School of Informatics, Indiana University, Indianapolis, USA.
BMC Genomics. 2012;13 Suppl 6(Suppl 6):S17. doi: 10.1186/1471-2164-13-S6-S17. Epub 2012 Oct 26.
Network pharmacology has emerged as a new topic of study in recent years. It aims to study the myriad relationships among proteins, drugs, and disease phenotypes. The concept of molecular connectivity maps has been proposed to establish comprehensive knowledge links between molecules of interest in a given biological context. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic profiles and toxicological profiles of candidate drugs.
To assess drug pharmacological effect, we assume that "ideal" drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (C²Maps) platform. An interactive interface for directionality annotation of drug-protein pairs with literature evidences from PubMed has been added to the new version of C²Maps. We also upload the curated directionality information of drug-protein pairs specific for three complex diseases - breast cancer, colorectal cancer and Alzheimer disease.
For relevant drug-protein pairs with directionality information, we use breast cancer as a case study to demonstrate the functionality of disease-specific searching. Based on the results obtained from searching, we perform pharmacological effect evaluation for two important breast cancer drugs on treating patients diagnosed with different breast cancer subtypes. The evaluation is performed on a well-studied breast cancer gene expression microarray dataset to portray how useful the updated C²Maps is in assessing drug efficacy and toxicity information.
The C²Maps platform is an online bioinformatics resource that provides biologists with directional relationships between drugs and genes/proteins in specific disease contexts based on network mining, literature mining, and drug effect annotating. A new insight to assess overall drug efficacy and toxicity can be provided by using the C²Maps platform to identify disease relevant proteins and drugs. The case study on breast cancer correlates very well with the existing pharmacology of the two breast cancer drugs and highlights the significance of C²Maps database.
网络药理学近年来成为一个新兴的研究课题。它旨在研究蛋白质、药物和疾病表型之间的无数关系。已经提出了分子连接图的概念,以在给定的生物背景下建立感兴趣分子之间的综合知识联系。在特定疾病背景下,药物和基因/蛋白质之间的分子连接图可能特别有价值,因为通过这些图谱的功能方法,研究人员可以从全局角度了解候选药物的治疗和毒理学特征。
为了评估药物的药理作用,我们假设“理想”药物可以通过调节患者的基因表达谱,使其与健康人群的水平相似,从而治疗或预防疾病。基于这一假设,我们基于一个计算连接图(C²Maps)平台,构建了具有药物-蛋白质方向性(抑制/激活)信息的全面疾病-基因-药物连接关系。新的 C²Maps 版本增加了一个带有 PubMed 文献证据的药物-蛋白质对方向性注释的交互式界面。我们还上传了针对三种复杂疾病(乳腺癌、结直肠癌和阿尔茨海默病)的特定药物-蛋白质对的已整理方向性信息。
对于具有方向性信息的相关药物-蛋白质对,我们以乳腺癌为例,演示了特定疾病搜索的功能。根据搜索结果,我们对两种重要的乳腺癌药物治疗不同乳腺癌亚型患者的疗效进行了药理作用评价。该评价是在一个经过充分研究的乳腺癌基因表达微阵列数据集上进行的,以描绘更新后的 C²Maps 在评估药物疗效和毒性信息方面的有效性。
C²Maps 平台是一个在线生物信息学资源,它为生物学家提供了特定疾病背景下药物与基因/蛋白质之间的方向性关系,基于网络挖掘、文献挖掘和药物作用注释。使用 C²Maps 平台识别与疾病相关的蛋白质和药物,可以提供一种评估药物整体疗效和毒性的新方法。乳腺癌的案例研究与两种乳腺癌药物的现有药理学非常吻合,突出了 C²Maps 数据库的重要性。