开发用于吸入治疗的共形系统-第1部分:从模型预测到临床成功
Eleonore Fröhlich1,2, Aurora Bordoni1, Nila Mohsenzada1
1Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria.
Pharmaceutics
|July 30, 2025
概括
机器学习准确地预测了吸入治疗的共同形态系统,简化了药物开发. 这种经过实验验证的方法有助于为结核病等治疗方法创建稳定的干粉配方.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 正在改变制药过程.
- 开发了一种ML模型,用于预测吸入治疗的二元共形系统 (COAMS).
- 评估了该模型在制造合适的干粉配方方面的能力.
研究的目的:
- 实验验证一个ML模型用于预测COAMS形成.
- 开发和描述用于结核病吸入治疗的利法皮辛 - 乙墨托尔 COAMS.
- 评估开发的COAMS的气溶和溶解特性.
主要方法:
- 对18种活性药物成分 (API) 组合进行了使用共磨和X射线衍射的扩展实验验证.
- 通过喷雾干燥和共同磨粉来开发一种利芬素-乙醇 (RIF-ETH) COAMS.
- 评估气溶性质 (例如,发射剂量,细颗粒分数) 和溶解概况.
主要成果:
- 机器学习模型实现了35个API的79%的预测准确度;实验验证产生了72%的准确度.
- 稳定的RIF-ETH COAMS成功地使用喷雾干燥和共同磨粉.
- 联合磨的RIF-ETH COAMS表现出优越的气溶性和更可重复的溶解特性.
结论:
- 机器学习是预测COAMS的一个有价值的工具,可以减少实验查时间和成本.
- 开发的RIF-ETH COAMS显示出通过吸入治疗结核病的前景.
- 这种预测建模方法加速了有效的吸入疗法的开发.
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