北京大学Zhengwei Xie、Ruimao Zheng、Ning Zhang和Hong Zhou研究组合作在研究中取得进展。他们利用转录谱深度学习预测了药物疗效。相关论文发表在2021年6月17日出版的《自然-生物技术》杂志上。
为了解决药物靶点匮乏的难题，研究人员研发了一种基于深度学习的疗效预测系统（DLEPS），其利用患者基因表达谱变化作为输入来识别候选药物。研究人员利用L1000项目揭示的化学诱导的转录改变对DLEPS进行了校正。研究发现，使用0.74的Pearson相关系数预测了以前未知的转录谱的变化。研究人员测试了三种疾病，并通过实验预测了小鼠疾病模型中的潜在候选药物。
验证表明，perillen、枝孢素苷IV和Trametinib可能分别对肥胖、高尿酸和非酒精性脂肪肝炎疾病产生功效。DLEPS可以有助于对致病机制的理解，并且研究表明MEK-ERK信号通路可作为治疗非酒精性脱脂性肝炎的靶点。该研究结果表明DLEPS是药物重新使用和发现的有效工具。
据悉，靶向特点蛋白的药物研发策略是一种成功的方法，但许多疾病和生物过程缺乏明显的靶标从而难以利用该方法。
附：英文原文
Title: Prediction of drug efficacy from transcriptional profiles with deep learning
Author: Jie Zhu, Jingxiang Wang, Xin Wang, Mingjing Gao, Bingbing Guo, Miaomiao Gao, Jiarui Liu, Yanqiu Yu, Liang Wang, Weikaixin Kong, Yongpan An, Zurui Liu, Xinpei Sun, Zhuo Huang, Hong Zhou, Ning Zhang, Ruimao Zheng, Zhengwei Xie
Issue&Volume: 2021-06-17
Abstract: Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning–based efficacy prediction system (DLEPS) that identifies drug candidates using a change in the gene expression profile in the diseased state as input. DLEPS was trained using chemically induced changes in transcriptional profiles from the L1000 project. We found that the changes in transcriptional profiles for previously unexamined molecules were predicted with a Pearson correlation coefficient of 0.74. We examined three disorders and experimentally tested the top drug candidates in mouse disease models. Validation showed that perillen, chikusetsusaponin IV and trametinib confer disease-relevant impacts against obesity, hyperuricemia and nonalcoholic steatohepatitis, respectively. DLEPS can generate insights into pathogenic mechanisms, and we demonstrate that the MEK–ERK signaling pathway is a target for developing agents against nonalcoholic steatohepatitis. Our findings suggest that DLEPS is an effective tool for drug repurposing and discovery.
DOI: 10.1038/s41587-021-00946-z
Source: https://www.nature.com/articles/s41587-021-00946-z
Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex
本期文章：《自然—生物技术》：Online/在线发表
