aceA machine learning framework for precision dosing of daptomycin across diverse disease states
The large pharmacokinetic variability of daptomycin across disease states poses a challenge for individualized dosing. Conventional population pharmacokinetic (PopPK) models are often limited to specific conditions, lacking generalizability. This study used a machine learning (ML) framework to predict daptomycin exposure and guide dose optimization across heterogeneous populations. Nine published PopPK models were used to generate 90,000 simulated profiles across patients with infections and/or patients treated…
A machine learning model trained on simulated profiles predicted daptomycin AUC more accurately than existing population pharmacokinetic models in a small real-world validation cohort, supporting pre-treatment precision dosing.
The real-world validation involved only 20 infected patients and 19 renal replacement therapy patients, and gains in the latter group were modest, with accuracy of 52.6%.
Evidence
- Peer-reviewedAntimicrobial Agents and Chemotherapy2026-10-14
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Truvace Impact Record TRV-2026-1925, v1: “A machine learning framework for precision dosing of daptomycin across diverse disease states.” Truvace, 2026-10-19. /record/TRV-2026-1925 (accessed at citation time). sha256 ea58d183a53b1f6d…
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