Engineering Lipid–Polymer Nanoparticles for siRNA Delivery to Cancer Cells - Arthur Manda; Abdulelah Alhazza; Hasan Uludağ; Hamidreza Montazeri Aliabadi (Pharmaceuticals)
Background: RNA interference (RNAi) is a powerful tool that can target many proteins without the expensive and time-consuming drug development studies. However, due to the challenges in delivering RNA molecules, the potential impact of RNAi approaches is yet to be fully realized in clinical settings. Lipid nanoparticles (LNPs) have been the most successful delivery system for nucleic acids, but targeted delivery to a solid tumor still eludes the developed LNPs. We hypothesized that specially designed low-molecular-weight PEIs can partially or completely replace the ionizable lipids for more accommodating vehicles due to the structural flexibility offered by polymers, which could lead to safer and more efficient nucleic acid delivery. Methods: To achieve this, we first optimized the LNP formulations as a point of reference for three outcomes: cellular uptake, cytotoxicity, and silencing efficiency. Using a response surface methodology (Design Expert), we optimized siRNA delivery by varying mole fractions of lipid components. Leveraging the optimal LNP formulation, we integrated specifically designed cationic polymers as partial or complete replacements for the ionizable lipid. This methodological approach, incorporating optimal combined designs and response surface methodologies, refined the LPNPs to an optimal efficiency. Results: Our data revealed that DOPE and Dlin-MC3-DMA contributed to higher efficiency in selected breast cancer cells over DSPC and ALC-0315 as neutral and ionizable lipids, respectively, based on the software analysis and direct comparative experiments. Incorporation of selected polymers enhanced the cellular internalization significantly, which in some formulations resulted in higher efficiency. Conclusions: These findings offer a framework for the rational design of LPNPs, that could enhance the passive targeting and silencing efficiency in cancer treatment and broader applications for RNAi-based strategies.