在机器学习的指导下,开发了通过联合喷雾干燥制备的抗结核吸入干粉
Xiaoyun Hu1,2, Xian Chen1, Ziling Zhou1
1State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.
Pharmaceutics
|February 27, 2026
概括
机器学习加速了可吸入抗结核病 (TB) 药物的开发. 这项研究使用ML来预测喷雾干燥干粉吸入器 (DPI) 的性能,确定最佳的药物-氨基酸组合,以改善肺部输送.
科学领域:
- 制药科学 制药科学
- 药物输送系统 药物输送系统
- 计算化学计算化学
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战.
- 目前的抗结核病药物给药方法存在局限性,包括全身副作用和肺部分布不良.
- 干粉吸入器 (DPI) 为有针对性的肺部输送提供了一个有希望的替代方案.
研究的目的:
- 为了研究含有里法或皮拉津胺和氨基酸的联合喷雾干燥DPI的空气动力学性能.
- 开发和利用机器学习 (ML) 模型来预测DPI性能.
- 加快开发可吸入的抗结核病药物配方.
主要方法:
- 制备72种配方,具有不同的药物-氨基酸组合,摩尔比率和喷雾干燥参数.
- 使用下一代冲击器对空气动力学性能进行评估.
- 开发和应用四个ML模型 (包括XGBoost) 来预测细颗粒剂量 (FPD),细颗粒分数 (FPF),质量中介气动直径 (MMAD) 和几何标准偏差 (GSD).
主要成果:
- 最佳配方包括里法-L-氨酸酸和皮拉津胺-L-氨酸,达到高FPF (73.37%,87.74%) 和最佳MMAD (2.59μm,1.88μm).
- XGBoost 模型表现出极好的预测性能,R2 值高达 0.991.1.
- 分子重量和LogP被确定为影响DPI性能预测的关键特征.
结论:
- 机器学习可用于加快可吸入喷雾干燥抗结核病药物的开发.
- ML模型可以准确预测DPI配方的空气动力学性能.
- 这种方法有助于优化药物-氨基酸组合,以有效的肺部输送.
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