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新药开发一直面临着高投入低产出的困境.药物重定位(drug repositioning)是解决这一问题的有效方法,因而成为现今药物研究的热点之一.目前流行的方法主要是基于药物小分子的相似性或关联性(化学结构、副作用、表达谱和小分子-蛋白作用关系等),预测药物的新功能.由于仅仅依赖药物-药物之间的关系,这些新功能限制在现有药物的功能空间之内.本研究中提出一种以疾病为导向,基于pathway谱的药物-疾病关联性评估方法来预测药物的新功能.考虑到药物在临床应用中除了作用于预先设定的主要治疗靶点以外,还能作用于其他一些额外的靶点,而这往往是产生副作用或是新的治疗功能的原因.该方法充分利用药物的已有靶点信息,认为药物通过作用于多条疾病相关蛋白影响的pathway,达到调节疾病的病理过程的目的.以2010年全球最热销的8个药做测试,美国食品药品监督管理局(Food and Drug Administration,FDA)批准的主治功能排在前10的位置,超过60%的预测功能可以得到文献的直接支持.它可以作为以往方法的有效补充,为药物的重定位以及毒副作用评估提供一种新的视角.
Drug development has always been faced with the dilemma of high input and low output.Drug repositioning is an effective way to solve this problem and has become one of the hot topics in the current drug research.At present, the prevailing methods are mainly based on small drug molecules Predict the new function of drugs due to their similarity or relevance (chemical structure, side effects, expression profiles and small molecule-protein interactions, etc.) Because of relying only on drug-drug interactions, these new functions are limited to the function of existing drugs Space.In this study, a disease-oriented, pathway-based drug-disease association assessment method was proposed to predict the new function of a drug.According to the fact that in addition to its role in pre-setting the primary therapeutic target Point, but also on some other additional targets, which are often the cause of side effects or new therapeutic functions.This method takes full advantage of existing target drug information, that the drug through the role of a number of disease-related Protein pathway, to achieve the purpose of regulating the pathological process of disease.With 2010 the world’s best-selling eight drugs to do the test, the US Food and Drug Administration Authorities approved by the Food and Drug Administration (FDA) rank top 10 and over 60% of predictive functions are directly supported by the literature, which can serve as an effective complement to previous approaches to drug relocation and toxin Side effects assessment provides a new perspective.