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

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.

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.

Smart active-targeting of lipid-polymer hybrid nanoparticles for therapeutic applications: Recent advances and challenges

The advances in producing multifunctional lipid-polymer hybrid nanoparticles (LPHNs) by combining the biomimetic behavior of liposomes and architectural advantages of polymers have provided great opportunities for selective and efficient therapeutics delivery. The constructed LPHNs exhibit different therapeutic efficacies for special uses based on characteristics of different excipients. However, the high mechanical/structural stability of hybrid nano-systems could be viewed as both a negative property and a positive feature, where the concomitant release of drug molecules in a controllable manner is required. In addition, difficulties in scaling up the LPHNs production, due to involvement of several criteria, limit their application for biomedical fields, especially in monitoring, bioimaging, and drug delivery. To address these challenges bio-modifications have exhibited enormous potential to prepare reproducible LPHNs for site-specific therapeutics delivery, diagnostic and preventative applications. The ever-growing surface bio-functionality has provided continuous vitality to this biotechnology and has also posed desirable biosafety to nanoparticles (NPs). As a proof-of-concept, this manuscript provides a crucial review of coated lipid and polymer NPs displaying excellent surface functionality and architectural advantages. We also provide a description of structural classifications and production methodologies, as well as the biomedical possibilities and translational obstacles in the development of surface modified nanocarrier technology.