Hanlu Gao

TL;DR: A comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling is provided in this article.

TL;DR: An combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations, which will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.

TL;DR: The self-assembled nanomedicine based on Pt(IV)-ADD could be a promising strategy for TNBC therapy and showed great potential in improving the sensitivity of cisplatin to TNBC with up to 266-fold lower IC50 value and significantly enhanced in vivo tumor growth inhibition.

TL;DR: To validate the prediction modeling, berberine (BBR) was used as the model drug to form the complex with phospholipid, and molecular dynamics simulation was used to investigate the molecular mechanism for self-aggregation of BBR in solution and BBR-phospholIPid complex complexation.

TL;DR: In this paper, a machine learning predictive model for LNP-based mRNA vaccines was developed, validated by experiments, and further integrated with molecular modeling, which can be used for virtual screening of LNP formulations in the future.