ScholarWorks@중앙대학교

초록

An integrated three-step artificial-intelligence (AI) workflow was developed to accelerate the design of a CXCR4-targeted, dual-drug nanocarrier for colorectal cancer therapy. The workflow combined AI-based drug synergy prediction, peptide ligand discovery, and formulation optimization to create a core-shell nanocarrier consisting of a mesoporous silica core coated with a liposomal shell, co-delivering berberine (BBR) and paclitaxel (PTX). The nanocarrier exhibited efficient drug loading, sustained release, and selective uptake by CXCR4-positive cancer cells. In vitro, it synergistically inhibited cancer proliferation and migration, while in vivo it produced pronounced tumor regression and reversal of tumor-associated splenomegaly without systemic toxicity. These findings demonstrate that AI-guided synergy scouting and modular nanocarrier engineering can yield a receptor-targeted combination therapy with translational potential for next-generation cancer treatment. © 2025. The Author(s).

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