Tuning of Hydrogel Architectures by Ionotropic Gelation in Microfluidics: Beyond Batch Processing to Multimodal Diagnostics

Artificial neural network modelling hydrodenticity for optimal design by microfluidics of polymer nanoparticles to apply in magnetic resonance imaging

The engineering of nanoparticles impacts the control of their nano-bio interactions at each level of the delivery pathway. Therefore, optimal nanoparticle physicochemical properties should be identified to favour on-target interactions and deliver efficiently active compounds to a specific target. To date, traditional batch processes do not guarantee the reproducibility of results and low polydispersity index of the nanostructures, while microfluidics has emerged as cost effectiveness, short-production time approach to control the nanoparticle size and size distribution. Several thermodynamic processes have been implemented in microfluidics, such as nanoprecipitation, ionotropic gelation, self-assembly, etc., to produce nanoparticles in a continuous mode and high throughput way.   In this work, we show how the Artificial Neural Network (ANN) can be adopted to model the impact of microfluidic parameters (namely, flow rates and polymer concentrations) on the size of the nanoparticles. Promising results have been obtained, with the highest model accuracy reaching 98.9 %, thus confirming the proposed approach's potential applicability for an ANN-guided biopolymer nanoparticle design for biomedical applications. Nanostructures with different degrees of complexity are analysed, and a proof-of-concept machine learning approach is proposed to evaluate Hydrodenticity in biopolymer matrices. STATEMENT OF SIGNIFICANCE: Size, shape and surface charge determine nano-bio interactions of nanoparticles and their ability to target diseases. The ideal nanoparticle design avoids off-target interactions and favours on-target interactions. So, tools enabling the identification of the optimal nanoparticle physicochemical properties for delivery to a specific target are required. In this work, we evaluate the use of Artificial Neural Network (ANN) to analyse the role of microfluidic parameters in predicting the optimal size of the different hydrogel nanoparticles and their ability to trigger Hydrodenticity.

Phase transitions of aqueous solutions of Pluronic F68 in the presence of Diclofenac Sodium

In recent years, advancements in bioengineering and materials science have witnessed increasing interest in synthetic polymers capable of fulfilling various applications. Owing to their distinctive properties, Pluronics can be used as nano-drug carriers, to deliver poorly water-soluble drugs, and as model systems to study colloidal science by tuning amphiphilic properties. In this work, we investigated the effect of diclofenac sodium on the self-assembly and thermoresponsive crystallization of Pluronic F68 in water solutions, by employing experimental rheology and Nuclear Magnetic Resonance (NMR). We built a complete phase diagram as a function of temperature and concentration for 45 wt% Pluronic F68 with various amounts of diclofenac sodium in water. The morphological transitions were followed as a function of temperature via linear rheology. We extrapolated the transition temperatures - identifying distinct phases - as a function of the drug concentration and proposed an empirical model for their prediction. NMR analysis provided further information on the structural characteristics of the systems, shedding light on the interactions between F68 and diclofenac sodium. Although dealing with a pharmaceutical salt, the study is focused on a colloidal system and its interaction with a binding molecule, that is of general interest for colloidal science.

Insulin Activation Mediated by Uptake Mechanisms: A Comparison of the Behavior between Polymer Nanoparticles and Extracellular Vesicles in 3D Liver Tissues

In this work, we compare the role of two different uptake mechanisms in the effectiveness of a nanoformulated drug, specifically insulin. Insulin is activated by interacting with insulin receptors exposed on the liver cell membrane that triggers the uptake and storage of glucose. To prove that the uptake mechanism of a delivery system can interfere directly with the effectiveness of the delivered drug, two extremely different delivery systems are tested. In detail, hydrogel-based NPs (cHANPs) and natural lipid vesicles (EVs) encapsulating insulin are used to trigger the activation of this hormone in 3D liver microtissues (μTs) based on their different uptake mechanisms. Results demonstrated that the fusion mechanism of Ins-EVs mediates faster and more pronounced insulin activation with respect to the endocytic mechanism of Ins-cHANPs. Indeed, the fusion causes an increased reduction in glucose concentration in the culture medium EV-treated l-μTs with respect to free insulin-treated tissues. The same effect is not observed for Ins-cHANPs that, taken up by endocytosis, can only equal the reduction in glucose concentration produced by free insulin in 48 h. Overall, these results demonstrate that the effectiveness of nanoformulated drugs depends on the identity they acquire in the biological context (biological identity). Indeed, the nanoparticle (NP) biological identity, such as the uptake mechanism, triggers a unique set of nano-bio-interactions that is ultimately responsible for their fate both in the extracellular and intracellular compartments.

coupled Hydrodynamic Flow Focusing (cHFF) to Engineer Lipid–Polymer Nanoparticles (LiPoNs) for Multimodal Imaging and Theranostic Applications

An optimal design of nanocarriers is required to overcome the gap between synthetic and biological identity, improving the clinical translation of nanomedicine. A new generation of hybrid vehicles based on lipid–polymer coupling, obtained by Microfluidics, is proposed and validated for theranostics and multimodal imaging applications. A coupled Hydrodynamic Flow Focusing (cHFF) is exploited to control the time scales of solvent exchange and the coupling of the polymer nanoprecipitation with the lipid self-assembly simultaneously, guiding the formation of Lipid–Polymer NPs (LiPoNs). This hybrid lipid–polymeric tool is made up of core–shell structure, where a polymeric chitosan core is enveloped in a lipid bilayer, capable of co-encapsulating simultaneously Gd-DTPA and Irinotecan/Atto 633 compounds. As a result, a monodisperse population of hybrid NPs with an average size of 77 nm, with preserved structural integrity in different environmental conditions and high biocompatibility, can be used for MRI and Optical applications. Furthermore, preliminary results show the enhanced delivery and therapeutic efficacy of Irinotecan-loaded hybrid formulation against U87 MG cancers cells.