In-silico guided discovery of novel CCR9 antagonists

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Immunology

Background:

• Chemokine CC receptor 9 (CCR9) antagonism is a potential therapeutic strategy for inflammatory bowel diseases (IBD), including ulcerative colitis and Crohn's disease.
• Limited experimental data on CCR9 and its ligands hinders the identification of effective small molecule antagonists.
• Developing novel antagonists is crucial for advancing IBD treatments.

Purpose of the Study:

• To discover novel CCR9 antagonists using a virtual screening and hit-to-lead campaign.
• To apply a data fusion strategy integrating multiple computational tools for enhanced hit identification.
• To identify and incorporate privileged chemokine fragments into the drug discovery process.

Main Methods:

• Utilized a novel data fusion strategy combining 2D similarity, shape similarity, pharmacophore searching, and molecular docking.
• Integrated privileged chemokine fragments to guide the identification of potential antagonists.
• Employed consensus and parallel selection ranking strategies to balance enrichment and novelty of hits.

Main Results:

• Successfully identified 198 virtual screening hits for CCR9 antagonists.
• Achieved an overall virtual screening hit rate of 18%.
• Several identified hits were advanced to early lead compounds through synthesis and analog acquisition.

Conclusions:

• The study successfully demonstrated a novel computational approach for identifying CCR9 antagonists.
• The discovered compounds represent promising starting points for developing new treatments for inflammatory bowel diseases.
• This work highlights the potential of integrated computational strategies in accelerating drug discovery for chemokine targets.