Therapeutic drug monitoring · 2025
Simultaneous Prediction of Area Under the Curves of Mycophenolic Acid and Its Metabolites and Enterohepatic Recirculation in Kidney Transplant Recipients.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Mohamed Moataz E, Saqr Abdelrahman, Onyeaghala Guillaume, Remmel Rory P, Staley Christopher, Dorr Casey R, Teigen Levi, Guan Weihua, Madden Henry, Munoz Julia, Sanchez Bryan, Vo Duy, El-Rifai Rasha, Oetting William S, Matas Arthur J, Israni Ajay K, Jacobson Pamala A
Аннотация
BACKGROUND: Therapeutic drug monitoring of mycophenolic acid (MPA) is limited due to the requirement for intensive pharmacokinetic sampling to assess the area under the curve (AUC). Limited sampling strategies (LSS) offer a practical alternative; however, enterohepatic recirculation (EHR) affects prediction accuracy and precision. This study is the first to develop LSS models capable of simultaneously predicting the AUC of MPA, its metabolites [mycophenolic acid glucuronide (MPAG) and acyl mycophenolic acid glucuronide (Acyl-MPAG)], and MPA EHR in kidney transplant recipients (KTRs).
METHODS: Intensive pharmacokinetic sampling was conducted in 84 adult KTRs receiving mycophenolate mofetil. MPA AUC 0-12 was calculated, and MPA EHR was determined. During the development of the LSS models, a balanced representation of patients with high and low EHR was ensured. Multiple linear regression was used to develop AUC prediction models for MPA, MPAG, and Acyl-MPAG, as well as an EHR prediction model. The best models were selected based on prediction performance, the highest prediction concordance, and the shortest interval between the first and last samples.
RESULTS: Three models for AUC 0-12 prediction were identified, incorporating 4, 5, and 6 concentration timepoints. The LSS model with 6 concentrations demonstrated the best performance, with excellent prediction concordance (100% for MPA and MPAG, and 93% for Acyl-MPAG). The EHR prediction model included 4 concentrations and exhibited an ∼80% prediction concordance. An online calculator was developed for these models.
CONCLUSIONS: The developed LSS models simultaneously predict MPA, MPAG, and Acyl-MPAG AUC 0-12 using the same timepoints with high accuracy and precision. MPA EHR can be predicted using 4 concentration timepoints. The inclusion of late concentration timepoints is essential for the high predictive performance of LSS models.
CLINICAL TRIAL NOTATION: clinicaltrials.gov, NCT04953715.
METHODS: Intensive pharmacokinetic sampling was conducted in 84 adult KTRs receiving mycophenolate mofetil. MPA AUC 0-12 was calculated, and MPA EHR was determined. During the development of the LSS models, a balanced representation of patients with high and low EHR was ensured. Multiple linear regression was used to develop AUC prediction models for MPA, MPAG, and Acyl-MPAG, as well as an EHR prediction model. The best models were selected based on prediction performance, the highest prediction concordance, and the shortest interval between the first and last samples.
RESULTS: Three models for AUC 0-12 prediction were identified, incorporating 4, 5, and 6 concentration timepoints. The LSS model with 6 concentrations demonstrated the best performance, with excellent prediction concordance (100% for MPA and MPAG, and 93% for Acyl-MPAG). The EHR prediction model included 4 concentrations and exhibited an ∼80% prediction concordance. An online calculator was developed for these models.
CONCLUSIONS: The developed LSS models simultaneously predict MPA, MPAG, and Acyl-MPAG AUC 0-12 using the same timepoints with high accuracy and precision. MPA EHR can be predicted using 4 concentration timepoints. The inclusion of late concentration timepoints is essential for the high predictive performance of LSS models.
CLINICAL TRIAL NOTATION: clinicaltrials.gov, NCT04953715.