Abstract
Polymeric nanoparticles represent a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this manuscript we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK. This nanoformulation has been shown to be efficacious against triple negative breast cancer cells \textit{in vivo}. The physical characteristics of our simulated PEG-PLGA nanoparticles are in good agreement with the previously reported experimentalquantities. To describe the internal structure of the nanoparticles, we apply unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, implying that the individual polymer conformations within such nanoparticles are potentially tunable parameters for the rational design of new polymer nanoparticles for therapeutic applications.
Supplementary materials
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Supplementary information
Description
The Supporting Information contains (i) a detailed description of the analysis carried out, (ii) plots of the fraction of PLGA monomers in the core to analyse the equilibration time of the nanoparticle as a function of time, (iii) plots of the $R_{G}$ of the nanoparticle as a function of time, (iv) the intrinsic density of the various nanoparticle components (v) the distance between the peptides and the COM of the nanoparticle, (vi) the autocorrelation of the peptide local environment, (vii) the UMAP embedding and average cluster distances, (viii) the polymer cluster percentage and polymer cluster enrichment in the two storage locations of the peptides and (ix) the normalized contacts between the peptides and the polymer cluster they interact the most with.
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