Hybrid Population Pharmacokinetic–Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn’s Disease
This hybrid population PK-ML approach provides a promising framework for improving the predictive performance of Bayesian estimation, with the potential for continuous learning from new clinical data to enhance dose individualization.
Authors
- Jeffrey S. Hyams (ORCID: https://orcid.org/0000-0002-9769-236X)
- Tomoyuki Mizuno (ORCID: https://orcid.org/0000-0002-8471-9826)
- Jack Reifenberg (ORCID: https://orcid.org/0000-0001-7612-0349)
- Brendan M. Boyle
- Phillip Minar (ORCID: https://orcid.org/0000-0003-4223-4211)
- Kei Irie (ORCID: https://orcid.org/0000-0002-0820-1208)
- Joshua D. Noe
Institutions
- Children's Hospital of Wisconsin (US)
- Cincinnati Children's Hospital Medical Center (US)
- Nationwide Children's Hospital (US)
- Connecticut Children's Medical Center (US)
- University of Cincinnati (US)
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- Clinical Pharmacokinetics
- Published
- 2025-08-30
- DOI
- https://doi.org/10.1007/s40262-025-01564-7
- Citations
- 4
- Primary Topic
- Inflammatory Bowel Disease
- Type
- article
- Field-Weighted Citation Impact
- 7.27
Funders
- Cincinnati Children's Hospital Medical Center
- Crohn's and Colitis Foundation
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases