Liquid lipid nanoparticles (LLN) are oil-in-water nanoemulsions of great interest in the delivery of hydrophobic drug molecules. They consist of a surfactant shell and a liquid lipid core. The small size of LLNs makes them difficult to study, yet a detailed understanding of their internal structure is vital in developing stable drug delivery vehicles (DDVs). Here, we implement machine learning techniques alongside small angle neutron scattering experiments and molecular dynamics simulations to provide critical insight into the conformations and distributions of the lipid and surfactant throughout the LLN. We simulate the assembly of a single LLN composed of the lipid, triolein (GTO), and the surfactant, Brij O10. Our work shows that the addition of surfactant is pivotal in the formation of a disordered lipid core; the even coverage of Brij O10 across the LLN shields the GTO from water and so the lipids adopt conformations that reduce crystallisation. We demonstrate the superior ability of unsupervised artificial neural networks in characterising the internal structure of DDVs, when compared to more conventional geometric methods. We have identified, clustered, classified and averaged the dominant conformations of lipid and surfactant molecules within the LLN, providing a multi-scale picture of the internal structure of LLNs.
Keywords: drug delivery vehicles; liquid lipid nanoparticles; molecular dynamics; self-organized maps; small angle neutron scattering.
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