Model-Informed Precision Dosing on Cyclosporine Therapy in Hematopoietic Stem Cell Transplant Recipients
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age2-65
SponsorYasmin medhat munir Mohamed
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
• CsA therapy indicated alone or in combination for GVHD prophylaxis.
Aged 2-65 years.
Clinically stable after first HSCT.
Disqualifiers
• Inaccurate sampling or dose administration times.
Patients with missing key covariates.
Patients lacking sufficient pharmacokinetic or TDM data
Trial population
The study population consists of pediatric and adult patients aged 2-65 years who underwent first allogeneic hematopoietic stem cell transplantation (HSCT) and received cyclosporine for graft-versus-host disease (GVHD) prophylaxis. Participants will be identified retrospectively from electronic medical records and therapeutic drug monitoring (TDM) databases. Eligible patients must have complete demographic, clinical, laboratory, dosing, and cyclosporine TDM data. Patients with inaccurate dose administration or blood sampling times, missing essential covariates, or insufficient pharmacokinetic or TDM data will be excluded.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Not listed
Trial groups
300 Participants
are grouped into 1 trial groupGroup A: Patients receiving cyclosporine to prevent graft-versus-host disease after HSCT.
Trial outcomes
Primary outcomes
1
Predictive accuracy of individualized cyclosporine dosing models.
Comparison of the predictive performance of the hybrid Population Pharmacokinetic-Machine Learning (PopPK-ML) model, deep learning model, and conventional Bayesian forecasting for predicting individualized cyclosporine doses using therapeutic drug monitoring (TDM) data. Performance will be assessed using root mean square error (RMSE), mean absolute error (MAE), mean prediction error (MPE), coefficient of determination (R²), and target dose prediction accuracy.
Time frame
up to 6 monthsLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Yasmin medhat munir Mohamed
Lead sponsor
Helwan University
Sponsor institution
Source ClinicalTrials.gov
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