State of art of dose individualization to support tacrolimus drug monitoring: What’s next?
Tacrolimus is an immunosuppressant with a narrow therapeutic index and a high intra- and inter-patient variability showing significant challenges in optimal dosing and monitoring. Historically, pre-dose concentration monitoring and simplified area under the curve measurements have been the standard...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/223500 |
| Acceso en línea: | https://hdl.handle.net/2445/223500 |
| Access Level: | acceso abierto |
| Palabra clave: | Trasplantament renal Immunosupressors Farmacogenètica Kidney transplantation Immunosupressive agents Pharmacogenetics |
| Sumario: | Tacrolimus is an immunosuppressant with a narrow therapeutic index and a high intra- and inter-patient variability showing significant challenges in optimal dosing and monitoring. Historically, pre-dose concentration monitoring and simplified area under the curve measurements have been the standard approach. However, recent advances in pharmacokinetic modeling have improved individualized dosing strategies, moving beyond empirical methods. This review explores the evolving landscape of Tacrolimus therapeutic drug monitoring, focusing on advanced modeling techniques that support personalized dosing. Key methodological approaches include Population Pharmacokinetic (PopPK) modeling, Bayesian prediction, Physiologically-Based Pharmacokinetic (PBPK) modeling, and emerging machine learning and artificial intelligence technologies. While no single method provides a perfect solution, these approaches are complementary and offer increasingly sophisticated tools for dose individualization. The review critically examines the potential and limitations of current modeling strategies, highlighting the complexity of translating advanced statistical and mathematical techniques into clinically accessible tools. A significant challenge remains the gap between sophisticated modeling techniques and the practical usability for healthcare professionals. The need for user-friendly platforms is emphasized, with recognition of existing commercial solutions while also noting their inherent limitations. Future directions point towards more integrated, intelligent systems that can bridge the current technological and practical gaps in personalized immunosuppressant therapy. |
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