对人类的药物重新定位MKN45胃癌小鼠模型使用深度学习AI和实验验证
Ghazaleh Ghavami1, Ramin Ebrahimi Kiasari1, Setareh Ghavami1
1Drug Design and Bioinformatics Unit, Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 13164, Iran.
European journal of pharmacology
|February 4, 2026
概括
人工神经网络确定了阿米特里普提林氧化物和植物作为新的胃癌疗法. 这些药物在体内表现出强烈的疗效,与西斯相比,毒性降低.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 胃癌 (GC) 需要创新的治疗策略.
- 现有的药物重新利用为新型治疗提供了一个有希望的途径.
研究的目的:
- 利用人工神经网络 (ANN) 来识别可用于胃癌治疗的现有药物.
- 通过计算方法确定候选药物的疗效和安全性.
主要方法:
- 开发了ANN模型,以根据药物基因组学描述符预测DrugBank中的候选药物.
- 排名第一的化合物经过了细胞毒性和毒性的体外查.
- 有希望的线索在体内使用MKN-45异种移植小鼠模型进行了验证,并评估了瘤标记物和点基因.
主要成果:
- 阿米特里普提林氧化物和植物在体外显示出高强度和选择性.
- 这两种化合物在体内显著抑制瘤生长,其疗效与西斯普拉丁相当或超过.
- 与西斯胺不同,这些候选药物促进了体重增加,表明其安全性更好,系统毒性降低. 它们还抑制了关键的瘤标记物 (Ki67,CD44) 和茎基因.
结论:
- 通过ANN驱动的药物重定向,成功地确定了阿米酸和植物作为有效的胃癌药物候选者.
- 这些化合物在体内表现出强大的疗效,并且与目前的标准治疗方法西斯相比显著改善了毒性概况.
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