早期药物发现期间对等级排序化合物的以人为本的多参数优化得分:基于临床PK数据的PBPK模型的验证
Yanran Wang1, Wenyi Wang1, Fabio Broccatelli1
1Department of Drug Metabolism and Pharmacokinetics, Genentech, South San Francisco, California 94080, United States.
基于生理学上的药理动力学 (PBPK) 模型使用早期发现数据预测药物疗效. 这种多参数方法可以改善化合物选择,而不是单一的体外参数.
科学领域:
- 药理动力学和药物发现
- 计算机生物学和建模
背景情况:
- 基于生理学的药理动力学 (PBPK) 模型对于预测药物行为至关重要.
- 早期的体外试验数据对于优先考虑具有所需药理动力学 (PK) 概况的候选药物至关重要.
研究的目的:
- 开发和验证一个通用的PBPK模型,仅使用早期发现的体外数据.
- 评估模型根据PK/PD驱动因素对化合物进行排名,并预测临床PK参数的能力.
主要方法:
- 开发了一个通用的PBPK模型,包括早期发现的体外数据.
- 在18个化合物中验证了该模型,而没有特定的化合物配件.
- 根据PK驱动因素 (Cminu,Cmaxu,AUCu) 和单个体外参数进行评估的化合物排名.
主要成果:
- 基于PK/PD驱动器的PBPK模型有效地排序了基于PK/PD驱动器的化合物.
- 使用单个体外参数进行排名的预测能力较低.
- 该模型合理地预测了临床PK参数 (清除率,Vd,Cmax,AUCinf) 和度-时间概况.
结论:
- 这项研究通过使用自下而上的PBPK方法从早期发现数据验证了临床PK预测.
- PBPK建模作为一种强大的多参数优化工具,用于早期药物发现.
- 开发的模型有效指导药物化合物的选择和优化.
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