时间标签：数据日期 2026-10-31 · 生成时间 2026-11-01 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/10/28–2026/10/31 ｜ 情报发生日期 2026-10-31
研究问题
Jacobson1 Received: 23 April 2026 / Accepted: 7 September 2026 © The Author(s) 2026 Abstract Background and Objectives The gut microbiome contributes to interindividual variability in drug pharmacokinetics; however, its role in guiding dose adjustments remains unclear.；This study aimed to characterize the gut microbiome effect on MPA pharmacokinetics and determine if these effects neces- sitate dose modifications.；However, the effect of the gut microbiome on immunosuppressant pharmacokinetics Weihua Guan: Deceased Extended author information available on the last page of the article A.
方法
Stool samples were collected within 2 days of pharmacokinetic sampling for microbiome shotgun metagenomic sequencing.；This pharmaco-microbiome study aimed to identify gut bacterial species affecting the pharma- cokinetics and systemic exposure of MPA, and to translate these findings into microbiome-informed dosing strategies for kidney transplant recipients. 2 Materials and Methods 2.1 Study Design and Patient Enrollment We enrolled 84 adult kidney transplant recipients receiving oral MMF and immediate-release tacrolimus in a pharmaco- microbiome study (MISSION; NCT04953715).；The study included two groups: an early-transplant cohort (n = 37) enrolled within 90 days post-transplant and a late-transplant cohort (n = 47) enrolled 3–10 years post-transplant.；Written informed consent was obtained from all participants, and the study was approved by a centralized Institutional Review Board (
核心结论
Machine learning was used to identify microbial species associated with pharmacokinetic variability.；Results Five bacteria were associated with the magnitude or duration of enterohepatic circulation (EHC).；High relative abundance of Eubacterium callanderi increased EHC and MPA exposure by 29–63%, requiring a 17% lower MMF dose. ‖ 结论：Rather than offering an immediately deployable dosing algorithm, our findings establish a framework that may help identify kidney transplant recipients who may benefit from close therapeutic drug monitoring or individualized dosing strategies.
局限
A major gap in knowledge is determining whether these effects warrant per- sonalized dose modifications.；The relative bioavailability was determined byFrel = Frel,ref × ( mycophenolateDOSE 1000 ) DoseEffect a Statistically significant and clinically relevant b Statistically significant with uncertain clinical relevance Parameter Estimate (RSE%) Bootstrap median [95% CI] ka (h–1 ) 2.08 (21.4%) 2.1 [1.56, 2.83] MPAG formation CL (L/h/70 kg) 17.13 (2.3%) 17.25 [15.16, 20.17] AcMPAG formation CL (L/h/70 kg) 0.09 (1.8%) 0.09 [0.08, 0.1] CLr MPA (L/h/67.6 mL/min/1.73 m2 ) 0.08 (3%) 0.08 [0.07, 0.1] Vc MPA (L/70 kg) 20.96 (3.1%) 20.85 [17.63, 25.43] Q MPA (L/h/70 kg) 18.19 (2.1%) 17.83 [15.03, 2
对本项目的价值
★☆☆☆☆ 菌群是可远程部署、成本最低的干预变量，但因果性与个体化响应仍是主要瓶颈。
全文来源
出版商 OA PDF（正文 75,334 字符，已过 DOI 逐字 / 标题指纹归属校验）
原文题录
- 英文标题：Microbiome-Mediated Pharmacokinetics and Informed Dosing of Mycophenolate in Kidney Transplantation.
- 期刊与标识：Clin Pharmacokinet · PMID 42955188 · DOI 10.1007/s40262-026-01707-4
A 级证据分级：A 级（同行评议人体研究）
本文属人体研究证据，但需注意：观察性或小样本干预的结论不等于已证实临床获益；效应量与长期安全性仍需更大规模、更长随访的试验验证。
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