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Population dynamics analysis of the interaction between tacrolimus and voriconazole in renal transplant recipients

Affiliation
International Research Center for Precision Medicine, Transformative Technology and Software Services ,Changsha ,Hunan ,China
Sun, Zhi-Hua;
Affiliation
International Research Center for Precision Medicine, Transformative Technology and Software Services ,Changsha ,Hunan ,China
Zhao, Yi-Chang;
Affiliation
International Research Center for Precision Medicine, Transformative Technology and Software Services ,Changsha ,Hunan ,China
Li, Jia-Kai;
Affiliation
The Second Xiangya Hospital ,Central South University ,Changsha ,Hunan ,China
Peng, Fenghua;
Affiliation
Basic Medicine and Clinical Pharmacy, China Pharmaceutical University ,Nanjing ,Jangsu ,China
Yu, Feng;
Affiliation
International Research Center for Precision Medicine, Transformative Technology and Software Services ,Changsha ,Hunan ,China
Zhang, Bi-Kui;
Affiliation
International Research Center for Precision Medicine, Transformative Technology and Software Services ,Changsha ,Hunan ,China
Yan, Miao

Background The concurrent administration of tacrolimus and voriconazole in kidney transplant recipients can lead to drug interactions, potentially resulting in severe adverse reactions. This study aimed to establish a robust population pharmacokinetic model to explore the interaction between tacrolimus and voriconazole in greater depth. Methods Tacrolimus blood samples and laboratory data were prospectively collected from eligible patients enrolled between April 2023 and April 2024, following predefined inclusion and exclusion criteria. Using Phoenix (version 8.1), a pharmacokinetic prediction model was developed. Model performance was assessed using model fitting plots, bootstrap analysis, and visual predictive checks (VPC). Results This study ultimately included 51 eligible patients, with a total of 281 blood samples collected. Analysis revealed a significant negative correlation between voriconazole concentration (Cvrc) and tacrolimus volume of clearance rate (CL), a significant positive correlation between platelets (PLT) and tacrolimus clearance (CL), and a significant negative correlation between blood cells (RBC) and tacrolimus clearance (CL). Conclusion This study successfully established a population pharmacokinetic model for renal transplant patients concurrently receiving tacrolimus and voriconazole. The model demonstrated good predictive performance and offers valuable insights to clinicians for optimizing tacrolimus dosing in this patient population.

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License Holder: Copyright © 2025 Sun, Zhao, Li, Peng, Yu, Zhang and Yan.

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