Lipid-like nanoparticles (LNPs) have potential as non-viral delivery systems for mRNA therapies. However, repeated administrations of LNPs may lead to accumulation of delivery materials and associated toxicity. To address this challenge, we have developed biodegradable lipids which improve LNPs clearance and reduce toxicity. We modify the backbone structure of Dlin-MC3-DMA by introducing alkyne and ester groups into the lipid tails. We evaluate the performance of these lipids when co-formulated with other amine containing lipid-like materials. We demonstrate that these formulations synergistically facilitate robust mRNA delivery with improved tolerability after single and repeated administrations. We further identify albumin-associated macropinocytosis and endocytosis as an ApoE-independent LNP cellular uptake pathway in the liver. Separately, the inclusion of alkyne lipids significantly increases membrane fusion to enhance mRNA release, leading to synergistic improvement of mRNA delivery. We believe that the rational design of LNPs with multiple amine-lipids increases the material space for mRNA delivery.

Significance: Hydrogels maintain great potential for biomedical applications. However, predicting whether a chemical can form a hydrogel simply based on its chemical structure remains challenging. In this study, we developed a combinational approach to obtain a structurally diverse hydrogel library with over 2,000 peptides as a training dataset for machine learning. We calculated their chemical features, including topological and physicochemical properties, and utilized machine learning methods to predict the self-assembly behavior.