtration rate (GFR) were identified as significant covariates of the pharmacokinetic parameters CL and V of intravenous vancomycin in the kidney transplant patients. The typical values of vancomycin CL and V were 2.08 L h^-1 and 63.2 L, respectively. A dosage strategy scheme according to model results was also designed.

Both WT and GFR of the kidney transplant patients positively influence the pharmacokinetic parameters CL and V for intravenous vancomycin. Our population pharmacokinetic model provides a reference for vancomycin dosage adjustment in kidney transplant recipients.

• kidney
• population pharmacokinetics
• therapeutic drug monitoring
• transplantation Key Points
• transplantation vancomycin
• vancomycin

• Clearance
• Creatinine
• Cystatin C
• Vancomycin
• eGFR

• Bayes Theorem
• Creatinine/blood
• Cystatin C/blood
• Databases, Factual
• Glomerular Filtration Rate/physiology
• Humans
• Vancomycin/metabolism

The aim of this study was to evaluate the influence of patient body mass index (BMI) and estimated creatinine clearance (CrCl) on serum vancomycin concentrations to define a possible optimal dosage regimen in overweight patients based on data obtained during therapeutic drug monitoring.

This retrospective study used data collected from January 2017 to January 2019. Adult patients (n = 204) received vancomycin treatment at a dose of 1000 mg every 12 h and underwent serum monitoring. Data collected included patient disease category, sex, age, height, weight, vancomycin concentrations, and serum creatinine. The CrCl values were estimated using the Cockcroft-Gault formula. In this study, statistical comparisons were performed on the results of patients according to serum vancomycin concentration.

Serum vancomycin concentration was significantly related to BMI (P <  0.001) and CrCl (P <  0.05) in adult patients. Furthermore, the trough serum vancomycin concentration showed a logarithmic correlation with BMI (R = − 0.5108, 95% CI: − 0.6082 to − 0.3982, P <  0.001) and CrCl (R = − 0.5739, 95% CI: − 0.6616 to − 0.4707, P <  0.001). The multivariate analysis showed that BMI and CrCl are independent contributors to the trough vancomycin concentration. Moreover, some of the patients with higher BMI (≥ 24 kg/m²) met the goal trough concentration after an adjustment from 1000 mg every 12 h to 1000 mg every 8 h.

Serum vancomycin concentration decreases progressively with increasing BMI and the augmentation in CrCl in adult patients. The trough concentration of vancomycin should be continuously monitored for patients with a BMI ≥ 24 kg/m², and the dosage regimen should be adjusted to reach the target trough concentration in these patients to reduce the impact of BMI.

• Body mass index
• Creatinine clearance
• Therapeutic drug monitoring
• Vancomycin

• bacteremia
• critical illness
• obesity
• pharmacokinetics
• septic shock
• vancomycin