在Adavosertib (抗癌API) 制造中使用的关键有机化合物的可溶性建模
Matthew Blair1, Mazaher M Chalchooghi2, Robert J Cox2
1Institute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, The King's Buildings, Edinburgh EH9 3FB, United Kingdom.
ACS omega
|July 14, 2025
概括
使用非随机双液体细分活动系数 (NRTL-SAC) 模型进行预测性溶解度建模有助于药物开发. 这项研究应用了NRTL-SAC来优化抗癌药物Adavosertib及其中间体的过程.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 物理化学 物理化学
- 计算化学计算化学
背景情况:
- 有机化合物的可溶性对于制药制造过程如反应和结晶至关重要.
- 预测性溶解度建模将新分子实体 (NME) 的实验工作降至最低.
研究的目的:
- 应用非随机双液体细分活动系数 (NRTL-SAC) 模型来描述参与Adavosertib生产的六种有机化合物的可溶性.
- 利用NRTL-SAC模型来估计融温度和聚变度,克服直接测量的挑战.
主要方法:
- 使用非随机的双液体细分活动系数 (NRTL-SAC) 模型.
- 将模型应用于与Adavosertib (AZD1775) 生产相关的六种特定有机化合物.
- 使用NRTL-SAC模型估计的化温度和聚变度.
主要成果:
- 该NRTL-SAC模型成功地描述了六种有机化合物的可溶性.
- 该模型为这些化合物提供了可靠的化温度和聚变度估计.
- 这种方法绕过了与热性质的直接实验测量相关的困难.
结论:
- 在药物开发中,NRTL-SAC模型是预测有机化合物的可溶性和热性质的宝贵工具.
- 这种预测能力可以简化新药的过程开发,如Adavosertib.
- 这项研究证明了计算建模在克服制药科学中的实验局限性的实用性.
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