Layer-by-Layer (LbL) coatings are promising tools for the biofunctionalization of biomaterials, as they allow stress-free immobilization of proteins. Here, we explore the possibility to immobilize phosvitin, a highly phosphorylated protein viewed as a model of bone phosphoproteins and, as such, a potential promotive agent of surface-directed biomineralization, into biomimetic LbL architectures. Two immobilization protocols are attempted, first, using phosvitin as the polyanionic component of phosvitin/poly-(L-lysine) films and, second, adsorbing it onto preformed chondroitin sulfate/poly-(L-lysine) films. Surprisingly, it is neither possible to embed phosvitin as the constitutive polyanion of the LbL architectures nor to adsorb it atop preformed films. Instead, phosvitin triggers instant massive film disassembly. This unexpected, incidentally detected behavior constitutes the first example of destructive interactions between LbL films and a third polyelectrolyte, a fortiori a protein, which might open a route toward new stimuli-responsive films for biosensing or drug delivery applications. Interestingly, additional preliminary results still indicate a promotive effect of phosvitin-containing remnant films on calcium phosphate deposition.
Statistical analysis approaches have been developed to predict the biological response to nanoparticles, especially quantitative structure-activity relationship (QSAR) models. But one major limitation remains the quantitative lack of data to build accurate models. The aim of this study was to investigate if simple alternative mathematical models could rank nanoparticles in a very binary way (i.e. toxic or not) in case of small dataset. We synthesized and characterized 25 nanoparticles from 6 metal (hydr)oxide families with particle size and shape tuning. We assessed their toxicity on RAW 264.7 cells and investigated relationships with both physicochemical and dimensional descriptors. A simple partial least square (PLS) regression analysis allowed ranking nanoparticles with respect to their toxicity, without false-negative results. But this model was not predictive due to the specific response of each family to dimensional parameters variations. A classification tree extracted the same main bulk descriptor as PLS, but interestingly showed the relevance of dimensional descriptors for the second and third node. We thus recommend the development of family-specific models and propose the combination of these two simple methods as pre-screening tools, a compromise to bridge the gap between case-by-case studies (expensive and time-consuming) and sophisticated nano-QSAR models (not suitable for small datasets).
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