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OTTM: an automated classification tool for translational drug discovery from omics data.

Authors :
Yang, Xiaobo
Zhang, Bei
Wang, Siqi
Lu, Ye
Chen, Kaixian
Luo, Cheng
Sun, Aihua
Zhang, Hao
Source :
Briefings in Bioinformatics; Sep2023, Vol. 24 Issue 5, p1-12, 12p
Publication Year :
2023

Abstract

Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named O mics and T ext driven T ranslational M edicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs—tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)—showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
24
Issue :
5
Database :
Complementary Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
172331665
Full Text :
https://doi.org/10.1093/bib/bbad301