推进抗癌药物发现:利用代谢学和机器学习来通过模式识别预测作用模式
Mohamad Saoud1, Jan Grau2, Robert Rennert1
1Leibniz Institute of Plant Biochemistry, Dept. of Bioorganic Chemistry, Weinberg 3, 06120, Halle (Saale), Germany.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 21, 2024
概括
代谢学和机器学习预测抗癌药物作用机制 (MoA). 这种方法可以准确地识别前列腺癌细胞中的药物MOA,并提供关于线粒体功能障碍和药物优化的见解.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 确定抗癌药物作用机制 (MoA) 是药物开发中的一个关键瓶.
- 新型抗增殖剂需要有效的方法来阐明MOA.
研究的目的:
- 开发和验证一种基于代谢学的机器学习方法,用于预测抗癌药物MoAs.
- 为了研究MoA预测在不同类型的癌细胞中的可转移性.
- 为了获得对药物诱导的细胞过程的生物化学见解,例如线粒体功能障碍.
主要方法:
- 在中央碳和细胞能量代谢 (CCEM) 中,使用液态染色学-并联质谱法 (LC-MS/MS) 分析低分子量代谢物.
- 机器学习算法的应用以预测基于38种已知的药物治疗的人类前列腺癌细胞 (PC-3) 的代谢概况的MOA.
- 使用乳腺癌和尤文肉瘤细胞系的MOA预测模型的验证.
- 生物化学和脂质组分析,以及分子对接,以确认预测的药物效应.
主要成果:
- 独特的代谢模式成功地区分了38种药物的已知MOA.
- 机器学习模型准确地预测了PC-3细胞中新型抗癌药物候选者的MOA.
- MoA预测模型显示可转移到其他癌细胞类型,尽管预测质量略有下降.
- 获得了关于五环三烯的具体见解,预测了氧化酸化的抑制和对脂生物合成的影响,这些都经过实验证实了.
结论:
- 代谢学与机器学习相结合,为预测抗癌药物MoAs提供了一个强大的策略.
- 这种方法通过快速识别MoA来加速药物发现,并为优化组合药物治疗提供了潜力.
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