Integrating Machine Learning and Structure-Guided Discovery of a Novel Type II SYK Inhibitor for Treating...

Area of Science:

• Oncology
• Molecular Biology
• Drug Discovery

Background:

• Spleen tyrosine kinase (SYK) plays a crucial role in immune signaling and DNA repair.
• Aberrant SYK activation is linked to therapeutic resistance in triple-negative breast cancer (TNBC).

Purpose of the Study:

• To develop a potent SYK inhibitor for TNBC treatment.
• To investigate the therapeutic potential of SYK inhibition in overcoming treatment resistance.

Main Methods:

• Utilized a machine learning-corrected virtual screening strategy to identify a benzimidazole lead.
• Performed structure-activity relationship (SAR)-guided optimization to yield the inhibitor 5i.
• Evaluated 5i's effects on TNBC cells and in a xenograft model, and elucidated its mechanism of action.

Main Results:

• Identified and optimized a potent type II SYK inhibitor, 5i (IC50 = 16 nM), stabilizing the inactive DFG-out conformation.
• 5i demonstrated significant antiproliferative, pro-apoptotic, and antimigratory effects in TNBC cells.
• 5i suppressed tumor growth in vivo and showed synergy with Olaparib by increasing DNA damage via inhibition of CtIP phosphorylation.

Conclusions:

• 5i is a promising therapeutic candidate for TNBC, demonstrating potent anti-cancer effects and overcoming resistance mechanisms.
• SYK inhibition represents a viable strategy to combat homologous recombination-mediated resistance in TNBC.