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当前小分子药物研发候选物人体 PK 预测方法:IQ 人体 PK 预测工作组调查结果。

Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey.

机构信息

The Healthcare Business of Merck KGaA, Frankfurter Str. 250, Postcode D50/902, 64293, Darmstadt, Germany.

Eli Lilly and Company, Indianapolis, IN, USA.

出版信息

AAPS J. 2022 Jul 19;24(5):85. doi: 10.1208/s12248-022-00735-9.

Abstract

Accurate prediction of human clearance (CL) and volume of distribution at steady state (V) for small molecule drug candidates is an essential component of assessing likely efficacious dose and clinical safety margins. In 2021, the IQ Consortium Human PK Prediction Working Group undertook a survey of IQ member companies to understand the current PK prediction methods being used to estimate these parameters across the pharmaceutical industry. The survey revealed a heterogeneity in approaches being used across the industry (e.g., the use of allometric approaches, differing incorporation of binding terms, and inconsistent use of empirical correction factors for in vitro-in vivo extrapolation, IVIVE), which could lead to different PK predictions with the same input data. Member companies expressed an interest in improving human PK predictions by identifying the most appropriate compound-class specific methods, as determined by physiochemical properties and knowledge of CL pathways. Furthermore, there was consensus that increased understanding of the uncertainty inherent to the compound class-dependent prediction would be invaluable in aiding communication of human PK and dose uncertainty at the time of candidate nomination for development. The human PK Prediction Working Group is utilizing these survey findings to help interrogate clinical IV datasets from across the IQ consortium member companies to understand PK prediction accuracy and uncertainty from preclinical datasets.

摘要

准确预测小分子候选药物的人体清除率 (CL) 和稳态分布容积 (V) 是评估有效剂量和临床安全边际的重要组成部分。2021 年,IQ 联盟人体 PK 预测工作组对 IQ 成员公司进行了一项调查,以了解制药行业目前用于估算这些参数的 PK 预测方法。调查显示,行业内使用的方法存在异质性(例如,使用体表面积法、不同程度地纳入结合术语、以及体外-体内外推法(IVIVE)中经验校正因子的不一致使用),这可能会导致相同输入数据的不同 PK 预测。成员公司表示有兴趣通过确定最适合特定化合物类别的方法来改进人体 PK 预测,这取决于化合物的物理化学性质和 CL 途径的知识。此外,人们一致认为,增加对化合物类别相关预测中固有不确定性的理解,将有助于在候选药物开发提名时,辅助沟通人体 PK 和剂量不确定性。人体 PK 预测工作组正在利用这些调查结果来帮助询问 IQ 联盟成员公司的临床 IV 数据集,以了解从临床前数据集得出的 PK 预测准确性和不确定性。

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