在生成药物设计中整合药理动力学和定量系统药理学方法
Helle W van den Maagdenberg1, Jikke de Mol van Otterloo1, J G Coen van Hasselt1
1Leiden Academic Centre of Drug Research, Leiden University, 2333, Leiden, The Netherlands.
这项研究将药理动力学 (PK) 预测整合到生成性药物设计中,以优化癌症免疫治疗的候选药物. 通过将PK/PD建模与AI相结合,研究人员可以设计具有更高效率和向性质的分子.
科学领域:
- 计算机化药物发现.
- 药理动力学和药理动力学
- 免疫瘤学药物开发
背景情况:
- 生成性药物设计往往优先考虑功效而不是药理动力学特性.
- 结合药理动力学 (PK) 和药理动力学 (PD) 的综合方法对于成功的药物发现至关重要.
研究的目的:
- 将PK属性预测集成到DrugEx生成药物设计框架中.
- 使用定量系统药理 (QSP) 模型探索产生的化合物的药理动力学 (PD).
- 为了产生具有特定PK配置文件和对腺氨酸A2A受体 (A2AAR) 高 afinity 的分子.
主要方法:
- 开发了定量结构-属性关系 (QSPR) 模型来预测PK参数 (清除率,分布量,未结合分数) 和A2AAR亲和力.
- 利用强化学习框架,根据预测的PK和亲和力评分化合物.
- 采用QSP模型来模拟候选分子的瘤生长抑制概况.
主要成果:
- 优化A2AAR亲和力,同时操纵PK特性,改变了生成的分子支架.
- 预测的PK参数与观察到的物理化学性质差异相关.
- 模拟显示了各种化合物特性对预测瘤体积的影响.
结论:
- 将亲和力预测与PK/PD建模相结合的集成工作流提供了用于生成药物设计的新方法.
- 这种方法可以作为下一代计算药物设计的模板,优化疗效和类似药物的特性.
- 该研究强调了在药物设计过程的早期考虑PK特性的重要性,以改善治疗结果.
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