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Model-Informed Precision Dosing on Cyclosporine Therapy in Hematopoietic Stem Cell Transplant Recipients
Sponsor: Yasmin medhat munir Mohamed
Summary
The purpose of this study is to develop a new tool that helps doctors choose the right cyclosporine dose for patients undergoing bone marrow transplantation. The tool is designed to predict the best dose using sparse sampling, making it practical for everyday clinical care. It combines information about population pharmacokinetics of cyclosporine with advanced artificial intelligence techniques, including machine learning and deep learning. This tool aims to improve treatment, personalize dosing for each patient, and reduce the risk of graft-versus-host disease.
Official title: Hybrid Population Pharmacokinetic,Machine Learning and Deep Learning Modelling to Predict Dosing for the Individualization of Cyclosporine Therapy in Transplant Recipients
Key Details
Gender
All
Age Range
2 Years - 65 Years
Study Type
OBSERVATIONAL
Enrollment
300
Start Date
2026-08-01
Completion Date
2027-06-01
Last Updated
2026-07-10
Healthy Volunteers
No