ArticleAntimicrobial agents and chemotherapy2026
Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing.
Article in Antimicrobial agents and chemotherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Review
- Population pharmacokinetics of dalbavancin: external validation, model averaging, and implications for precision dosing in prolonged therapy.Antimicrobial agents and chemotherapy · 2026Article
- Proactive Therapeutic Drug Monitoring of Dalbavancin in the Long-Term Treatment of Chronic Infections: A Narrative Review.Antibiotics (Basel, Switzerland) · 2026Review
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Authors and funding
11 authors.
Funding
Abstract
Dalbavancin is a long-acting lipoglycopeptide increasingly used off-label for complex Gram-positive infections requiring prolonged therapy. Its extended half-life enables simplified regimens, but interindividual pharmacokinetic variability and pathogen MIC heterogeneity complicate dosing. We developed and externally validated machine learning (ML) models to predict whether dalbavancin plasma concentrations remain above predefined pharmacokinetic/pharmacodynamic targets after two standard 1,500 mg doses (day 1/day 8 or day 1/day 15). Predictions were binary (adequate vs subtherapeutic concentration), directly reflecting the clinical decision to readminister a 1,500 mg dose. Models were trained on simulated PK profiles from a published population PK (popPK) model and evaluated in three independent settings: (i) simulated validation data sets from two alternative published popPK models, (ii) a real-world cohort from Limoges University Hospital (
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