Advanced materials (Deerfield Beach, Fla.)Jan 9, 2026

Improved Lipid Nanoparticles for Delivering mRNA and siRNA Treatments in Hard-to-Treat Liver Cancer

mRNA Technology Weekly Brief ↗PubMed ↗DOI ↗

Updated Jun 27, 2026

Abstract

Essence

Machine-learning-guided lipid nanoparticles may improve combined p53 mRNA and Nrf2 siRNA delivery for sorafenib-resistant hepatocellular carcinoma models.

Evidence

This preclinical formulation and platform experiment engineered 120 degradable ionizable lipids, used ML-assisted SAR analysis and molecular dynamics simulations, and tested lactobionic acid-functionalized AT-s LNPs for RNA co-delivery and tumor inhibition.

Caveat

The abstract presents preclinical optimization and tumor-model inhibition, not human efficacy or safety data.

Simplified

Hepatocellular carcinoma (HCC) exhibits poor prognosis and rapid resistance to sorafenib, particularly involving p53 loss and Nrf2 hyperactivation. Here, we employ machine learning (ML)-assisted structure-activity relationship (SAR) analysis to guide the engineering of a library of 120 degradable ionizable lipids, enabling the rational design of fluorinated aromatic lipid nanoparticles (LNPs) optimized for combinatorial RNA delivery. ML-based feature-importance analysis prioritizes -CFaromatic tails, and molecular dynamics simulations confirm that these tails enhance RNA binding and nanoparticle stability. The resulting AT-s LNPs, functionalized with lactobionic acid for selective HCC targeting, enable efficient co-delivery of p53 mRNA and Nrf2 siRNA. This strategy restores ferroptosis and induces apoptosis in sorafenib-resistant HCC by suppressing SLC7A11, leading to marked tumor inhibition. Our study demonstrates an ML-assisted LNP optimization strategy, advancing precision RNA therapeutics to overcome resistance in refractory liver cancer. 3 2 5