关闭设计-制造-测试-分析循环:实验和预测之间的相互作用推动了PROTAC的生物可用性.
Zulma Santisteban Valencia1, Jennifer Kingston2, Filip Miljković3
1Assays, Profiling and Cell Sciences, Discovery Sciences, R&D, AstraZeneca, Mölndal SE-431 83, Sweden.
Journal of medicinal chemistry
|November 8, 2024
概括
像PROTEolysis-TTargeting Chimeras (PROTACs) 这样的新药模式需要更新口服生物可用性预测. 染色学测定和机器学习模型有助于优化这些复杂的分子用于药物开发.
科学领域:
- 药物的发现和开发.
- 药物化学 药物化学
- 药理动力学 药理动力学
背景情况:
- 新型药物模式的出现,包括PROTAC和,为向以前难以处理的生物目标提供了新的机会.
- 这些先进模式的独特尺寸和化学特性与传统的小分子框架有所不同,挑战了预测口服生物利用性的现有模型.
- 越来越需要创新的方法和预测模型来适应药物开发中更大,更复杂的分子的复杂性.
研究的目的:
- 描述高容量染色体物理化学试验的实施,以优化药物发现中的口服生物可用性.
- 解释这些测试中的实验数据如何为指导化合物合成的机器学习模型提供信息和改进.
- 确定最佳的物理化学性质,特别是log D值,以实现PROTACs的口服生物可用性.
主要方法:
- 采用两种染色体物理化学试验的高通量采用,以生成化合物数据.
- 以实验测试结果为指导的代化合物优化.
- 开发和不断完善使用测试数据的内部机器学习模型.
- 分析了来自691个PROTAC和两个项目案例研究的数据.
主要成果:
- 建立了一种工作流程,用于使用染色体测试来代优化候选药物.
- 证明,与传统的小分子相比,PROTACs的最佳口服生物可用性发生在更高的log D值.
- 展示了实验数据和预测建模在推动高效化学优化的协同效应.
结论:
- 染色体物理化学试验为优化新药模式提供了宝贵的数据.
- 通过实验数据改进的机器学习模型有效地指导分子设计阶段,以改善口服生物可用性.
- 这些发现为口服生物利用率预测制定了新的指导方针,特别是对于像PROTACs这样的大分子.
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