Back

Lipid nanoparticle co-delivery of mRNA and a small molecule drug for oral cancer chemoimmunotherapy

Padilla, M. S.; MITCHELL, M.; Le, A. D.; Weissman, D.; Alameh, M.-G.; Teerdhala, S. V.; Hymms, B. N.; Joseph, R. A.; Yamagata, H. M.; Patwari, K.; Zhang, Q.; Li, J. J.

2025-09-29 bioengineering
10.1101/2025.09.26.678832 bioRxiv
Show abstract

Oral squamous cell carcinoma (OSCC) represents 90% of all head and neck cancers. Despite decades of research, the 5-year survival rate is 50%, and strikingly, the overall incidence rate is projected to increase by 30% in the next ten years, which will result in a sharp increase in mortality. Two fundamental aspects of OSCC are that it progresses via the inactivation and mutation of tumor suppressor (TS) genes and has a "cold" tumor immune microenvironment (TIME). A major barrier in the treatment of OSCC is the lack of novel therapies clinicians have at their disposal that are designed to disrupt tumor progression by reshaping the cold tumors into inflammatory "hot" tumors. To overcome these obstacles, we employed a lipid nanoparticle (LNP) that co-encapsulates p53 mRNA and the small molecule ciclopirox (CPX). We demonstrate that both drugs have innate chemotherapeutic properties by facilitating caspase activation. Moreover, these therapies can create a less immunosuppressive TIME in part by repolarizing tumor-associated macrophages (TAMs) to M1-like phenotypes. When formulated together, our platform provides an all-in-one approach for OSCC, effective in both p53-therapy-susceptible and p53-therapy-resistant models. Additionally, this work provides a template for a delivery platform capable of tackling multiple mechanisms of OSCC progression and survival.

Published in Advanced Materials (predicted rank #15) · training set

Matching journals

The top 13 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.