Author

Tomoyuki Mizuno

Cincinnati Children's Hospital Medical CenterORCID 0000-0002-8471-9826Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Pharmacology & Toxicology· 2

Signature Assays, Organisms & Methods

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Assay:clinical cohort / trial· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Brendan M. Boyle· 2 shared Jack ReifenbergiD· 2 shared Jeffrey S. HyamsiD· 2 shared Joshua D. NoeiD· 2 shared Phillip MinariD· 2 shared Kei IrieiD· 1 shared Kei IrieiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 20 mutual co-author links

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2 shared papersPharmacology & Toxicology

Brendan M. Boyle

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2 papers

Recent·Most cited

Model-informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease

Kei Irie, Phillip Minar, Jack Reifenberg, Brendan M. Boyle, Joshua D. Noe, Jeffrey S. Hyams, Tomoyuki Mizuno

This study develops a model-informed Deep Q-Network to automate infliximab dosing in pediatric Crohn's disease, achieving high target attainment with minimal overtreatment.

Clinical Pharmacology & Therapeutics · Nov 5, 2025Peer-reviewedPharmacology & Toxicology

Hybrid Population PK-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease

Kei Irie, Phillip Minar, Jack Reifenberg, Brendan M. Boyle, Joshua D. Noe, Jeffrey S. Hyams, Tomoyuki Mizuno

This paper develops a hybrid population PK-ML model to improve infliximab concentration predictions in pediatric and young adult Crohn's disease patients, showing better accuracy than Bayesian estimation alone.

bioRxiv · May 1, 2025 · PreprintPharmacology & Toxicology