基于ML的综合策略识别了针对异常性肺纤维化症的药物重定位
Faheem Ahmed1, Anupama Samantasinghar1, Myung Ae Bae2
1Department of Mechatronics Engineering, Jeju National University, Jeju 63243, Republic of Korea.
ACS omega
|July 15, 2024
概括
本研究引入了一种机器学习策略,以确定现有药物的新用途,以治疗异常性肺纤维化 (IPF). 该方法成功地确定了27种潜在的可重复使用药物和15种药物组合用于IPF治疗.
科学领域:
- 生物医学研究的研究.
- 计算机化药物发现.
- 药理学 药理学是指药理学的学科.
背景情况:
- 异常性肺纤维化 (IPF) 影响全球300万,导致肺部痕和渐进性呼吸道疾病.
- 有限的FDA批准的治疗方法和新药开发的高成本需要替代策略,如药物重新定位.
- 目前使用计算工具的药物重定向方法产生较低的命中率.
研究的目的:
- 开发一个集成的机器学习策略,以实现IPF的高效和准确的药物再利用.
- 为了确定潜在的可重复使用的药物和药物组合用于IPF治疗.
- 通过文献,基因组丰富分析 (GSEA) 和途径分析来验证已识别的药物.
主要方法:
- 开发了一个集成的机器学习策略,结合了前端预测,文献综述,GSEA和途径分析.
- 该战略对1480种FDA批准的药物和临床试验中的药物进行了选,针对关键的IPF相关蛋白质.
- 验证包括评估现有的IPF/纤维化药物使用情况,并预测IPF途径的参与.
主要成果:
- 该战略确定了247种潜在药物,其中27种药物被认为是IPF重用最有希望的药物.
- 验证证实,已有72种药物用于IPF,13种用于肺纤维化,20种用于其他纤维状况.
- 途径分析涉及IPF的29个途径,其中11个直接涉及,并建议15种协同作用的药物组合.
结论:
- 开发的机器学习策略显著提高了IPF药物重新定位的准确性.
- 该研究确定了有前途的候选药物和组合,以加速IPF治疗开发.
- 这种方法为发现IPF和相关纤维性疾病的治疗方法提供了一种可行和有效的方法.
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