Back

Active Liposomal Targeting for Head and Neck Squamous Cell Carcinoma Treatment

del Campo-Balguerias, A.; Martin-Hernandez, C.; Herrero-Igartua, C.; de Andres, F.; Gueddari, A.; Garcia-Bravo, M.; Ocana, A.; Bravo, I.; Alonso-Moreno, C.; Lorz, C.

2025-12-05 cancer biology
10.64898/2025.12.03.691158 bioRxiv
Show abstract

Head and neck squamous cell carcinoma (HNSCC) is a major oncological challenge due to its high recurrence rates, biological heterogeneity, and poor long-term survival outcomes. Despite advances in surgery and radiotherapy, chemotherapeutic options are limited. Cisplatin (CDDP) remains the mainstay, while small molecule inhibitors, such as the selective PI3K inhibitor Alpelisib, are still under clinical evaluation. However, systemic side effects and suboptimal tumor specificity hamper the clinical utility of both standard and novel specific therapies. In this study, we developed passive- and active-targeting liposomes encapsulating Cisplatin (CDDP) and Alpelisib. To achieve active targeting, the anti-epidermal growth factor receptor (EGFR) antibody Cetuximab was conjugated to the liposome surface. All formulations were characterized based on size, morphology, encapsulation efficiency, and drug release profiles. In vitro assays demonstrated that liposomal encapsulation preserved the cytotoxic effects of both CDDP and Alpelisib in HNSCC cell lines, with CDDP showing adequate encapsulation efficiency for in vivo evaluation. In vivo near-infrared fluorescence imaging demonstrated that active-targeting liposomes achieved greater tumor accumulation and a better tumor-to-spleen ratio compared to passive formulations. Tumor growth by CDDP-loaded activetargeting liposomes was comparable to that of the free drug, likely due to the hydrophilic nature and rapid release of CDDP from liposomes in physiological conditions. Importantly, blood chemistry analysis indicated a favorable toxicity profile for the targeted liposomal formulation. These findings highlight the potential of liposome-based targeted strategies to improve therapeutic outcomes in HNSCC and underscore the value of nanotechnology in the development of next-generation oncological therapies.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.