基于系统生物学方法和定量构效关系的药物副作用研究

Drug safety is of great importance to public health. It is crucial to identify adverse effects of drugs before testing in human. For a novel chemical entry, some of adverse events could be predicted from the known mechanism of a drug. However, the situation might be complicated by 'off-target' pathways. Methods of system biology provide novel sight in drug design, in which both the efficacy and side-effects of a drug can be concluded from knowledge of binding partners and affected biological pathways. However, this method represents molecules as nodes, which would lead to the deletion of molecular information. In this project, methods for system biology and quantitativy structure-activity relationship(QSAR) will be combined for predicting adverse drug reaction.Adverse events and pathways would be associated firstly and then related targets determined through analysis of both protein-protein interaction network and gene expression. In the next step, interactions between drugs and these targets will be investegated. This would facilitate designing high thoughput screening algorithm and quantitative index for correlation between a drug and a target.Finally, these methods will be combined with complex network to give final prediction of adverse events of a drug. Webserver will be constructed for online prediction. In this project, the strategy of investigating adverse drug reactions on both systematic and molecular specificity aspects is promising to give accurate estimation on drug safety, which is anticipated useful for drug design.

药物的安全问题关系到公众健康。副作用是导致药物研发失败的一个重要因素，因此对药物副作用的研究在药物设计过程中至关重要。尽管有些副作用可以通过药物作用机制进行预测，但是对于"脱靶"导致的副作用存在不可预知性。系统生物学方法被用于系统层面的药效/副作用的研究。然而该类方法通常将蛋白质和药物分子抽象成节点，忽略了分子结构的特异性。因此，本项目拟将系统生物学方法与定量构效关系模型结合对药物副作用进行研究。基于蛋白质网络的拓扑分析和相关基因的表达分析筛选副作用相关蛋白质；研究蛋白质与药物分子相互作用机制，开发基于QSAR建模的高通量筛选算法和提出两者相关性的量化指标；尝试将QSAR方法与复杂网络算法结合构建药物副作用预测模型，搭建在线预测平台。本项目从系统宏观和分子特异性两个层次研究药物副作用的思路能为药物安全评估提供有效地方法，有助于加快药物研发进程，降低研发成本。

药物的安全问题关系到公众健康。副作用是导致药物研发失败的一个重要因素，因此对药物副作用的研究在药物设计过程中至关重要。尽管有些副作用可以通过药物作用机制进行预测，但是对于"脱靶"导致的副作用存在不可预知性。系统生物学方法被用于系统层面的药效/副作用的研究。然而该类方法通常将蛋白质和药物分子抽象成节点，忽略了分子结构的特异性。因此，本项目拟将系统生物学方法与定量构效关系模型结合对药物副作用进行研究。基于蛋白质网络的拓扑分析和相关基因的表达分析筛选副作用相关蛋白质，研究蛋白质与药物分子相互作用机制，开发基于QSAR建模的高通量筛选算法和提出两者相关性的量化指标，尝试将QSAR方法与复杂网络算法结合构建药物副作用预测模型，搭建在线预测平台。基于复杂网络分析方法和算法改进，本项目在药物不良反应的预测上取得了三种新的预测方法。基于复杂网络数据和基因组以及转录组数据的结合，本项目还开展了基因组信息与疾病关联的研究，如：人类乳头瘤病毒致病机理相关研究，二型糖尿病风险位点的注释以及风险模型构建，识别和描述原发性肝细胞癌中的环状RNA及其相关联的基因和基于网络基因重要性打分的方法来研究癌症相关生物标记物等工作。