{
  "abstract": "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 with renal transplantation and/or renal replacement therapy. Seven ML algorithms were trained to predict the 24 h area under the concentration–time curve (AUC0–24). Model performance was evaluated using statistical metrics and clinical scenario assessments, and comparison with PopPK models. External validation was conducted with real-world data. CatBoost demonstrated the highest predictive accuracy, identifying dose, creatinine clearance, disease state, and weight as key predictors. The validation cohort comprised 20 patients with infections (501 concentration measurements) and 19 patients receiving renal replacement therapy (513 concentration measurements). In infected patients, the prediction error of AUC0–24 was reduced by 60.1% compared with existing PopPK models (root mean square error [RMSE], 141 vs 353 mg·h/L; mean absolute prediction error, 23.5% vs 58.1%). The ML model correctly classified 75.0% of patients into the appropriate clinical AUC categories (<666, 666–939, and >939 mg·h/L), outperforming PopPK models (average overall accuracy: 30.0%). In patients undergoing renal replacement therapy, ML also improved performance (RMSE, 197 vs 218 mg·h/L; overall accuracy, 52.6% vs 31.6%). The ML model demonstrated accurate prediction of steady-state daptomycin AUC in heterogeneous clinical populations and was successfully validated using real-world data. By enabling accurate pre-treatment exposure prediction, it provides a flexible and clinically useful pathway toward precision dosing of daptomycin.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pharmacy, The Second Qilu Hospital of Shandong University, Jinan, China",
    "Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Institute of Clinical Pharmacology, Shandong University, Jinan, China",
    "Cheeloo College of Medicine, Shandong University, Jinan, China",
    "Department of Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China",
    "Center for Health Outcomes Research and Delivery Science, Children’s National Hospital, Washington, DC, USA",
    "Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
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        0,
        1
      ],
      "name": "Xin-Fang Zhang"
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    {
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        1
      ],
      "name": "Jing Sun"
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    {
      "affs": [
        1
      ],
      "name": "Guo-Xiang Hao"
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    {
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        2,
        3
      ],
      "name": "Xiao-Mei Chen"
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    {
      "affs": [
        1
      ],
      "name": "Yue Zhou"
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    {
      "affs": [
        1
      ],
      "name": "Xiao-Jie Wang"
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    {
      "affs": [
        1,
        4,
        5
      ],
      "name": "John van den Anker"
    },
    {
      "affs": [
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        1
      ],
      "name": "Wei Zhao",
      "orcid": "0000-0002-1830-338X"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "De-Qing Sun"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Bo-Hao Tang",
      "orcid": "0009-0000-8266-916X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f9t0xzgz6bcmq.bib",
  "cited_by": 0,
  "doi": "10.1128/aac.01089-26",
  "fulltext_url": "https://journals.asm.org/doi/10.1128/aac.01089-26",
  "id": "f9t0xzgz6bcmq",
  "issns": [
    "1098-6596",
    "0066-4804"
  ],
  "landing_url": "https://doi.org/10.1128/aac.01089-26",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Pharmacology"
    ],
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      "lcc",
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    ],
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      "openalex",
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  "openalex": "W7221800102",
  "pdf_url": null,
  "pmcid": "PMC13731230",
  "pmid": "42872962",
  "published": "2026-10-14",
  "publisher": "ASM",
  "ris": "https://research.draconitic.com/paper/f9t0xzgz6bcmq.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T14:20:44Z",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "heading:Pharmacology",
      "subfield": "pharmacology-toxicology"
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  ],
  "title": "A machine learning framework for precision dosing of daptomycin across diverse disease states",
  "title_text": "A machine learning framework for precision dosing of daptomycin across diverse disease states",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:27Z",
  "url": "https://research.draconitic.com/paper/f9t0xzgz6bcmq",
  "venue": "Antimicrobial Agents and Chemotherapy",
  "versions": []
}