在设计点对剂量预测的方向
Dries Van Rompaey1, Siladitya Ray Chaudhuri2, Mazen Ahmad1
1In Silico Discovery, Janssen Pharmaceutica NV, Beerse 2340, Belgium.
Journal of medicinal chemistry
|December 12, 2024
概括
这项研究提出了一个 in silico人类剂量预测 (HDP) 策略,使用机器学习来估计药物剂量在发现早期. 该方法有助于优先考虑化合物,减少不那么有前途的候选物的合成.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 药理动力学 药理动力学
背景情况:
- 人类剂量预测 (HDP) 对于优化临床前药物发现中的化合物至关重要.
- 准确的HDP可以简化可行的候选药物的识别.
- 当前的方法可能需要在过程的早期获得重要的实验数据.
研究的目的:
- 开发一种专门用于化HDP的战略,用于分类复合材料设计.
- 创建一个模型估计人类剂量基于化学结构的目标暴露.
- 评估和纳入模型不确定性可靠的剂量估计.
主要方法:
- 机器学习模型被构建用于估计大鼠的药理动力学.
- 鼠类的药理动力学被测量以预测人类的药理动力学.
- 在缺乏强度数据的情况下,用于早期HDP的10nM自由度目标.
- 进行了不确定性分析,并将其推广到最终的剂量估计.
主要成果:
- 与非结构化方法相比,in silico HDP策略可以减少具有不利性质的化合物的合成.
- 该模型提供了基于化学结构的初步剂量估计.
- 不确定性量化提供了关于HDP估计可靠性的指导.
结论:
- 在 silico HDP 开发的战略为早期化合物优化提供了有价值的工具.
- 这种计算方法通过优先考虑化合物,有助于高效的临床前药物发现.
- 虽然有利,但该策略需要继续进行实验验证和测试.
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