Center for Advanced Biomaterials for Healthcare

Triple‐negative breast cancer (TNBC) is characterized by unique clinical and pathological traits, from which its aggressive nature and the absence of specific treatments are the most worrying. Prostaglandin E1 (PGE1) can affect the morphology of human breast tumor cell lines, but its potential therapeutic effects appear to be counteracted by its high degradability in physiological environments. For this reason, polylactic‐glycolic acid polymeric microparticles (MPs) are developed to stabilize PGE1 in damp conditions and enable sustained local release for the treatment of TNBC after surgical removal of the tumor mass. Specifically, the PGE1 embedded MPs are produced using a double emulsion solvent‐evaporation method, then analyzed through Mastersizer and scanning electron microscopy. Afterward, the encapsulation efficiency and the PGE1 release are examined using liquid chromatography–mass spectrometry, and their stability at various storage temperatures is assessed. Finally, the carrier toxicity is evaluated in TNBC cells and colon adenocarcinoma epithelial cells, Caco‐2. A reliable action on carcinogenic cells specific to breast cancer is observed. Although in vivo studies are still needed, these results open the way to using such a carrier for the intralesional delivery of PGE1 and its use against TNBC.

We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this approach, the model captures the transduction of environmental cues into biological responses directly from experimental observations, without explicitly predefining cell behavior. This enables the prediction of dynamic, environment-dependent cell behavior and offers a scalable and flexible alternative to traditional rule-based ABM. To illustrate its potential, we present an application to barotactic cell migration data from microfluidic device experiments, where cells adapt their migration behavior based on pressure gradients, demonstrating the model’s ability to generalize across varying geometries and pressure configurations. Thus, this approach introduces a novel direction for modeling how cells sense and transduce environmental cues into biological behaviors.

New strategies for enhancing drug delivery to the blood-brain barrier (BBB) represent a major challenge in treating cerebral diseases. Nanoemulsion-based nanocarriers represent an ideal candidate to improve drug delivery thanks to their versatility in functionalization and cargo protection. In this work, a paclitaxel-loaded nano-emulsion has been firstly functionalized and stabilized with two layers constituted of chitosan and hyaluronic acid, and, secondly, the latter has been conjugated to the CRT peptide. CRT is a bioactive peptide that selectively recognizes bEnd.3 cells, a model of the BBB, thanks to its interactions with transferrin (Tf) and its receptor (TfR). Cytotoxic results showed a 41.5% higher uptake of CRT functionalized nano-emulsion than the negative control, demonstrating the ability of this novel tool to be accumulated in brain endothelium tissue. Based upon these results, our approach can be fully generalizable to the design of multifunctional nanocarriers for delivery of therapeutic agents to the central nervous systems.

The development of biomaterials capable of regulating cellular processes and guiding cell fate decisions has broad implications in tissue engineering, regenerative medicine, and cell-based assays for drug development and disease modeling. Recent studies have shown that three-dimensional (3D) nanoscale physical cues such as nanotopography can modulate various cellular processes like adhesion and endocytosis by inducing nanoscale curvature on the plasma and nuclear membranes. Two-dimensional (2D) biochemical cues such as protein micropatterns can also regulate cell function and fate by controlling cellular geometries. Development of biomaterials with precise control over nanoscale physical and biochemical cues can significantly influence programming cell function and fate. In this study, we utilized a laser-assisted micropatterning technique to manipulate the 2D architectures of cells on 3D nanopillar platforms. We performed a comprehensive analysis of cellular and nuclear morphology and deformation on both nanopillar and flat substrates. Our findings demonstrate the precise engineering of single cell architectures through 2D micropatterning on nanopillar platforms. We show that the coupling between the nuclear and cell shape is disrupted on nanopillar surfaces compared to flat surfaces. Furthermore, our results suggest that cell elongation on nanopillars enhances nanopillar-induced endocytosis. We believe our platform serves as a versatile tool for further explorations into programming cell function and fate through combined physical cues that create nanoscale curvature on cell membranes and biochemical cues that control the geometry of the cell.

Intrinsic biophysical cell properties hold an enormous potential for cell class and state classification in microfluidics, allowing to avoid the need of cost intensive fluorescence labelling. Several methods can accomplish cell identification, while convolutional neural networks show an outstanding performance compared to other state-of-the-art classification methods, regarding accuracy and speed. In fact, neural networks show high performance for known image class prediction but struggles when unknown (out of distribution) image classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in image misclassification. In fact, to distinguish unknown cell classes, a neural network must first construct an in-distribution of known images to afterwards detect out of distribution as unknowns, which is also called open-set classification assumption. Ones, a new cell class is identified, the neural network can be retrained with the obtained knowledge to dynamically update its cell class database. This process can be simply repeated for each new detected cell class. We applied this open-set idea to scattering pattern snapshots of different classes of living cells obtained in microfluidics. Our outcome shows a proof-of-concept for open-set based convolutional neural network for cell image classification, which can be applied to a wide range of single cell classification approaches to reduce uncertainty in machine learning based technologies.

The computation of the brain relies on the highly efficient communication among billions of neurons. Such efficiency derives from the brain’s plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ∼20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards investigation of novel solutions to emulate specific brain features (such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening the understanding of how neurons communicate, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of biological signals. In fact, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials in particular are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still successfully communicating with silicon-based electronics. Here, we envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues. We believe that this approach may pave the way towards the development of a functional bidirectional communication between biological and artificial ‘brains’, offering new potential therapeutic applications and allowing for novel approaches in prosthetics.

Current 3D cancer models (in vitro) fail to reproduce complex cancer cell extracellular matrices (ECMs) and the interrelationships occurring (in vivo) in the tumor microenvironment (TME). Herein, we propose 3D in vitro colorectal cancer microtissues (3D CRC μTs), which reproduce the TME more faithfully in vitro. Normal human fibroblasts were seeded onto porous biodegradable gelatin microbeads (GPMs) and were continuously induced to synthesize and assemble their own ECMs (3D Stroma μTs) in a spinner flask bioreactor. Then, human colon cancer cells were dynamically seeded onto the 3D Stroma μTs to achieve the 3D CRC μTs. Morphological characterization of the 3D CRC μTs was performed to assess the presence of different complex macromolecular components that feature in vivo in the ECM. The results showed the 3D CRC μTs recapitulated the TME in terms of ECM remodeling, cell growth, and the activation of normal fibroblasts toward an activated phenotype. Then, the microtissues were assessed as a drug screening platform by evaluating the effect of 5-Fluorouracil (5-FU), curcumin-loaded nanoemulsions (CT-NE-Curc), and the combination of the two. When taken together, the results showed that our microtissues are promising in that they can help clarify complex cancer–ECM interactions and evaluate the efficacy of therapies. Moreover, they may be combined with tissue-on-chip technologies aimed at addressing further studies in cancer progression and drug discovery.

The design, synthesis and characterization of a novel nucleoamino acid derivative based on an l-tryptophanamide functionalised with a thymine nucleobase (named TrpT) is here described. The novel construct’s tendency to self-assemble into supramolecular networks in aqueous solution was demonstrated by dynamic light scattering (DLS), circular dichroism (CD), fluorescence and UV spectroscopic measurements. TrpT nanoaggregates showed good stability (up to 5 h) at 140 µM and proved to comprise species of mean hydrodynamic diameter 330 nm and a homogeneous size distribution; scanning electron microscopy (SEM) analysis further revealed these to be spherical-shaped assemblies. The ability of TrpT nanoaggregates to bind curcumin, selected as a model anticancer drug, was also evaluated and its release was monitored over time by confocal microscopy. Molecular docking studies were performed on both TrpT self-assembly and curcumin-loaded nanoaggregates suggesting that the phytomolecule can be accommodated in the interior of the supramolecular network via hydrophobic (π−π and π-alkyl) interactions; the formation of TrpT-curcumin adducts may improve the polarity of the highly-hydrophobic curcumin with a resulting logP closer to the optimal values expected for a good drug bioavailability, as estimated by the ADMETlab software. Finally, the high stability of TrpT nanoassembly in human serum, and the absence of significant toxic effects on human model cells in a cell viability assay, were also demonstrated. Despite its thymine-based scaffold, TrpT was shown not to bind adenine-bearing nucleic acids, suggesting that this interaction is hindered by its intrinsic propensity to self-assemble in preference to forming A-T base pairings. Instead, TrpT was able to interact with a serum protein such as bovine serum albumin (BSA), known to improve the bloodstream transportation and bioavailability of its cargo. Collectively, our findings support the potential use of TrpT for the development of new drug delivery systems.Graphical abstract

Bacteriophages have attracted great attention in the bioengineering field in diverse research areas from tissue engineering to therapeutic and clinical applications. Recombinant filamentous bacteriophage, carrying multiple copies of foreign peptides on protein capsid has been successfully used in the vaccine delivery setting, even if their plasma instability and degradation have limited their use on the pharmaceutical market. Encapsulation techniques in polymeric materials can be applied to preserve bacteriophage activity, extend its half-life, and finely regulate their release in the target environment. The main goal of this study was to provide tunable formulations of the bacteriophage encapsulated in polymeric microparticles (MPs). We used poly (lactic-co-glycolic-acid) as a biocompatible and biodegradable polymer with ammonium bicarbonate as a porogen to encapsulate bacteriophage expressing OVA (257–264) antigenic peptide. We demonstrate that nano-engineered fdOVA bacteriophages encapsulated in MPs preserve their structure and are immunologically active, inducing a strong immune response towards the delivered peptide. Moreover, MP encapsulation prolongs bacteriophage stability over time also at room temperature. Additionally, in this study, we show the ability of in silico-supported approach to predict and tune the release of bacteriophages. These results lay the framework for a versatile bacteriophage-based vaccine delivery system that could successfully generate robust immune responses in a sustained manner, to be used as a platform against cancer and new emerging diseases.Graphical abstractSynopsis: administration of recombinant bacteriophage-loaded PLGA microparticles for antigen delivery. PLGA microparticles release the bacteriophages, inducing activation of dendritic cells and enhancing antigen presentation and specific T cell response. Bacteriophage-encapsulated microneedles potentially can be administered into human body and generate robust immune responses.