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

Area of Science:

• Biomedical Informatics
• Translational Medicine
• Drug Discovery

Background:

• Omics data generates numerous potential targets, overwhelming traditional validation methods.
• Many identified genes/proteins lack existing drugs or therapeutic relevance.
• Efficiently prioritizing targets from omics data is crucial for drug development.

Purpose of the Study:

• To develop and validate a novel classification tool, Omics and Text driven Translational Medicine (OTTM), for prioritizing drug targets from omics data.
• To accelerate the identification of druggable targets and existing compounds for therapeutic intervention.
• To streamline the translation of omics findings into clinical applications.

Main Methods:

• Developed OTTM, a classification tool integrating omics data with drug availability and literature mining.
• Applied OTTM to a dataset of 4489 candidate proteins from a proteomics study.
• Evaluated drug candidates on hepatocellular carcinoma Hep-G2 cells.

Main Results:

• OTTM recommended 40 FDA-approved or clinical trial drugs from 4489 candidate proteins.
• Fifteen commercially available drugs were tested, with two showing potent inhibitory activity.
• Tafenoquine succinate and branaplam demonstrated significant efficacy against Hep-G2 cells, identifying CYC1 and SMN1 as potential targets.

Conclusions:

• OTTM effectively narrows down candidate proteins for drug discovery and target validation.
• The study identified tafenoquine succinate and branaplam as promising agents for hepatocellular carcinoma.
• OTTM facilitates the acceleration of drug discovery and target identification from large-scale omics data.