ArticlePharmaceutical research2022
Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning.
Article in Pharmaceutical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 33 citations in OpenAlex.
- Development and evaluation of machine learning-based prediction-modelling for initial vancomycin serum concentrations in septic ICU patients using clinical health record data.Intensive care medicine experimental · 2026Article
- Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care.CPT: pharmacometrics & systems pharmacology · 2026Article
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study.Journal of medical Internet research · 2025Article
- Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.The AAPS journal · 2025Article
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.Antimicrobial agents and chemotherapy · 2024Article
- Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.International journal of clinical pharmacy · 2024Observational
- A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.Clinical pharmacokinetics · 2024Article
- Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.Paediatric drugs · 2024Review
- A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.Antimicrobial agents and chemotherapy · 2024Article
- Optimization of Ganciclovir and Valganciclovir Starting Dose in Children by Machine Learning.Clinical pharmacokinetics · 2024Article
- Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence.Kidney international reports · 2024Article
- Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.Frontiers in pharmacology · 2024Article
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates.Clinical pharmacokinetics · 2023Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved regarding the optimal initial dose. The objectives of this study were: to develop a Machine learning (ML) algorithm based on pharmacokinetic profiles obtained by Monte Carlo simulations using a population pharmacokinetic model (POPPK) from the literature, in order to derive the best vancomycin initial dose in preterm and term neonates, and to compare ML performances with those of an literature equation (LE) derived from a POPPK previously published. MATERIALS AND
methodsThe parameters of a previously published POPPK model of vancomycin in children and neonates were used in the mrgsolve R package to simulate 1900 PK profiles. ML algorithms were developed from these simulations using Xgboost, GLMNET and MARS in parallel, benchmarked and used to calculate the ML first dose. Performances were evaluated in a second simulation set and in an external set of 82 real patients and compared to those of a LE.
resultsThe Xgboost algorithm yielded numerically best performances and target attainment rates: 46.9% in the second simulation set of 400-600 AUC/MIC ratio vs. 41.4% for the LE model (p = 0.0018); and 35.3% vs. 28% in real patients (p = 0.401), respectively). The Xgboost model resulted in less AUC/MIC > 600, thus decreasing the risk of nephrotoxicity.
conclusionThe Xgboost algorithm developed to estimate the initial dose of vancomycin in term or preterm infants has better performances than a previous validated LE and should be evaluated prospectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.