A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

Journal: Clinical pharmacokinetics
PMID: 39085523

Abstract

BACKGROUND AND OBJECTIVE: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose.

Authors

  • Florence Rivals
    Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France.
  • Sylvain Goutelle
    Service de Pharmacie, Hospices Civils de Lyon, Groupement Hospitalier Nord, Lyon, France.
  • Cyrielle Codde
    Service de Maladies Infectieuses et Tropicales, CHU Dupuytren, Limoges, France.
  • Romain Garreau
    Service de Pharmacie, Hospices Civils de Lyon, Groupement Hospitalier Nord, Lyon, France.
  • Laure Ponthier
    Inserm, Univ. Limoges, CHU Limoges, Pharmacology & Toxicology, Limoges, France.
  • Pierre Marquet
    University of Limoges, UMR 1248.
  • Tristan Ferry
    Faculté de Médecine et de Pharmacie de Lyon, Univ Lyon, Université Claude Bernard Lyon 1, Lyon, France.
  • Marc Labriffe
    University of Limoges, IPPRITT, Limoges, France.
  • Alexandre Destere
    Département de Pharmacologie et de Pharmacovigilance, CHU de Nice, Université Côte d'Azur, France.
  • Jean-Baptiste Woillard
    P&T, Unité Mixte de Recherche 1248 Université de Limoges, Institut National de la Santé et de la Recherche Médicale, Limoges, France.

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