Bone Marrow TransplantationNCT07695571
Model-Informed Precision Dosing on Cyclosporine Therapy in Hematopoietic Stem Cell Transplant Recipients
An observational study of Bone Marrow Transplantation and AML, run by Yasmin medhat munir Mohamed.
Trial 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. Read more about this study
- Who is it for?
- Ages 2 to 65 years
- Conditions the study is for
- Bone Marrow Transplantation, AML
- How many people
- 300 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2026to 1 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 2 to 65 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- • CsA therapy indicated alone or in combination for GVHD prophylaxis.
- Aged 2-65 years.
- Clinically stable after first HSCT.
Who cannot join
- • Inaccurate sampling or dose administration times.
- Patients with missing key covariates.
- Patients lacking sufficient pharmacokinetic or TDM data
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read the 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.
- Conditions
- Bone Marrow Transplantation, AML
- Keywords
- Clinical Pharmacokinetics, Machine learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: up to 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jul 2026
- Study start1 Aug 2026 (estimated)
- Primary completion1 Jan 2027 (estimated)
- Study completion1 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Bone Marrow TransplantationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yasmin medhat munir Mohamed
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Yasmin medhat munir Mohamed, Helwan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07695571
- Sponsor's protocol ID
- HPM CYCLOSPORINE BMT
- Status last verified
- Jul 2026
- First submitted
- 2 Jul 2026
- Last updated
- 10 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Yang Y, Zhu Y, Xia L, Chai Y, Quan D, Xue Q, Wang Z. Population pharmacokinetics of cyclosporine A in hematopoietic stem cell transplant recipients: A systematic review. Eur J Pharm Sci. 2025 Jan 1;204:106882. doi: 10.1016/j.ejps.2024.106882. Epub 2024 Aug 29.
PubMed 39214318Destere A, Marquet P, Labriffe M, Drici MD, Woillard JB. A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction. Pharm Res. 2023 Apr;40(4):951-959. doi: 10.1007/s11095-023-03507-y. Epub 2023 Mar 29.
PubMed 36991227Irie K, Minar P, Reifenberg J, Boyle BM, Noe JD, Hyams JS, Mizuno T. Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease. Clin Pharmacokinet. 2025 Nov;64(11):1669-1679. doi: 10.1007/s40262-025-01564-7. Epub 2025 Aug 30.
PubMed 40885855Chen K, Wang C, Wei Y, Ma S, Huang W, Dong Y, Wang Y. Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients. Microbiol Spectr. 2025 May 6;13(5):e0049925. doi: 10.1128/spectrum.00499-25. Epub 2025 Mar 31.
PubMed 40162774van Os W, O'Jeanson A, Troisi C, Liu C, Brooks JT, Hughes JH, Tong DMH, Keizer RJ. Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing. CPT Pharmacometrics Syst Pharmacol. 2025 Oct;14(10):1650-1660. doi: 10.1002/psp4.70084. Epub 2025 Jul 30.
PubMed 40734640
Study at a glance
From the registry record, last updated by the sponsor on 10 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Hybrid Population Pharmacokinetic,Machine Learning and Deep Learning Modelling to Predict Dosing for the Individualization of Cyclosporine Therapy in Transplant Recipients
- ClinicalTrials.gov ID
- NCT07695571
- Acronym / sponsor ID
- HPM CYCLOSPORINE BMT
- Conditions
- Bone Marrow Transplantation, AML
- Keywords
- Clinical Pharmacokinetics, Machine learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Yasmin medhat munir MohamedUniversity, hospital or other
- Study start
- 1 Aug 2026 (estimated)
- Primary completion
- 1 Jan 2027 (estimated)
- Study completion
- 1 Jun 2027 (estimated)
- First posted
- 10 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 2 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- • CsA therapy indicated alone or in combination for GVHD prophylaxis.
Key exclusion criteria (3 listed)
- • Inaccurate sampling or dose administration times.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Jul 2026.
- Background: PubMed 39214318Yang Y, Zhu Y, Xia L, Chai Y, Quan D, Xue Q, Wang Z. Population pharmacokinetics of cyclosporine A in hematopoietic stem cell transplant recipients: A systematic review. Eur J Pharm Sci. 2025 Jan 1;204:106882. doi: 10.1016/j.ejps.2024.106882. Epub 2024 Aug 29.
- Background: PubMed 36991227Destere A, Marquet P, Labriffe M, Drici MD, Woillard JB. A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction. Pharm Res. 2023 Apr;40(4):951-959. doi: 10.1007/s11095-023-03507-y. Epub 2023 Mar 29.
- Background: PubMed 40885855Irie K, Minar P, Reifenberg J, Boyle BM, Noe JD, Hyams JS, Mizuno T. Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease. Clin Pharmacokinet. 2025 Nov;64(11):1669-1679. doi: 10.1007/s40262-025-01564-7. Epub 2025 Aug 30.
- Background: PubMed 40162774Chen K, Wang C, Wei Y, Ma S, Huang W, Dong Y, Wang Y. Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients. Microbiol Spectr. 2025 May 6;13(5):e0049925. doi: 10.1128/spectrum.00499-25. Epub 2025 Mar 31.
- Background: PubMed 40734640van Os W, O'Jeanson A, Troisi C, Liu C, Brooks JT, Hughes JH, Tong DMH, Keizer RJ. Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing. CPT Pharmacometrics Syst Pharmacol. 2025 Oct;14(10):1650-1660. doi: 10.1002/psp4.70084. Epub 2025 Jul 30.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Bone Marrow Transplantation and AML, run by Yasmin medhat munir Mohamed.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 10 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yasmin Mohammed, demonstrater assitant (+201093201956, Yasmin.Mohammed@pharm.capu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive accuracy of individualized cyclosporine dosing models. (time frame: up to 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07695571, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Jul 2026.