风险分析(工程)
钥匙(锁)
计算机科学
风险评估
微尺度化学
数据科学
计算生物学
管理科学
系统工程
生化工程
过程(计算)
价值(数学)
鉴定(生物学)
模拟生物系统
计算模型
过程管理
数据集成
作者
Eric W. Hsu,Kai Wang,Yik Pui Tsang,Jonathan Himmelfarb,Catherine K. Yeung,Edward J. Kelly
标识
DOI:10.1080/17425255.2026.2633283
摘要
MOC systems are currently poised to complement, not replace, established in vitro and modeling approaches for DDI predictions. Near-term value lies in fit-for-purpose contexts of use, supplying physiologically grounded parameters and mechanistic insight to physiologically based pharmacokinetic (PBPK) modeling. With continued progress in addressing key challenges (e.g. physiological scaling, sorptive materials, microscale analytics, variability, throughput, and standardization), MOCs should mature into reliable tools to assist in DDI prediction, and potentially even qualified assays as part of regulatory DDI risk assessment frameworks.
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