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Interplay of lipid and surfactant : Impact on nanoparticle structure

Pink, Demi L. ; Loruthai, Orathai ; Ziolek, Robert M. ; Terry, Ann E. LU ; Barlow, David J. ; Lawrence, M. Jayne and Lorenz, Christian D. (2021) In Journal of Colloid and Interface Science 597. p.278-288
Abstract

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... (More)

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.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
drug delivery vehicles, liquid lipid nanoparticles, molecular dynamics, self-organized maps, small angle neutron scattering
in
Journal of Colloid and Interface Science
volume
597
pages
11 pages
publisher
Elsevier
external identifiers
  • scopus:85104348046
  • pmid:33872884
ISSN
0021-9797
DOI
10.1016/j.jcis.2021.03.136
language
English
LU publication?
yes
id
6195c4bd-4f34-4162-85d9-6a25ba691f98
date added to LUP
2021-04-26 08:10:16
date last changed
2024-03-08 11:39:21
@article{6195c4bd-4f34-4162-85d9-6a25ba691f98,
  abstract     = {{<p>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.</p>}},
  author       = {{Pink, Demi L. and Loruthai, Orathai and Ziolek, Robert M. and Terry, Ann E. and Barlow, David J. and Lawrence, M. Jayne and Lorenz, Christian D.}},
  issn         = {{0021-9797}},
  keywords     = {{drug delivery vehicles; liquid lipid nanoparticles; molecular dynamics; self-organized maps; small angle neutron scattering}},
  language     = {{eng}},
  pages        = {{278--288}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Colloid and Interface Science}},
  title        = {{Interplay of lipid and surfactant : Impact on nanoparticle structure}},
  url          = {{http://dx.doi.org/10.1016/j.jcis.2021.03.136}},
  doi          = {{10.1016/j.jcis.2021.03.136}},
  volume       = {{597}},
  year         = {{2021}},
}