对临床前药物动力学数据的大规模分区模型研究及其对药物发现中化合物选的影响
Peter Zhiping Zhang1, Jeanine Ballard1, Facundo Esquivel Fagiani1
1Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB), MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States.
Molecular pharmaceutics
|February 17, 2025
概括
区间PK (CPK) 和自动剂量预测 (ADP) 的自动计算框架使得人类剂量预测的可靠性. 不同的模拟假设显著影响预测的人类剂量,暴露和Cmax.
科学领域:
- 药理动力学和药物发现
- 计算生物学 计算生物学
- 翻译医学是一种翻译医学.
背景情况:
- 准确的人类剂量预测对于药物发现至关重要,它依赖于临床前静脉注射 (IV) 药物动力学 (PK) 数据分析.
- 非分区分析 (NCA) 简化了PK数据的解释,但省略了曲线形状的细节,而分区分析提供了全面的见解,但很难用于高通量选.
研究的目的:
- 开发和验证自动计算框架,隔间PK (CPK) 和自动剂量预测 (ADP),用于可扩展的基于模型的IVPK数据分析.
- 量化评估各种PK建模和仿真方法对药物发现中人类剂量预测准确性的影响.
- 通过高效和大规模的人类剂量预测来支持化合物分类和优化.
主要方法:
- 开发CPK和ADP框架,用于IV PK数据的自动分区建模,翻译和模拟.
- 从2013年至2023年期间在MRL测试的化合物中分析临床前PK数据.
- 对不同的PK建模和模拟方法进行比较评估,以确定它们对人类剂量预测的影响.
主要成果:
- 通过CPK-ADP框架,可以对IV PK数据进行高效,大规模的分析.
- 虽然动物PK参数估计显示方法之间的差异很小,但预测的人类剂量,暴露和Cmax受到模拟假设的显著影响.
- 模拟假设的选择对人类剂量预测的影响比PK建模方法本身更大.
结论:
- 在药物发现中,CPK-ADP为复杂,大规模的人类剂量预测提供了一个有效的平台.
- 将吸收,分布和清除数据与CPK-ADP集成,可以在化合物开发过程中支持知情决策.
- 该研究强调了模拟假设在准确预测人体剂量的关键作用.
相关概念视频
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Two primary types of compartment models are recognized: mammillary and catenary. The more...
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