在聚合物基质上模板核化克洛特里马和凯托
Michael A Bellucci1, Lina Yuan2, Grahame R Woollam3
1XtalPi Inc., 245 Main St, Second Floor, Cambridge, Massachusetts 02142, United States.
Molecular pharmaceutics
|August 20, 2024
概括
使用聚合物表面的模板核化增强了活性药物成分 (API) 结晶. API-聚合物相互作用能量准确预测结晶结果,指导药物开发的最佳模板的选择.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 制药科学 制药科学
背景情况:
- 模板表面极大地影响活性药物成分 (API) 结晶,影响核形成,生长和形态.
- 模板核化提供了一种控制结晶动力学和多态选择的策略,特别是对于具有挑战性的药物分子.
- 了解API模板交互是设计有效结晶过程的关键.
研究的目的:
- 调查聚合物模板对克洛特里马和的结晶的影响.
- 开发和验证用于预测API-聚合物相互作用和结晶结果的计算工作流.
- 确定关键描述符,将API-聚合物相互作用与异质结晶结果相关联.
主要方法:
- 使用12个聚合物模板图书馆在二烯中进行克洛特里马和二烯的实验结晶.
- 开发一个计算工作流程,将分子动力学 (MD) 模拟与衍生相互作用描述器结合起来.
- 应用14个机器学习模型,根据API-聚合物相互作用能量,结合和粗度相似性来预测结晶结果.
主要成果:
- 在预测实验结晶结果方面,通过简单地根据API-聚合物相互作用能量描述器对聚合物进行排名,可以达到92%的准确性.
- 随机森林模型在预测两个API的结晶结果方面表现出最高的准确性.
- 特性重要性分析证实,能量描述符是异质结晶结果最重要的预测因素.
结论:
- API-聚合物相互作用能量与异质结晶结果有很强的相关性.
- 计算建模和机器学习为预测和优化模板结晶过程提供了强大的工具.
- 这种方法促进了用于控制API结晶和多态选择的聚合物模板的合理设计.
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