Model-Informed Precision Dosing on Cyclosporine Therapy in Hematopoietic Stem Cell Transplant Recipients

July 8, 2026 updated by: Yasmin medhat munir Mohamed

Hybrid Population Pharmacokinetic,Machine Learning and Deep Learning Modelling to Predict Dosing for the Individualization of Cyclosporine Therapy in Transplant Recipients

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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

Cyclosporine (CsA) is a cornerstone immunosuppressive agent used for the prevention of graft-versus-host disease (GVHD) following allogeneic hematopoietic stem cell transplantation (HSCT). Despite its widespread use, cyclosporine has a narrow therapeutic index and exhibits substantial inter- and intra-individual pharmacokinetic variability. Subtherapeutic exposure increases the risk of GVHD and graft failure, whereas excessive exposure is associated with nephrotoxicity, neurotoxicity, hypertension, and other adverse events. Variability in cyclosporine pharmacokinetics is influenced by numerous patient-specific factors, including body weight, hematocrit, age, renal and hepatic function, concomitant medications (particularly azole antifungals), genetic factors, and post-transplant physiological changes.

Current therapeutic drug monitoring (TDM) practices are primarily reactive, with dose adjustments made only after measured drug concentrations fall outside the therapeutic range. Consequently, many patients fail to achieve target cyclosporine concentrations following the initial dose and require multiple dose modifications before therapeutic exposure is attained. Although Bayesian forecasting based on population pharmacokinetic (PopPK) models has improved dose individualization, existing models often assume linear covariate-parameter relationships, have limited external validation, and may not adequately capture the complex nonlinear interactions that influence cyclosporine pharmacokinetics in bone marrow transplant recipients.

This study aims to develop and externally validate individualized cyclosporine dosing models by integrating mechanistic population pharmacokinetic modeling with advanced machine learning and deep learning techniques. A retrospective cohort will be used for model development and internal validation, while a prospective observational cohort of transplant recipients receiving standard-of-care cyclosporine therapy will be used for external validation.

Demographic characteristics, transplantation-related variables, laboratory measurements, cyclosporine dosing history, therapeutic drug monitoring results, concomitant medications, and relevant clinical outcomes will be collected from routine clinical practice. A mechanistic PopPK model will first be developed to characterize cyclosporine pharmacokinetics. Machine learning algorithms, including XGBoost and LightGBM, together with deep learning models, will then be trained to improve dose prediction by modeling complex nonlinear relationships among patient-specific covariates and residual variability. Bayesian forecasting using the PopPK model will serve as the reference approach for comparison.

Model performance will be evaluated using predictive accuracy, bias, precision, root mean square error (RMSE), mean absolute error (MAE), mean prediction error (MPE), coefficient of determination (R²), and the proportion of predicted concentrations or doses within predefined acceptable error limits. External validation will assess model generalizability in an independent prospective cohort. Model interpretability will be evaluated using SHAP (Shapley Additive Explanations) to identify the most influential variables contributing to individualized dose predictions.

The final validated hybrid model will be implemented as an R Shiny web-based clinical decision-support application capable of providing individualized initial cyclosporine dose recommendations, prediction intervals, and model explanation before the first therapeutic drug monitoring measurement. The study is expected to demonstrate whether hybrid PopPK-machine learning and deep learning approaches provide superior predictive performance compared with conventional Bayesian forecasting, thereby supporting precision dosing of cyclosporine, improving early therapeutic target attainment, reducing dose adjustments and drug-related toxicity, and establishing the foundation for future interventional clinical trials.

Study Type

Observational

Enrollment (Estimated)

300

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study 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.

Description

Inclusion Criteria:

  • • CsA therapy indicated alone or in combination for GVHD prophylaxis.

    • Aged 2-65 years.
    • Clinically stable after first HSCT.

Exclusion Criteria:

  • • Inaccurate sampling or dose administration times.

    • Patients with missing key covariates.
    • Patients lacking sufficient pharmacokinetic or TDM data

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Patients receiving cyclosporine to prevent graft-versus-host disease after HSCT.
Participants undergoing allogeneic hematopoietic stem cell transplantation who received cyclosporine for graft-versus-host disease (GVHD) prophylaxis. Cyclosporine was administered according to institutional practice, and blood concentration measurements obtained during routine therapeutic drug monitoring were used to develop and evaluate a model-informed precision dosing algorithm.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive accuracy of individualized cyclosporine dosing models.
Time Frame: up to 6 months
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.
up to 6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Yasmin medhat munir Mohamed

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 1, 2026

Primary Completion (Estimated)

January 1, 2027

Study Completion (Estimated)

June 1, 2027

Study Registration Dates

First Submitted

July 2, 2026

First Submitted That Met QC Criteria

July 8, 2026

First Posted (Actual)

July 10, 2026

Study Record Updates

Last Update Posted (Actual)

July 10, 2026

Last Update Submitted That Met QC Criteria

July 8, 2026

Last Verified

July 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • HPM CYCLOSPORINE BMT

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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