Polyelectrolyte multilayers for bio‐applications: recent advancements

Pahal, Suman; Gakhar, Ruchi; Raichur, Ashok M.; Varma, Manoj M.

doi:10.1049/iet-nbt.2017.0007

Cited by 26 publications

(30 citation statements)

References 66 publications

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“…It is possible to develop antibacterial surfaces, smart healing materials, and coatings for tissue engineering. Moreover, LbL coatings can be used for loading drugs or other bioactive molecules, which allows their local delivery 6 – 9 . Non-biomedical LbL applications include construction of gas barrier films 10 , optical fiber sensing 11 , and many electrochemical systems 12 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Gribova

Наваліхіна²,

Lysenko³

et al. 2021

Sci Rep

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Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.

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Section: Introductionmentioning

confidence: 99%

Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Gribova

Наваліхіна²,

Lysenko³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

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“…Surface modification is one of the strategies used to tailor the properties of objects, with sizes spanning several orders of magnitude, to make them suitable for practical applications. Such modification may change the chemical, biological, physical properties of the materials, allowing their use, e.g., as drug-delivery systems, cosmetics, textiles, adsorbents, membranes, self-healing and anticorrosive materials, to mention just a few examples [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Tuning the Surface Properties of Poly(Allylamine Hydrochloride)-Based Multilayer Films

et al. 2021

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The layer-by-layer (LbL) method of polyelectrolyte multilayer (PEM) fabrication is extremely versatile. It allows using a pair of any oppositely charged polyelectrolytes. Nevertheless, it may be difficult to ascribe a particular physicochemical property of the resulting PEM to a structural or chemical feature of a single component. A solution to this problem is based on the application of a polycation and a polyanion obtained by proper modification of the same parent polymer. Polyelectrolyte multilayers (PEMs) were prepared using the LbL technique from hydrophilic and amphiphilic derivatives of poly(allylamine hydrochloride) (PAH). PAH derivatives were obtained by the substitution of amine groups in PAH with sulfonate, ammonium, and hydrophobic groups. The PEMs were stable in 1 M NaCl and showed three different modes of thickness growth: exponential, mixed exponential-linear, and linear. Their surfaces ranged from very hydrophilic to hydrophobic. Root mean square (RMS) roughness was very variable and depended on the PEM composition, sample environment (dry, wet), and the polymer constituting the topmost layer. Atomic force microscopy (AFM) imaging of the surfaces showed very different morphologies of PEMs, including very smooth, porous, and structured PEMs with micellar aggregates. Thus, by proper choice of PAH derivatives, surfaces with different physicochemical features (growth type, thickness, charge, wettability, roughness, surface morphology) were obtained.

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“…At second, microcarriers are in vitro already well-established drug delivery systems, in particular for local administrations [ 16 , 17 , 18 , 19 ]. Moreover, the modular construction by stepwise assembly of biopolymers onto a solid core with subsequent core dissolution facilitates a multifunctional and highly biocompatible design [ 20 , 21 ]. Active agents can be assembled either into the core or the multilayer.…”

Section: Introductionmentioning

confidence: 99%

The Metabolic Response of Various Cell Lines to Microtubule-Driven Uptake of Lipid- and Polymer-Coated Layer-by-Layer Microcarriers

et al. 2021

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Lipid structures, such as liposomes or micelles, are of high interest as an approach to support the transport and delivery of active agents as a drug delivery system. However, there are many open questions regarding their uptake and impact on cellular metabolism. In this study, lipid structures were assembled as a supported lipid bilayer on top of biopolymer-coated microcarriers based on the Layer-by-Layer assembly strategy. The functionalized microcarriers were then applied to various human and animal cell lines in addition to primary human macrophages (MΦ). Here, their influence on cellular metabolism and their intracellular localization were detected by extracellular flux analysis and immunofluorescence analysis, respectively. The impact of microcarriers on metabolic parameters was in most cell types rather low. However, lipid bilayer-supported microcarriers induced a decrease in oxygen consumption rate (OCR, indicative for mitochondrial respiration) and extracellular acidification rate (ECAR, indicative for glycolysis) in Vero cells. Additionally, in Vero cells lipid bilayer microcarriers showed a more pronounced association with microtubule filaments than polymer-coated microcarrier. Furthermore, they localized to a perinuclear region and induced nuclei with some deformations at a higher rate than unfunctionalized carriers. This association was reduced through the application of the microtubule polymerization inhibitor nocodazole. Thus, the effect of respective lipid structures as a drug delivery system on cells has to be considered in the context of the respective target cell, but in general can be regarded as rather low.

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Polyelectrolyte multilayers for bio‐applications: recent advancements

Cited by 26 publications

References 66 publications

Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Tuning the Surface Properties of Poly(Allylamine Hydrochloride)-Based Multilayer Films

The Metabolic Response of Various Cell Lines to Microtubule-Driven Uptake of Lipid- and Polymer-Coated Layer-by-Layer Microcarriers

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