Advances in approved nucleic acid drugs and lipid nanoparticle system / 药学学报

Nucleic acids, as a next generation of biotechnology drugs, not only can fundamentally treat diseases, but also own significant platform characteristics in view of technology and production. Therefore, nucleic acid-based drugs have broad clinical applications in biomedical fields. However, nucleic acids are degradable and unstable, and have very low intracellular delivery efficiency in vitro and in vivo, which greatly limits their applications. In recent years, ionizable lipid-based lipid nanoparticles have shown promising application potentials and have been successfully applied to COVID-19 (Coronavirus Disease 2019) vaccines in clinic. Lipid nanoparticles demonstrate high in vivo delivery efficiency and good safety profile due to their unique structural and physicochemical properties, which provides many possibilities for their clinical applications for nucleic acid delivery in the future. This review focused on the characteristics of nucleic acid drugs and their delivery barriers, and discussed the approved nucleic acid drugs to illustrate the key aspects of the success of their delivery carrier system. In addition, problems to be solved in the field were highlighted.

Research progress of ionizable lipid nanoparticles for siRNA delivery / 药学学报

Small interfering RNA (siRNA) is the initiator of RNA interference and inhibits gene expression by targeted degradation of specific messenger RNA. siRNA-mediated gene regulation has high efficiency and specificity and exhibits great significance in the treatment of diseases. However, the naked or unmodified siRNA has poor stability, easy to degrade by nuclease, short half-life, and low intracellular delivery. As an emerging non-viral nucleic acid delivery system, ionizable lipid nanoparticles play an important role in improving the druggability of siRNA. At present, one siRNA drug based on ionizable lipid nanoparticles has been approved for the treatment of rare disease. This review introduces the research progress in ionizable lipid nanoparticles for siRNA delivery, focusing on the effect of each component of lipid nanoparticles on the efficiency of siRNA-mediated gene silencing, which provides new references for the studies on ionizable lipid nanocarriers for siRNA delivery.

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R 2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.