通过虚拟多态选和有针对性的结晶,发现了最稳定的腺受体对手形式
Dedong Wu1, Emma S E Eriksson2, Sten O Nilsson Lill2
1Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Boston, USA.
这项研究引入了一种新的计算方法,结合了虚拟多态选和有针对性的结晶,以快速识别药物开发中最稳定的晶体形式. 这种方法加快了最佳候选药物的发现,并降低了制药开发中的风险.
科学领域:
- 固态化学 固态化学
- 计算材料科学 计算材料科学
- 制药科学 制药科学
背景情况:
- 确定最稳定的晶体形式 (多态) 对于药物开发至关重要,影响疗效和安全.
- 传统的多态选方法可能耗时且样本密集.
研究的目的:
- 开发和验证一种创新的计算方法,以快速识别热力学上最稳定的晶体形式.
- 在早期药物发现中,简化活性药物成分 (API) 的结晶过程.
主要方法:
- 使用虚拟多态选与晶体结构预测 (CSP) 技术.
- 采用像COSMO-RS (真实溶剂选模式) 这样的Conductor来指导目标结晶.
- 将综合方法应用于腺受体对抗剂.
主要成果:
- 成功确定了目标化合物的最稳定的多态形式.
- 开发了一种高效的结晶过程,使用最小的样品和缩短时间.
- 在实验结果中证明了CSP和COSMO-RS的预测能力.
结论:
- 集成的in silico和实验方法加快了最佳晶体形式的发现.
- 这种方法为在药物发现的早期选择稳定的多态体提供了科学基础.
- 降低开发风险,提高制药制造效率.
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