Fanglin Gong

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.

Abstract The advancement of message RNA (mRNA) ‐based immunotherapies for cancer is highly dependent on the effective delivery of RNA (Ribonucleic) payloads using ionizable lipid nanoparticles (LNPs). However, the clinical application of these therapies is hindered by variable mRNA expression among different cancer types and the risk of systemic toxicity. The transient expression profile of mRNA further complicates this issue, necessitating frequent dosing and thus increasing the potential for adverse effects. Addressing these challenges, a high‐throughput combinatorial method is utilized to synthesize and screen LNPs that efficiently deliver circular RNA (circRNA) to lung tumors. The lead LNP, H1L1A1B3, demonstrates a fourfold increase in circRNA transfection efficiency in lung cancer cells over ALC‐0315, the industry‐standard LNPs, while providing potent immune activation. A single intratumoral injection of H1L1A1B3 LNPs, loaded with circRNA encoding interleukin‐12 (IL‐12), induces a robust immune response in a Lewis lung carcinoma model, leading to marked tumor regression. Immunological profiling of treated tumors reveals substantial increments in CD45 + leukocytes and enhances infiltration of CD8 + T cells, underscoring the ability of H1L1A1B3 LNPs to modulate the tumor microenvironment favorably. These results highlight the potential of tailored LNP platforms to advance RNA drug delivery for cancer therapy, broadening the prospects for RNA immunotherapeutics.

Cholesterol crystals (CCs), originally accumulating in the lysosome of cholesterol-laden cells, can aggravate the progression of atherosclerosis. β-cyclodextrin (CD) is a potent cholesterol acceptor or CC solubilizer. However, the random extraction of cholesterol impedes the in vivo application of CD for removing lysosomal CCs. Here, we exploit poly-β-cyclodextrin (pCD) as a lysosomal CC solubilizer and dextran sulfate grafted with benzimidazole (BM) as a pH-sensitive switch (pBM) to self-assemble into a supramolecular nanoassembly (pCD/pBM-SNA). The CD cavity in pCD/pBM-SNA can be efficiently sealed by hydrophobic BM at pH 7.4 (OFF). After it enters the lysosome, pCD/pBM-SNA disassembles, recovers the CD cavity to dissolve CCs into free cholesterol due to the protonation of BM (ON), and reduces CCs, finally enhancing the cholesterol efflux and promoting atherosclerosis regression. Our findings provide an “OFF–ON” tactic to remove lysosomal CCs for antiatherosclerosis as well as other diseases such as Niemann–Pick type C diseases with excessive cholesterol accumulation in the lysosome.

The ionizable lipid component of lipid nanoparticle (LNP) formulations is essential for mRNA delivery by facilitating endosomal escape. Conventionally, these lipids are synthesized through complex, multistep chemical processes that are both time-consuming and require significant engineering. Furthermore, the development of new ionizable lipids is hindered by a limited understanding of the structure-activity relationships essential for effective mRNA delivery. In this work, we have developed a modular platform utilizing the Passerini reaction to rapidly generate large, chemically diverse libraries of biodegradable ionizable lipids. This high-throughput approach enables the systematic exploration of various lipid components-head groups, tails, and spacers-and their impacts on mRNA delivery efficiency. By investigating the hydrogen bonding potential between the lipid's head groups and the mRNA's ribose phosphate complex, we found that optimizing the methylene units between the lipid's head groups and linkages could enhance endosomal escape and, consequently, mRNA delivery efficiencies. Leveraging this insight, our platform has led to the identification of the biodegradable ionizable lipid A4B4-S3, which outperforms the current clinical benchmark, SM-102, in gene editing efficacy in mouse liver following systemic administration and demonstrates the promise for repeat-dose protein replacement treatments. This work not only offers a rapid, scalable method for ionizable lipid synthesis but also deepens our understanding of their structure-activity relationships, paving the way for more effective mRNA therapeutics.