Advances in applications of molecular dynamics simulation techniques to the research of self-assembled nano-drug delivery systems / 药学学报

Molecular dynamics simulation technology relies on Newtonian mechanics to simulate the motion of molecular system of the real system by computer simulation. It has been used in the research of self-assembly processes illustration and macroscopic performance prediction of self-assembly nano-drug delivery systems (NDDS) in recent years, which contributes to the facilitation and accurate design of preparations. In this review, the definitions, catalogues, and the modules of molecular dynamics simulation techniques are introduced, and the current status of their applications are summarized in the acquisition and analysis of microscale information, such as particle size, morphology, the formation of microdomains, and molecule distribution of the self-assembly NDDS and the prediction of their macroscale performances, including stability, drug loading capacity, drug release kinetics and transmembrane properties. Moreover, the existing applications of the molecular dynamic simulation technology in the formulation prediction of self-assembled NDDS were also summarized. It is expected that the new strategies will promote the prediction of NDDS formulation and lay a theoretical foundation for an appropriate approach in NDDS studies and a reference for the wider application of molecular dynamics simulation technology in pharmaceutics.

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.