Interplay of lipid and surfactant: Impact on nanoparticle structure

Demi L. Pink; Orathai Loruthai; Robert M. Ziolek; Ann E. Terry; David J. Barlow; M. Jayne Lawrence; Christian D. Lorenz

doi:10.1016/j.jcis.2021.03.136

Interplay of lipid and surfactant: Impact on nanoparticle structure

Demi L. Pink, Orathai Loruthai, Robert M. Ziolek, Ann E. Terry, David J. Barlow, M. Jayne Lawrence^*, Christian D. Lorenz

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

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 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.

Original language	English
Pages (from-to)	278-288
Number of pages	11
Journal	JOURNAL OF COLLOID AND INTERFACE SCIENCE
Volume	597
DOIs	https://doi.org/10.1016/j.jcis.2021.03.136
Publication status	Published - Sept 2021

Keywords

drug delivery vehicles
liquid lipid nanoparticles
molecular dynamics
self-organized maps
small angle neutron scattering

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10.1016/j.jcis.2021.03.136

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title = "Interplay of lipid and surfactant: Impact on nanoparticle structure",

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 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",

author = "Pink, {Demi L.} and Orathai Loruthai and Ziolek, {Robert M.} and Terry, {Ann E.} and Barlow, {David J.} and Lawrence, {M. Jayne} and Lorenz, {Christian D.}",

note = "Funding Information: Through C.D.L.'s membership within the UK HPC Materials Chemistry Consortium, which is funded by the Office of Science and Technology through the EPSRC High End Computing Programme (Grant No. EP/L000202, EP/R029431), the use of ARCHER, the UK National Supercomputing Service (http://www.archer.ac.uk) and the UK Materials and Molecular Modelling Hub (MMM Hub), which is partially funded by the EPSRC (EP/P020194/1, EP/T022213), was made possible for the molecular dynamics simulations presented in this work. We acknowledge the support by the Biotechnology and Biological Sciences Research Council (BB/M009513/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. acknowledges the supportive research environment of the EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, No. EP/L015854/1). Experiments at the ISIS Neutron and Muon Source were supported by beamtime allocation from STFC, and the SANS data are available at 10.5286/ISIS.E.RB1620384. This work benefited from the use of the SasView application, originally developed under National Science Foundation Award DMR-0520547. SasView also contains a code developed with funding from the European Union Horizon 2020 research and innovation program under the SINE2020 project, Grant No. 654000. Funding Information: Through C.D.L.'s membership within the UK HPC Materials Chemistry Consortium, which is funded by the Office of Science and Technology through the EPSRC High End Computing Programme (Grant No. EP/L000202, EP/R029431), the use of ARCHER, the UK National Supercomputing Service (http://www.archer.ac.uk) and the UK Materials and Molecular Modelling Hub (MMM Hub), which is partially funded by the EPSRC (EP/P020194/1, EP/T022213), was made possible for the molecular dynamics simulations presented in this work. We acknowledge the support by the Biotechnology and Biological Sciences Research Council (BB/M009513/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. acknowledges the supportive research environment of the EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, No. EP/L015854/1). Experiments at the ISIS Neutron and Muon Source were supported by beamtime allocation from STFC, and the SANS data are available at 10.5286/ISIS.E.RB1620384. This work benefited from the use of the SasView application, originally developed under National Science Foundation Award DMR-0520547. SasView also contains a code developed with funding from the European Union Horizon 2020 research and innovation program under the SINE2020 project, Grant No. 654000. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = sep,

doi = "10.1016/j.jcis.2021.03.136",

language = "English",

volume = "597",

pages = "278--288",

journal = "JOURNAL OF COLLOID AND INTERFACE SCIENCE",

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T2 - Impact on nanoparticle structure

AU - Pink, Demi L.

AU - Loruthai, Orathai

AU - Ziolek, Robert M.

AU - Terry, Ann E.

AU - Barlow, David J.

AU - Lawrence, M. Jayne

AU - Lorenz, Christian D.

N1 - Funding Information: Through C.D.L.'s membership within the UK HPC Materials Chemistry Consortium, which is funded by the Office of Science and Technology through the EPSRC High End Computing Programme (Grant No. EP/L000202, EP/R029431), the use of ARCHER, the UK National Supercomputing Service (http://www.archer.ac.uk) and the UK Materials and Molecular Modelling Hub (MMM Hub), which is partially funded by the EPSRC (EP/P020194/1, EP/T022213), was made possible for the molecular dynamics simulations presented in this work. We acknowledge the support by the Biotechnology and Biological Sciences Research Council (BB/M009513/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. acknowledges the supportive research environment of the EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, No. EP/L015854/1). Experiments at the ISIS Neutron and Muon Source were supported by beamtime allocation from STFC, and the SANS data are available at 10.5286/ISIS.E.RB1620384. This work benefited from the use of the SasView application, originally developed under National Science Foundation Award DMR-0520547. SasView also contains a code developed with funding from the European Union Horizon 2020 research and innovation program under the SINE2020 project, Grant No. 654000. Funding Information: Through C.D.L.'s membership within the UK HPC Materials Chemistry Consortium, which is funded by the Office of Science and Technology through the EPSRC High End Computing Programme (Grant No. EP/L000202, EP/R029431), the use of ARCHER, the UK National Supercomputing Service (http://www.archer.ac.uk) and the UK Materials and Molecular Modelling Hub (MMM Hub), which is partially funded by the EPSRC (EP/P020194/1, EP/T022213), was made possible for the molecular dynamics simulations presented in this work. We acknowledge the support by the Biotechnology and Biological Sciences Research Council (BB/M009513/1) via the London Interdisciplinary Doctoral Programme (LIDo). R.M.Z. acknowledges the supportive research environment of the EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, No. EP/L015854/1). Experiments at the ISIS Neutron and Muon Source were supported by beamtime allocation from STFC, and the SANS data are available at 10.5286/ISIS.E.RB1620384. This work benefited from the use of the SasView application, originally developed under National Science Foundation Award DMR-0520547. SasView also contains a code developed with funding from the European Union Horizon 2020 research and innovation program under the SINE2020 project, Grant No. 654000. Publisher Copyright: © 2021 Elsevier Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/9

Y1 - 2021/9

N2 - 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.

AB - 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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KW - molecular dynamics

KW - self-organized maps

KW - small angle neutron scattering

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JO - JOURNAL OF COLLOID AND INTERFACE SCIENCE

JF - JOURNAL OF COLLOID AND INTERFACE SCIENCE

ER -

Interplay of lipid and surfactant: Impact on nanoparticle structure

Abstract

Keywords

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