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Hepatocellular carcinoma (HCC) is the most common histological subtype of primary liver cancer and a leading cause of cancer-related mortality. Although immunotherapy combinations have expanded systemic treatment options for advanced HCC, sorafenib remains clinically relevant in select patient populations and provides a mechanistically informative model for treatment resistance. Sorafenib resistance arises from interrelated processes, including insufficient intratumoral drug exposure, hypoxia-driven escape signaling, ABC transporter-mediated drug efflux, epithelial-mesenchymal transition, MAPK and PI3K/AKT/mTOR pathway compensation, ferroptosis dysregulation, and immunosuppressive microenvironment remodeling. Based on these mechanisms, we propose a mechanism-driven nanomedicine framework that integrates sorafenib delivery with targeted resistance-axis intervention, rather than focusing only on drug solubility, circulatory stability, or tumor accumulation. Representative strategies include ligand-targeted nanocarriers, CXCR4-directed delivery systems, the synchronous co-delivery of sorafenib with pathway inhibitors or nucleic acid regulators, ferroptosis-modulating nanodrugs, and tumor microenvironment (TME)-responsive delivery systems. Among these, biomimetic membrane-modified smart responsive platforms are particularly noteworthy because they can convert HCC microenvironmental features, such as elevated glutathione (GSH), immunosuppressive tumor-associated macrophage (TAM) accumulation, and ferroptosis resistance, into triggers for drug release, dual targeting, and resistance regulation. Artificial intelligence and machine learning may further support resistance-pattern prediction, patient stratification, nanoplatform selection, and formulation optimization. Overall, sorafenib nanomedicine may integrate drug delivery optimization, resistance intervention, and patient stratification into a unified therapeutic framework with improved mechanistic specificity and translational potential for sorafenib-resistant HCC.
Huang et al. (Wed,) studied this question.