一个可药物向的疾病发现战略 (DTDS):将类风湿性关节炎作为一个案例
Chaoqun Liu1, Shuai Zhao1, Liansheng Qiao1
1Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
一项新的战略结合了机器学习和转录学来发现特定疾病的可药物点,成功地确定了PSMB9和PTPRC作为潜在的类风湿性关节炎治疗方法.
科学领域:
- 生物医学研究的研究.
- 药物发现 药物发现
- 计算生物学是一种计算生物学.
背景情况:
- 在药物开发中,确定既具有疾病特异性,又具有药物可用性的有效可用药物标是最大的挑战.
- 当前的方法往往侧重于药用性或疾病特异性,但不是两者兼而有之,限制了准确性.
- 类风湿性关节炎 (RA) 仍然是一个没有完全治愈的自身免疫性疾病,这凸显了对新型治疗点的需求.
研究的目的:
- 提出和验证一个新的"疾病可药物向发现策略" (DTDS).
- 整合机器学习用于药物可用目标预测,并将转录组学用于特定疾病的目标识别.
- 识别和实验验证类风湿性关节炎的新可用药物标.
主要方法:
- 构建了类风湿性关节炎 (RA) 的蛋白质-蛋白质相互作用网络作为背景.
- 开发了使用已知RA可用药和不可用药目标的拓参数的分类模型.
- 应用组织和细胞水平的转录组学,从预测的可药物向中识别RA特异性向.
- 通过体外实验验验证了确定的目标 (PSMB9,PTPRC).
主要成果:
- 使用机器学习模型,从264个RA相关目标中预测了168个潜在的可用药物目标.
- 在168个潜在目标中通过转录组学分析确定了40个RA特异性目标.
- 证实PSMB9和PTPRC是潜在的RA可用药物的标,通过体外抑制炎症和突细胞增殖.
结论:
- DTDS策略有效地结合了机器学习和转录学,以准确地发现可用药物的目标.
- 证实PSMB9和PTPRC是治疗类风湿性关节炎的有希望的可用药物标.
- DTDS方法为系统发现各种疾病的潜在治疗点提供了可靠的框架.
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