Identifying a novel anticancer agent with microtubule-stabilizing effects through computational cell-based...

Area of Science:

• Medicinal Chemistry
• Chemical Biology
• Computational Drug Discovery

Background:

• Developing novel anticancer agents is crucial for effective cancer therapy.
• Computational phenotypic modeling offers a promising approach for identifying bioactive small molecules.
• Existing treatments face challenges such as drug resistance and side effects.

Purpose of the Study:

• To identify novel anticancer agents using computational phenotypic modeling.
• To evaluate the efficacy of identified compounds against various cancer cell lines.
• To elucidate the mechanism of action for promising drug candidates.

Main Methods:

• Utilized established computational anticancer cell-based models for high-throughput screening.
• Assessed compound G03's inhibitory effects on MCF-7, HepG2, MDA-MB-231, HCTT116, and HeLa cell lines.
• Validated tubulin as the target of G03 using Surface Plasmon Resonance (SPR), tubulin polymerization assays, immunofluorescence, and Western blot analyses.

Main Results:

• Identified 14 novel potential anticancer agents.
• Compound G03 demonstrated superior antiproliferative activity compared to sorafenib across tested cell lines, with low IC50 values.
• G03 was confirmed to target tubulin, exhibiting unique microtubule-stabilizing properties.

Conclusions:

• Computational phenotypic modeling is an effective strategy for discovering novel bioactive small molecules.
• Compound G03 represents a promising lead for anticancer drug development due to its potent efficacy and unique mechanism of action.
• The findings provide a feasible route toward innovative leads in chemical biology and medicinal chemistry for cancer treatment.