Frontiers in immunology · 2026
Establishment of accurate estimation equations for area under the concentration curves using simultaneous limited sampling for extended-release tacrolimus and mycophenolic acid in kidney transplant recipients.
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Minamisono Kyoko, Nakao Toshimasa, Ohyama Takehiro, Nishida Sho, Sasaki Hajime, Hirose Takayuki, Hotta Kiyohiko, Mieno Makiko, Iwami Daiki
Аннотация
INTRODUCTION: This study aimed to develop a limited sampling strategy (LSS) and predictive equations to accurately estimate the areas under the concentration-time curves (AUC) of extended-release tacrolimus (TAC-ER) and mycophenolic acid (MPA).
METHODS: A retrospective analysis of Japanese kidney transplant recipients yielded 90 TAC-ER AUC0-24 (23 patients) and 80 MPA AUC0-12 (29 patients) datasets, which were randomly split into learning and validation datasets. Training datasets were used to generate the LSS model equations based on multiple linear regression analysis, and the coefficient of determination (R2) was used to assess the goodness of fit of regression models. Validation datasets applied the selected training equations to compute error indices, Passing-Bablok's Kendall's τ, and Bland-Altman limits of agreement, thereby assessing predictive bias, accuracy, and precision.
RESULTS AND DISCUSSION: Four equations (C0-C1-C6, C0-C1-C2-C6, C0-C1-C3-C6, C0-C1-C4-C6) showed strong correlations with the actual AUC (R² > 0.95), with the validation identifying C0-C1-C3-C6 as the most reliable for both TAC-ER and MPA. This study demonstrated that LSS using C0-C1-C3-C6 reliably and accurately estimated both the actual TAC-ER AUC0-24 and MPA AUC0-12 simultaneously in kidney transplant recipients. These equations can be feasibly implemented in outpatient clinical settings to reduce time and cost.
METHODS: A retrospective analysis of Japanese kidney transplant recipients yielded 90 TAC-ER AUC0-24 (23 patients) and 80 MPA AUC0-12 (29 patients) datasets, which were randomly split into learning and validation datasets. Training datasets were used to generate the LSS model equations based on multiple linear regression analysis, and the coefficient of determination (R2) was used to assess the goodness of fit of regression models. Validation datasets applied the selected training equations to compute error indices, Passing-Bablok's Kendall's τ, and Bland-Altman limits of agreement, thereby assessing predictive bias, accuracy, and precision.
RESULTS AND DISCUSSION: Four equations (C0-C1-C6, C0-C1-C2-C6, C0-C1-C3-C6, C0-C1-C4-C6) showed strong correlations with the actual AUC (R² > 0.95), with the validation identifying C0-C1-C3-C6 as the most reliable for both TAC-ER and MPA. This study demonstrated that LSS using C0-C1-C3-C6 reliably and accurately estimated both the actual TAC-ER AUC0-24 and MPA AUC0-12 simultaneously in kidney transplant recipients. These equations can be feasibly implemented in outpatient clinical settings to reduce time and cost.