{
  "abstract": "Long-term opioid use is common in critically ill children and can cause withdrawal symptoms at discontinuation. To avoid withdrawal, short-acting opioids are usually converted into a long-acting drug, such as methadone, which is then carefully tapered. However, the exact conversion ratio and the optimal tapering protocol are unknown. This study aimed to design optimized conversion and tapering schedules of morphine and fentanyl to methadone for pediatric intensive care unit patients using physiologically-based pharmacokinetic (PBPK) modeling and expert panel decision-making. PBPK models of fentanyl, morphine, and methadone were verified with published pharmacokinetic data and opioid conversion ratios and relevant tapering protocols identified through literature review. Then, plasma concentration-time curves of opioid infusions (1 µg/kg/h fentanyl, 10 µg/kg/h morphine), several opioid-methadone conversion ratios, and different methadone tapering protocols were simulated. Predicted methadone plasma concentrations were compared to minimal therapeutic concentrations (i.e., 1 ng/mL for fentanyl, 4 ng/mL for morphine, and 60 ng/mL for methadone). These simulations were reviewed by the expert panel consisting of pediatric intensivists and hospital pharmacists to establish recommendations. Simulations showed that a conversion ratio of 1:10 for fentanyl and 1:1 for morphine resulted in therapeutic methadone concentrations. Most previously established tapering recommendations resulted in equal maximum methadone concentrations, except for the low-risk tapering schedules of two of the three protocols. The robustness of these findings is reinforced by both a target attainment analysis and a sensitivity analysis employing the therapeutic target of methadone. The panel established consensus-based Dutch recommendations for conversion and tapering. These findings demonstrate that PBPK models can support dosing decisions by simulating clinical scenarios. The resulting recommendations have been implemented in the Dutch Pediatric Formulary, ensuring that the model-informed recommendations are directly applied in clinical practice.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pharmacy, Pharmacology and Toxicology, Radboud university medical center, Nijmegen, The Netherlands",
    "Royal Dutch Pharmacy Association (KNMP), Medicines Information Centre (GIC), The Hague, The Netherlands",
    "Dutch Knowledge Centre Pharmacotherapy for Children, Rotterdam, The Netherlands",
    "Division of Neonatology, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America",
    "Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America",
    "Division of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America",
    "Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive care, Erasmus MC-Sophia Children’s Hospital, Rotterdam, The Netherlands"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Marika A. de Hoop-Sommen"
    },
    {
      "affs": [
        1
      ],
      "name": "Sahar Alozai"
    },
    {
      "affs": [
        2
      ],
      "name": "Violette M. G. J. Gijsen"
    },
    {
      "affs": [
        3
      ],
      "name": "Tjitske M. van der Zanden"
    },
    {
      "affs": [
        4
      ],
      "name": "Henry T. Akinbi"
    },
    {
      "affs": [
        5
      ],
      "name": "Tomoyuki Mizuno"
    },
    {
      "affs": [
        6
      ],
      "name": "Alexander A. Vinks"
    },
    {
      "name": "Rick Greupink"
    },
    {
      "name": "Saskia N. de Wildt"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9wj2v2s1qbq9d.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356917",
  "fulltext_url": null,
  "id": "9wj2v2s1qbq9d",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356917",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2703|Pain Management and Opioid Use"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Addiction/Drug addiction"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Mental health and psychiatry/Substance-related disorders/Drug addiction"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pain management/Analgesics/Opioids"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pain management/Analgesics/Opioids/Morphine"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pediatrics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmaceutics/Dose prediction methods"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Analgesics/Opioids"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Analgesics/Opioids/Morphine"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Opioids"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Opioids/Morphine"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Substance-related disorders/Drug addiction"
    ],
    [
      "plos",
      "/People and places/Population groupings/Age groups"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Addiction/Drug addiction"
    ]
  ],
  "openalex": "W7147300909",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356917&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9wj2v2s1qbq9d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356917",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356917"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    },
    {
      "field": "medicine",
      "origin": "plos:pharmacology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Supporting clinical guidelines for opioid conversion to methadone and tapering to prevent withdrawal in critically ill children using physiology based pharmacokinetic modeling and simulation",
  "title_text": "Supporting clinical guidelines for opioid conversion to methadone and tapering to prevent withdrawal in critically ill children using physiology based pharmacokinetic modeling and simulation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/9wj2v2s1qbq9d",
  "venue": "PLOS One",
  "versions": []
}