Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing

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 (n = 31), and (iii) a secondary cohort from Nantes University Hospital (n = 7). Input features included age, body weight, creatinine clearance, MIC, and a single plasma concentration obtained before the second dose. Support vector machine models achieved high accuracy (>88%) and sensitivity (>90%) across testing sets and clinical validation cohorts. In clinical data sets, no false negatives were observed (limited by sample size), with overall accuracy approaching 95%. Compared with maximum a posteriori Bayesian estimation, ML achieved higher accuracy and sensitivity across validation cohorts, particularly by reducing false negatives. Predictions remained reliable through week 8, the clinically relevant exposure window. This ML-based approach enables early individualized redosing decisions using minimal clinical inputs. By complementing Bayesian forecasting and reducing reliance on serial sampling, it represents a pragmatic strategy to support model-informed precision dosing of dalbavancin.

Keywords: Monte Carlo simulations; dalbavancine; machine learning; model-informed precision dosing; population pharmacokinetics; therapeutic drug monitoring.

Fig 1

Distribution of simulated dalbavancin concentrations.

Fig 2

Distribution of dalbavancin plasma concentrations and number of samples per time point, University Hospital of Limoges cohort.

Fig 3

Distribution of dalbavancin plasma concentrations and number of samples per time point, University Hospital of Nantes cohort.