Model-based Medication Dosing Assist for Tacrolimus in Kidney Transplantation

NCT ID: NCT07030660

Last Updated: 2025-06-22

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 357 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-05-11
• Study Completion Date: 2025-03-28

Brief Summary

The daily dosing of tacrolimus, the most important immunosuppressant used after kidney transplantation, is a rather complex process in which many factors can have an influence in each individual in a unique way and variable over time. Based on retrospective and simulation studies in many hundreds of kidney transplant patients at the UZ Leuven, we developed a computer program that, based on your individual characteristics (e.g. age, hematocrit, ...) and the routinely measured concentration of tacrolimus in blood, suggests an individual dose to the physician for the next administration of tacrolimus. The physician must always approve (i.e., "validate") the dose of tacrolimus suggested by the computer in the electronic medical prescription before effective administration may occur. This is true for all medications. The physician can also, at any time, not approve the computer-proposed dose of tacrolimus and determine a dose him/herself.

A preliminary study on historical data of more than 300 kidney transplant patients from the UZ Leuven showed that the computer program predicted the dosage of tacrolimus better, reaching the optimal blood concentrations faster and in a higher percentage of patients compared to the classical calculation of the dose.

The purpose of this validation project is to directly compare computer-assisted dosing with the dosing suggested by physicians in order to learn in whom computer-assisted dosing gives a clear additional benefit.

Practical implementation of the study. Fate will determine whether in your case, during the first 14 days (maximum) after transplantation, the dosage of tacrolimus is suggested by the computer program (and validated by the physician; 2/3 chance) or whether the dosage of tacrolimus is determined only by the physician (1/3 chance). The clinical course after kidney transplantation will be identical to all patients according to routine treatment and follow-up. No additional blood draws or other additional tests will be performed; the hospital stay will not be prolonged by this validation study and there are no costs associated with this study.

Possible risks: there are no risks in participating in this study since the dose of tacrolimus administered will always be approved by a doctor (medication can only be prescribed by a medical doctor), regardless of whether the computer program is used or not.

Detailed Description

Tacrolimus (Prograft™, Advagraf™, Astellas), a calcineurin-inhibitor, is globally used as primary immunosuppressive drug in all types of solid organ transplantation (kidney, liver, lung, heart, pancreas, bowel,….). Tacrolimus is characterized by a strongly variable oral absorption (oral bioavailability) that necessitates continuous therapeutic drug monitoring (TDM) of tacrolimus dosing during clinical follow-up. Tacrolimus has a critical (narrow) therapeutic index whereby clinicians use pre-dose blood tacrolimus levels (C0) to guide dose adaptations aimed at predefined target concentration ranges that vary with time after grafting and according to clinical conditions. Attaining target concentration ranges is important to avoid rejection and toxicity. Clinicians currently execute tacrolimus dose adaptations based on (previous) experience, varying (pharmacological) knowledge about variables that affect tacrolimus disposition and clinical circumstances.

Based on the exploratory analysis of a test set of 315 kidney transplant patients in the University Hospitals Leuven with extensive tacrolimus PK data available, the accuracy and precision of this clinical approach was shown to be suboptimal. At day 2 post-transplantation clinicians underestimate tacrolimus exposure (i.e. overdose) in 63% of patients, overestimate exposure (i.e. underdose) in 23% of patients and correctly predict the dose in 14% of patients. Over time these numbers considerably improve: at day 4, in 30% of patients tacrolimus exposure is correctly predicted/dosed and at day 10, in 24% of patients correctly predicted/dosed, respectively. These numbers appear low, but the clinicians performance is hampered by variable individual experience with the drug, the rapidly changing post-transplantation circumstances (gastrointestinal motility, steroid tapering, peri- and post-transplantation hemodynamic changes, co-medication, comorbidity, …) and lack of pharmacometric (PM) training, which leads to the use of steady-state approaches to execute dose adaptations (previous tacrolimus concentration and corresponding dose) in patients who are effectively not in steady-state.

These problems are not unique for tacrolimus. In pharmacometrics, algorithms for data-driven identification of pharmacokinetic/pharmacodynamic (PK/PD) parameters in a population, a posteriori identification of individual PK/PD parameters and control of concentration and/or effect targets are available. They can be implemented in clinical patient care via software linked to a graphical user interface operable by clinicians lacking formal pharmacometric training. We hypothesize that such a software will help clinicians, in a safe environment, to significantly increase precision and decrease bias while aiming for the optimal individualized dosing of tacrolimus (personalized or precision dosing in solid organ recipients.

The use of organ-specific predictive pharmacometric PK models (PM) for tacrolimus that take into account genetic, demographic (age, body weight), biological (hematocrit) and other parameters (time after transplantation, calculated tacrolimus oral clearance from previous dosing) that influence ADME (Absorption, Distribution, Metabolism, Elimination) of tacrolimus, can support clinicians in determining the optimal dose adaptations in routine clinical practice. With model-based dosing support or dosing assistance, quality of clinical care can be potentially improved:

1. Faster achievement of target therapeutic concentrations ranges

2. Attenuation of "off-target concentrations" (sub- and supra-therapeutic)

3. Reduction of intra-patient variability of C0 (IPV)

4. Less tacrolimus pre-dose trough concentration measurements (except in initial phase)

5. More accurate dose adjustments in the presence of known (and unknown) inhibitors / inducers of tacrolimus ADME (drug-drug interactions; DDI)

6. Potential pharmaco-economic advantage

7. Improved drug tolerance / quality of life patients (less adverse effects)

8. Optimization of clinical workflow (automation clinical decision tree in KWS)

9. Improved patient safety (traceable validated decision process)

10. Support, education and (learning) feedback for medical trainees

Based on the high-grade granularity of the tacrolimus clinical PK data repository and the acquired knowledge from in vivo and ex vivo pharmacokinetic and pharmacogenetic studies about tacrolimus disposition, a predictive PM model for kidney transplantation was developed. Simultaneously, a "mathematical engine" was constructed in R™ (Ross Ihaka and Robert Gentleman). This generic mathematical engine allows construction and execution of different types of organ-specific (in case of tacrolimus) or drug-specific PM-based models to support the clinical TDM process.

The specific PM PK model for tacrolimus in de novo kidney transplantation will be validated as a proof-of-concept index test model in a prospective randomized study in the nephrology department of the university hospitals Leuven (PI Prof dr Dirk Kuypers). The primary endpoints of this study will be a selection of operational parameters and dosing accuracy. Study design and size (statistical power) will not allow evaluation of health-related or medical outcome (e.g. acute rejection, graft and patient survival). Because of the strong practical clinical orientation of the application, a bi-directional integrative software communication link was established between the hosptial electronic patient file (EPF) [KWS/EMV (Klinisch Werk Station / Elektronisch Medisch Voorschrift] module and the mathematical engine with the PM PK model.

Conditions

Kidney Transplantation

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Intervention arm

Model-Informed Precision Dosing of tacrolimus

Group Type: ACTIVE_COMPARATOR

Model-Informed Precision Dosing (MIPD)

Intervention Type: OTHER

Model-Informed Precision Dosing (MIPD) of tacrolimus in kidney transplantation

Control arm

Physician-based dosing of tacrolimus

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Model-Informed Precision Dosing (MIPD)

Model-Informed Precision Dosing (MIPD) of tacrolimus in kidney transplantation

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 18 years of age and older
• single kidney transplantation
• treated with tacrolimus, mycophenolate mofetil and corticosteroids

Exclusion Criteria

• combined organ transplantation
• ongoing tacrolimus treatment prior to study enrollment
• requirement for tacrolimus target concentration ranges outside the study range (12-15 ng/mL)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

KU Leuven

OTHER

Sponsor Role: lead

Responsible Party

Dirk R.J. Kuypers

Head of Department of Nephrology and Renal Transplantation

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Dirk Kuypers, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

KU Leuven

Locations

University Hospitals Leuven

Leuven, , Belgium

Countries

Belgium

References

Faelens R, Luyckx N, Kuypers D, Bouillon T, Annaert P. Predicting model-informed precision dosing: A test-case in tacrolimus dose adaptation for kidney transplant recipients. CPT Pharmacometrics Syst Pharmacol. 2022 Mar;11(3):348-361. doi: 10.1002/psp4.12758. Epub 2022 Feb 2.

Reference Type: BACKGROUND

PMID: 35020971 (View on PubMed)

Other Identifiers

UZ Leuven S63463

Identifier Type: -

Identifier Source: org_study_id