Machine Learning Models Reveal New Risk Factors for Sub-/Supra-Therapeutic Concentrations of Sirolimus in Children with Vascular Anomalies
This study is the first to use machine learning models to predict the risk of sub- or supra-therapeutic sirolimus concentrations in vascular anomalies children. By enabling personalized exposure risk prediction, the dosing accuracy of sirolimus for the treatment of children with vascular anomalies can be optimized, thereby enhancing effectiveness and safety.
Authors
- W. Li
- Jin Xu (ORCID: https://orcid.org/0000-0002-0869-0507)
- H. Henry Guo (ORCID: https://orcid.org/0000-0001-5511-1750)
- Feng Chen (ORCID: https://orcid.org/0000-0003-1051-2053)
- Zhou Zhou (ORCID: https://orcid.org/0000-0002-7674-9857)
- Wang Shen (ORCID: https://orcid.org/0000-0001-5779-5471)
- Lin Fan (ORCID: https://orcid.org/0009-0003-2210-6279)
- Jianjun Zou (ORCID: https://orcid.org/0000-0001-6274-5778)
- Yuxiang Hu (ORCID: https://orcid.org/0009-0005-8536-3840)
- Yi Ji
Institutions
- China Pharmaceutical University (CN)
- Nanjing Children's Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Drug Design Development and Therapy
- Published
- 2026-01-01
- DOI
- https://doi.org/10.2147/dddt.s563637
- Primary Topic
- Vascular Malformations and Hemangiomas
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Pharmaceutical Association