Computational functional genomics-based approaches in analgesic drug discovery and repurposing

Area of Science:

• Genomics
• Pharmacology
• Bioinformatics

Background:

• Persistent pain affects 20% of adults globally, with limited effective treatments.
• Functional genomics integrates diverse biological data (DNA, gene expression, protein function) for phenotype-genotype insights.
• Current analgesic drug discovery uses gene function databases, next-generation sequencing, and proteomics.

Purpose of the Study:

• To review functional genomics approaches for analgesic drug discovery and repurposing.
• To highlight the potential of computational functional genomics in pain management.
• To demonstrate a workflow for computational functional genomics using the 'dbtORA' R library.

Main Methods:

• Literature review of functional genomics in analgesic research.
• Exploration of computational methods for analyzing genomic data.
• Application and demonstration of the 'dbtORA' R library for workflow analysis.

Main Results:

• Functional genomics is a promising area for identifying novel analgesic targets.
• Computational approaches enhance the efficiency of drug discovery and repurposing.
• The 'dbtORA' library provides a practical tool for implementing these computational workflows.

Conclusions:

• Computational functional genomics holds significant potential for advancing analgesic drug development.
• Integrating diverse 'omics' data with advanced analytics is key to discovering new pain therapies.
• The presented workflow offers a framework for leveraging 'dbtORA' in future research.