Identification of Hit Compounds Using Artificial Intelligence for the Management of Allergic Diseases

Area of Science:

• Pharmacology
• Immunology
• Drug Discovery

Background:

• Allergic rhinitis (AR) is a prevalent condition often managed with therapies that have limitations.
• Suppressor of Cytokine Signaling 3 (SOCS3) plays a role in inflammatory pathways relevant to AR.
• Targeting the kinase inhibitory region of SOCS3 presents a potential therapeutic strategy.

Purpose of the Study:

• To identify and evaluate drug candidates targeting SOCS3 for AR treatment.
• To investigate the efficacy of novel compounds in preclinical models of AR.
• To explore the mechanism of action of identified drug candidates.

Main Methods:

• Artificial intelligence (AI)-based virtual screening to identify SOCS3 inhibitors.
• Luminescence assays to assess JAK-2 activity restoration.
• Cell-based assays (Jurkat T, BEAS-2B) to evaluate SOCS3, STAT3 expression, and phosphorylation.
• In vivo studies using an OVA-induced AR mouse model measuring IgE, cytokines, and eosinophils.

Main Results:

• AI screening identified 20 hit compounds with strong binding affinities.
• Compounds 5 and 8 effectively restored JAK-2 activity without toxicity.
• These compounds reduced SOCS3 expression and normalized STAT3 phosphorylation in cell lines.
• In vivo, compounds 5 and 8 alleviated AR symptoms and reduced key immune markers.

Conclusions:

• Novel drug candidates targeting SOCS3 demonstrate promising nonclinical efficacy for AR.
• AI-driven drug discovery platform successfully identified potent therapeutic agents.
• Targeting SOCS3 offers an innovative strategy for managing allergic rhinitis.