Tari Luis B, Patel Jagruti H
Knowledge Discovery Lab, Software Science and Analytics, GE Global Research, 1 Research Circle, Niskayuna, NY, 12309, USA,
Methods Mol Biol. 2014;1159:253-67. doi: 10.1007/978-1-4939-0709-0_14.
Drug development remains a time-consuming and highly expensive process with high attrition rates at each stage. Given the safety hurdles drugs must pass due to increased regulatory scrutiny, it is essential for pharmaceutical companies to maximize their return on investment by effectively extending drug life cycles. There have been many effective techniques, such as phenotypic screening and compound profiling, which identify new indications for existing drugs, often referred to as drug repurposing or drug repositioning. This chapter explores the use of text mining leveraging several publicly available knowledge resources and mechanism of action representations to link existing drugs to new diseases from biomedical abstracts in an attempt to generate biologically meaningful alternative drug indications.
药物研发仍然是一个耗时且成本高昂的过程,每个阶段的淘汰率都很高。鉴于监管审查的加强,药物必须跨越安全障碍,制药公司通过有效延长药物生命周期来最大化投资回报率至关重要。已经有许多有效技术,如表型筛选和化合物剖析,可识别现有药物的新适应症,通常称为药物再利用或药物重新定位。本章探讨利用几种公开可用的知识资源和作用机制表示进行文本挖掘,以便从生物医学摘要中将现有药物与新疾病联系起来,从而尝试生成具有生物学意义的替代药物适应症。