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Using Lipid Nanoparticles for the Delivery of Chemically Modified mRNA into Mammalian Cells
Published on: June 10, 2022
Optimized Lipid Nanoparticles for Co-Delivery of mRNA and siRNA Therapeutics in Refractory Liver Cancer
Yuqin Liao1,2,3, Xiaodong Zeng2,3, Xinwei Zhang2,3,4
1Department of Radiology, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
Abstract:
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 -CF3 aromatic tails, and molecular dynamics simulations confirm that these tails enhance RNA binding and nanoparticle stability. The resulting A2T5-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.
Insights
This study developed advanced lipid nanoparticles (LNPs) using machine learning to deliver RNA therapies. These targeted LNPs overcome resistance in liver cancer by restoring cell death pathways.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Oncology
Background:
- Hepatocellular carcinoma (HCC) shows poor outcomes and resistance to sorafenib, often linked to p53 loss and Nrf2 hyperactivation.
- Developing effective RNA therapeutics for resistant HCC remains a significant challenge.
Purpose of the Study:
- To engineer optimized lipid nanoparticles (LNPs) for combinatorial RNA delivery in sorafenib-resistant HCC.
- To leverage machine learning-assisted structure-activity relationship (SAR) analysis for rational LNP design.
Main Methods:
- Employed ML-assisted SAR analysis to design 120 degradable ionizable lipids for fluorinated aromatic LNPs.
- Utilized molecular dynamics simulations to assess RNA binding and nanoparticle stability.
- Functionalized LNPs with lactobionic acid for targeted HCC delivery of p53 mRNA and Nrf2 siRNA.
Main Results:
- Identified -CF3 aromatic tails as crucial for enhanced RNA binding and LNP stability via ML analysis.
- Developed A2T5-s LNPs capable of co-delivering p53 mRNA and Nrf2 siRNA.
- Demonstrated significant inhibition of sorafenib-resistant HCC tumors in vivo by restoring ferroptosis and inducing apoptosis through SLC7A11 suppression.
Conclusions:
- An ML-assisted LNP optimization strategy enables precision RNA therapeutics.
- This approach effectively overcomes resistance mechanisms in refractory liver cancer.
- The developed targeted LNPs show promise for treating advanced HCC.
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