A multiscale modeling study of nanoparticle-based targeting therapy against atherosclerosis

Although researches on nanoparticle-based (NP-based) drug delivery system for atherosclerosis treatment have grown rapidly in recent years, there are limited studies in quantifying the effects of targeting drugs on plaque components and microenvironment. The purpose of the present study was to quantitatively assess the targeting therapeutic effects against atherosclerosis by establishing a multiscale mathematical model. The multiscale model involved subcellular, cellular and microenvironmental scales to simulate lipid catabolism, macrophage behaviors and dynamics of microenvironmental components, respectively. In vitro and in vivo experimental data were integrated into the mathematical model according to Bayesian statistics, in order to evaluate the therapeutic effects of a proposed NP-based platform for macrophage-specific delivery to simultaneously deliver SR-A siRNA (to reduce LDL uptake) and LXR-L (to stimulate cholesterol efflux). Dosage variation analysis was then performed to investigate the drug efficacy under varied dosage combinations of SR-A siRNA and LXR-L. The simulation results demonstrated that the dynamics of the microenvironmental components presented different developments in Untreated and Treated groups. We also found that the balance of lipid metabolism between uptake and efflux resulted in the improvement of lipid and inflammatory microenvironment, consequently in the plaque regression. In addition, the model predicted optimized dosage combinations according to the co-effect analysis of the two drugs on the lipid microenvironment. This study suggests that multiscale modeling can be a powerful quantitative tool for estimating the therapeutic effects of targeting drugs for plaque regression and designing the enhanced treatment strategies against atherosclerosis.