一种基于模型的方法,可以从明显的合作性中获取内在的信息,并为三元复合形成化合物预测细胞目标占用率
Richard R Stein1, Marianne Fouché1, Jeffrey D Kearns2
1Novartis Institutes for BioMedical Research Basel Switzerland richard.stein@novartis.com hans-joerg.roth@novartis.com.
RSC chemical biology
|July 7, 2023
概括
本研究引入了一个数学模型来量化三元复合形成药物的内在合作性 (α). 该方法通过预测细胞点占用率和提高对具有挑战性的蛋白质点的选择性,有助于早期药物发现.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
背景情况:
- 针对具有挑战性的蛋白质的治疗方法通常涉及三元复合体形成.
- 了解化合物合作性对于优化药物发现和选择性至关重要.
- 内在的合作性 (α) 量化了前结合状态中的亲和力变化.
研究的目的:
- 介绍一种数学建模方法,用于从明显的合作性中估计内在合作性 (α).
- 将模型从生物化学测试扩展到细胞测试.
- 为了将生化功效转化为细胞目标占用率,以验证机制.
主要方法:
- 开发了一种数学模型,用二进制结合亲和力和蛋白质度来估计内在合作性 (α).
- 扩展了模型,以考虑细胞测试中的开放系统 (连接体度).
- 应用该模型来从生物化学效能预测细胞目标占用率.
主要成果:
- 该方法从实验数据准确地估计了内在的合作性 (α).
- 该模型成功地将生物化学发现转化为细胞环境.
- 该方法可以预测行动机制研究的细胞目标占用率.
结论:
- 提出的数学模型提供了一个强大的方法来量化三元复合形成化合物的内在合作性 (α).
- 这种方法对早期药物发现有价值,增强了标选择性和机制验证.
- 该模型有助于将体外功效转化为体内细胞反应.
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