使用基因表达签名数据对异常性进行药物重新定位
Pawan Kumar1, Deepak Sheokand1, Annu Grewal1
1Toxicology and Computational Biology Group, Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001.
Bioinformation
|September 1, 2023
概括
这项研究通过分析基因表达数据,重新利用现有的治疗药物. 它确定了21种化合物与标准药物相比,对目标具有更好的结合亲和力,为耐药提供了新的治疗选择.
科学领域:
- 神经学 神经学
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 是一种常见的神经系统疾病,具有重大负担,30%-40%的患者患有耐药性.
- 遗传性或异常性占病例的40%,这突显了需要新的治疗策略的需要.
研究的目的:
- 为了进行基因表达基因药物重新定位研究.
- 确定治疗耐药性和异常性的潜在候选药物.
主要方法:
- 从基因表达综合数据库中检索了的基因表达数据.
- 利用连接地图 (CMAP) 获取与药物相关的基因表达数据.
- 采用R脚本来预测309种针对的药物化合物.
- 对关键目标进行了分子对接:电压通通道子单元α2 (Nav1.2),GABA受体α1-β1和电压通道α1G (Cav3.1).
主要成果:
- 预测了309种能够改变相关基因表达特征的药物化合物.
- 确定了21种药物化合物,与标准药物 (Carbamazepine,Clonazepam,Pregabalin) 相比,它们对目标具有更高的结合 afinity.
- 突出了埃尔戈加尔西费罗尔,奥克萨普罗辛,弗卢纳利津,三利丁和胺作为有前所未有的抗活动的有前途的候选人.
结论:
- 基于基因表达的药物重新定位是识别新型治疗的可行策略.
- 几种重新使用的药物化合物显示出向特异性和耐药性的潜力.
- 进一步研究已识别的化合物,如埃尔戈硫醇,oxaprozin,flunarizine,triprolidine和cyproheptadine是必要的临床应用.
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