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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.
Nature Reviews Rheumatology ( IF 33.7 ) Pub Date : 2019-12-12 , DOI: 10.1038/s41584-019-0337-0
Kathryn M Kingsmore 1 , Amrie C Grammer 1 , Peter E Lipsky 1
Affiliation  

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

中文翻译:

药物用于改善风湿性自身免疫性炎性疾病的治疗。

上个世纪的特点是,学术界和制药业都在大力努力,向风湿性自身免疫性炎性疾病(RAID)的患者引入新的治疗方法,通常是借用已经在一个RAID中使用或以前在整个RAID中使用的“借用”治疗方法。不同的疾病,一个称为药物治疗的概念。然而,尽管有一些临床表现和免疫失调,但RAID之间的疾病发病机理和表型差异很大,并且对病因的有限了解使得重新选择RAID药物成为一项挑战。尽管如此,上个世纪的特点是重新定位的“浪潮”不同。早期药物重用发生在学术界,并且是基于偶然的观察结果或发现的疾病相似性,通常是由药品类别的可用性和受欢迎程度驱动的。自1990年代以来,大多数生物疗法已针对一种或几种RAID进行了开发,然后在其他RAID中进行了测试,取得了不同程度的成功。在过去的二十年中,数据驱动的重新定位以基于签名的方法为特征,这些方法依赖于分子生物学和基因组学。此外,许多数据驱动策略采用计算建模和机器学习来集成多个数据源。这些重用期共同导致了许多RAID的处理进展。在过去的二十年中,数据驱动的重新定位以基于签名的方法为特征,这些方法依赖于分子生物学和基因组学。此外,许多数据驱动策略采用计算建模和机器学习来集成多个数据源。这些重用期共同导致了许多RAID的处理进展。在过去的二十年中,数据驱动的重新定位以基于签名的方法为特征,这些方法依赖于分子生物学和基因组学。此外,许多数据驱动策略采用计算建模和机器学习来集成多个数据源。这些重用期共同导致了许多RAID的处理进展。
更新日期:2019-12-13
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