Computational functional genomics-based approaches in analgesic drug discovery and repurposing. (24th May 2018)

• Record Type: Journal Article
• Authors: Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn
• Is Part Of: Pharmacogenomics. Volume 19:Number 9(2018)
• Page Start: 783
• Page End: 797
• Publication Date: 2018-05-24
• Subjects: computational methods -- data-driven research -- data science -- dbtORA R library -- drug discovery -- knowledge discovery -- machine learning; Pharmacogenomics -- Periodicals; 615.1
• DOI: 10.2217/pgs-2018-0036
• Languages: English
• ISSNs: 1462-2416

Abstract

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.