Assessment of Appropriateness of an Initial Dosing Regimen of Vancomycin and Development of a New Dosing Nomogram

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age

BACKGROUND

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.

AIM

This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.

METHOD

A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.

RESULTS

The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.

CONCLUSION

An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

Determining steady-state trough range in vancomycin drug dosing using machine learning

BACKGROUND

Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient.

METHODS

Observed vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017.

RESULT

The final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models.

CONCLUSION

We developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.

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

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

Development of a mobile application for vancomycin dosing calculation: A useful tool for the rational use of antimicrobials

Background

Mobile applications (app) provide many benefits for healthcare professionals, making them a useful support clinical decision system.

Objectives

To describe the development of a mobile app, CalcVAN, to calculate vancomycin dosage regimens for adult and pediatric patients.

Methods

This study is a technological production research to develop a mobile app through the rapid prototyping type for the Android system in the Brazilian context. The mobile app structure was developed in four steps: 1) conception, including the needs assessment, the target audience, the literature search, and the definition of contents; 2) prototype planning, including the definition of topics and writing of modules, the selection of media, and the layout; 3) production of the mobile app, including the selection of multimedia tools, the navigation structure, and planning of environment configuration; and 4) make the mobile app available.

Results

The CalcVAN has six screens, containing the vancomycin dosing calculator for adult and pediatric patients based on weight and estimated creatinine clearance parameters. Moreover, the mobile app is free and can be used without internet connection.

Conclusions

A free mobile app was developed to calculate vancomycin dosage regimens for inpatients. This tool assists to optimize the vancomycin dosing, contributing to the antimicrobial stewardship.

Study on the Structure-Activity Relationship of an Antimicrobial Peptide, Brevinin-2GUb, from the Skin Secretion of Hylarana guentheri

Antimicrobial peptides (AMPs) are considered potential alternatives to antibiotics due to their advantages in solving antibiotic resistance. Brevinin-2GUb, which was extracted from the skin secretion of Hylarana guentheri, is a peptide with modest antimicrobial activity. Several analogues were designed to explore the structure–activity relationship and enhance its activity. In general, the Rana box is not an indispensable motif for the bioactivity of Brevinin-2GUb, and the first to the 19th amino acids at the N-terminal end are active fragments, such that shortening the peptide while maintaining its bioactivity is a promising strategy for the optimisation of peptides. Keeping a complete hydrophobic face and increasing the net charges are key factors for antimicrobial activity. With the increase of cationic charges, α-helical proportion, and amphipathicity, the activity of t-Brevinin-2GUb-6K (tB2U-6K), in combatting bacteria, drastically improved, especially against Gram-negative bacteria, and the peptide attained the capacity to kill clinical isolates and fungi as well, which made it possible to address some aspects of antibiotic resistance. Thus, peptide tB2U-6K, with potent antimicrobial activity against antibiotic-resistant bacteria, the capacity to inhibit the growth of biofilm, and low toxicity against normal cells, is of value to be further developed into an antimicrobial agent.

Survey on request form content and result reporting in therapeutic drug monitoring service among laboratories in Czechia and Slovakia

Introduction

The aim of the study was to investigate current practice and policies of therapeutic drug monitoring (TDM) service requesting and result reporting in Czechia and Slovakia.

Materials and methods

All 149 laboratories that measure plasma drug concentrations were given an online questionnaire during a regular external quality assessment TDM cycle. The questionnaire consisted of 17 questions. The optimal TDM practice was defined as the application of all elements (age, body weight, time of sampling, date of the first administration, time of the last dose administration, the dose, the dosing interval, the route of administration, information on reason of testing, and information on other co–administered drugs) needed for reporting a recommendation for further drug dosing (positive response to question number 16).

Results

The response rate was 69%, 103 out of 149 laboratories measuring drug concentrations. Only 12% (12 out of 103 laboratories) of the laboratories implemented all elements needed for optimal TDM practice and reported a recommendation. Both paper and electronic request forms were used by 77 out of 103 (75%) laboratories. A total of 69 out of 103 laboratories (67%) specified the type of sampling tube on their request form. Cystatin C was used for prediction of renal drug elimination by 24% (25 out of 103) of participants.

Conclusions

Small number of laboratories implemented all elements needed for optimal TDM practice and report a recommendation on further dosing. Further efforts in education on optimal TDM practice as well as harmonization of service are desirable.

Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults

This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well‐suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA‐based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline.