Cardiovascular disease, the leading cause of mortality worldwide, is primarily caused by atherosclerosis, which is characterized by lipid and inflammatory cell accumulation in blood vessels and carotid intima thickening, among others. Although disease management has improved significantly, new therapeutic strategies focused on accelerating atherosclerosis regression must be developed. Atherosclerosis models mimicking in vivolike conditions provide essential information for research and new advances towards clinical application. Here a therapeutic strategy to improve cholesterol efflux has been developed based on a twostep administration of rHDL consisting on a first dose of antagomiR33a loaded rHDLs to induce ABCA1 transporter overexpression, followed by a second dose of DPPC rHDLs, which remove efficiently cholesterol from foam cells. A triplecell 2D atheroma plaque model reflecting the cellular complexity of atherosclerosis has been used to overcome the translational gap providing a suitable model to improve the efficiency of the nanoparticles in promoting cholesterol efflux. The results show that sequential administration of rHDL potentiates cholesterol efflux indicating that this approach might be used in vivo to target more efficiently atherosclerotic lesions and improve prognosis of the disease. Sequential targeting of foam cells with nanoparticles including a first antagomiR33a delivery by DPPC:CE:LPC rHDL followed by a second infusion of DPPC rHDL induces a potent cholesterol efflux from foam cells, which may overcome current technique barriers to promote clinical applications.
The usefulness of 2D cell culture models as an experimental approach to bioengineer atheroma models is endorsed by the possibility of dissecting separately the events occurring in the process to better understand the observed effects. For example, allows independent isolation of each cellular compartment for downstream analysis without cell sorting.
In any case, future in vivo studies in animal models will provide the necessary information to calculate the doses, the time interval between infusions and determine the antiatherogenic effects of the treatment strategy proposed in this work.
For more information check our paper: DOI: 10.1002/smll.202105915
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Have you considered conducting computational Complex Network analysis and Artificial Intelligence / Machine Learning (AI/ML) algorithms studies on this area, which ones could be the advantages?
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We did not considered to conduct any computational algorithm in this work specifically, but we are in the process of developing an algorithm related to atherosclerosis,
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