The use of supramolecular systems in biomedical applications for antimicrobial properties, biocompatibility, and drug delivery.

Supramolecular chemistry is versatile for developing stimuli-responsive, dynamic and multifunctional structures. In the context of biomedical engineering applications, supramolecular assemblies are particularly useful as coatings for they can closely mimic the natural structure and organisation of the extracellular matrix (ECM), they can also fabricate other complex systems like drug delivery systems and bioinks. In the current context of growing medical device-associated complications and the developments in the controlled drug delivery and regenerative medicine fields, supramolecular assemblies are becoming an indispensable part of the biomedical engineering arsenal. This review covers the different supramolecular assemblies in different biomedical applications with a specific focus on antimicrobial coatings, coatings that enhance biocompatibility, surface modifications on implantable medical devices, systems that promote therapeutic efficiency in cancer therapy, and the development of bioinks. The introduced supramolecular systems include multilayer coating by polyelectrolytes, polymers incorporated with nanoparticles, coating simulation of ECM, and drug delivery systems. A perspective on the application of supramolecular systems is also included.

Titanium induces proinflammatory and tissue-destructive responses in primary human macrophages.

Implants and medical devices are efficient and practical therapeutic solutions for a multitude of pathologies. Titanium and titanium alloys are used in orthopedics, dentistry, and cardiology. Despite very good mechanical properties and corrosion resistance, titanium implants can fail due to inflammatory or tissue degradation-related complications. Macrophages are major immune cells that control acceptance of failure of the implant. In this study, for the first time, we have performed a systematic analysis of the response of differentially activated human macrophages, M(Control), M(IFNγ), and M(IL-4), to the polished and porous titanium surfaces in order to identify the detrimental effect of titanium leading to the tissue destruction and chronic inflammation. Transcriptome analysis revealed that the highest number of differences between titanium and control settings are found in M(IL-4) that model healing type of macrophages. Real-time quantitative polymerase chain reaction analysis confirmed that both polished and porous titanium affected expression of cytokines, chitinases/chitinase-like proteins, and matrix metalloproteinases (MMPs). Titanium-induced release and activation of MMP7 by macrophages was enhanced by fibroblasts in both juxtacrine and paracrine cell interaction models. Production of titanium-induced MMPs and cytokines associated with chronic inflammation was independent of the presence of Staphylococcus aureus. MMP7, one of the most pronounced tissue-destroying factors, and chitinase-like protein YKL-40 were expressed in CD68+ macrophages in peri-implant tissues of patients with orthopedic implants. In summary, we demonstrated that titanium induces proinflammatory and tissue-destructing responses mainly in healing macrophages, and the detrimental effects of titanium surfaces on implant-adjacent macrophages are independent on the bacterial contamination.

The antibacterial properties of branched peptides based on poly(l-arginine): In vitro antibacterial evaluation and molecular dynamic simulations.

The emergence of bacterial strains resistant to antibiotics is a major issue in the medical field. Antimicrobial peptides are widely studied as they do not generate as much resistant bacterial strains as conventional antibiotics and present a broad range of activity. Among them, the homopolypeptide poly(l-arginine) presents promising antibacterial properties, especially in the perspective of its use in biomaterials. Linear poly(l-arginine) has been extensively studied but the impact of its 3D structure remains unknown. In this study, the antibacterial properties of newly synthesized branched poly(l-arginine) peptides, belonging to the family of multiple antigenic peptides, are evaluated. First, in vitro activities of the peptides shows that branched poly(l-arginine) is more efficient than linear poly(l-arginine) containing the same number of arginine residues. Surprisingly, peptides with more arms and more residues are not the most effective. To better understand these unexpected results, interactions between these peptides and the membranes of Gram positive and Gram negative bacteria are simulated thanks to molecular dynamic. It is observed that the bacterial membrane is more distorted by the branched structure than by the linear one and by peptides containing smaller arms. This mechanism of action is in full agreement with in vitro results and suggest that our simulations form a robust model to evaluate peptide efficiency towards pathogenic bacteria.

Preclinical in vitro evaluation of implantable materials: conventional approaches, new models and future directions.

Nowadays, implants and prostheses are widely used to repair damaged tissues or to treat different diseases, but their use is associated with the risk of infection, inflammation and finally rejection. To address these issues, new antimicrobial and anti-inflammatory materials are being developed. Aforementioned materials require their thorough preclinical testing before clinical applications can be envisaged. Although many researchers are currently working on new in vitro tissues for drug screening and tissue replacement, in vitro models for evaluation of new biomaterials are just emerging and are extremely rare. In this context, there is an increased need for advanced in vitro models, which would best recapitulate the in vivo environment, limiting animal experimentation and adapted to the multitude of these materials. Here, we overview currently available preclinical methods and models for biological in vitro evaluation of new biomaterials. We describe several biological tests used in biocompatibility assessment, which is a primordial step in new material's development, and discuss existing challenges in this field. In the second part, the emphasis is made on the development of new 3D models and approaches for preclinical evaluation of biomaterials. The third part focuses on the main parameters to consider to achieve the optimal conditions for evaluating biocompatibility; we also overview differences in regulations across different geographical regions and regulatory systems. Finally, we discuss future directions for the development of innovative biomaterial-related assays: in silico models, dynamic testing models, complex multicellular and multiple organ systems, as well as patient-specific personalized testing approaches.

Peptide Hydrogels as Immunomaterials and Their Use in Cancer Immunotherapy Delivery.

Peptide-based hydrogel biomaterials have emerged as an excellent strategy for immune system modulation. Peptide-based hydrogels are supramolecular materials that self-assemble into various nanostructures through various interactive forces (i.e., hydrogen bonding and hydrophobic interactions) and respond to microenvironmental stimuli (i.e., pH, temperature). While they have been reported in numerous biomedical applications, they have recently been deemed promising candidates to improve the efficacy of cancer immunotherapies and treatments. Immunotherapies seek to harness the body's immune system to preemptively protect against and treat various diseases, such as cancer. However, their low efficacy rates result in limited patient responses to treatment. Here, the immunomaterial's potential to improve these efficacy rates by either functioning as immune stimulators through direct immune system interactions and/or delivering a range of immune agents is highlighted. The chemical and physical properties of these peptide-based materials that lead to immuno modulation and how one may design a system to achieve desired immune responses in a controllable manner are discussed. Works in the literature that reports peptide hydrogels as adjuvant systems and for the delivery of immunotherapies are highlighted. Finally, the future trends and possible developments based on peptide hydrogels for cancer immunotherapy applications are discussed.

A miniaturized genotoxicity evaluation system for fast biomaterial-related risk assessment.

Implants and prostheses are widely used to either repair damaged tissues or treat different diseases. Before an implant reaches the market, multiple preclinical and clinical tests must be performed. Along with cytotoxicity or hemocompatibility preclinical tests, genotoxicity is an essential feature to investigate. Indeed, the materials used for implantation should be non-genotoxic, i.e. they should not promote mutations that can potentially lead to tumour formation. However, given the complexity level of genotoxicity tests, such tests are not readily available to biomaterials researchers, which is the reason why this aspect is severely underreported in the literature. To solve this problem, we developed a simplified genotoxicity test that can be further adapted by standard biomaterials laboratories. We started by simplifying the classic Ames test in Petri dishes, after which we developed a miniaturized test in a microfluidic chip, which takes only 24 hours, requiring significantly less material and space. An automatization option with a customized testing chamber architecture and microfluidics-based control system has been designed as well. This optimized microfluidic chip system can significantly improve the availability of genotoxicity tests for biomaterials developers, with the additional benefit of more in-depth observation and quantitative comparison due to the availability of processable image components.

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties.

Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak.

In vitro two-step granuloma formation model for testing innate immune response to implants and coatings.

The extensive innate immune response to implanted biomaterials contributes significantly to their sub-par performance and failure. Granuloma formation is one of such reactions which results in multi-cell type clusters in line with the immune reaction to implanted materials. However, currently no in vitro model of granuloma formation exists that takes into account the arrival of multiple cell types (immune cells and connective tissue cells) to the implant insertion site. In this study, we developed a two-step model based on stimulated macrophage seeding followed by fibroblast introduction after a physiologically relevant time period for mimicking initial steps of immune reaction to biomaterials and inducing granuloma like behavior. Both LPS and TNF-α induction resulted in granuloma like formations which persisted longer than the control conditions. Introduction of human fibroblasts resulted in the colonization of the surfaces where the cell numbers and the collagen secretion were dependent on the microenvironment. In order to demonstrate the capacity of our model system to monitor the reaction to a given coating, a validated antimicrobial coating (Polyarginine (PAR)/Hyaluronic acid (HA)) was used as a testing bed. The coating prevented the adhesion of macrophages while allowing the adhesion of the fibroblast at the time of their arrival. Similar to its antimicrobial activity, macrophage metabolic activity and M2 differentiation in the presence of PAR was dependent to its chain length. The incorporation of fibroblasts resulted in decreased TNF-α and increased IL-1RA secretion especially in stimulation conditions. The pro- and anti-inflammatory cytokine secretions were low for PAR/HA coatings in line with the decreased number of macrophage presence. In the presence of complex PBMC population, the coating resulted in slightly less cellular attachment, without any significant cytokine secretion; the absence of inflammatory reaction was also demonstrated in vivo in a mouse model. The described in vitro granuloma testing system can control the macrophage reaction as a function of stimulation. It can also be used for testing new biomaterials for the potential innate immune responses and also for validation of implant coatings beyond their primary function from the immune response point of view.

Nitric Oxide Delivering Surfaces: An Overview of Functionalization Strategies and Efficiency Progress.

An overview on the design of nitric oxide (NO) delivering surfaces for biomedical purposes is provided, with a focus on the advances of the past 5 years. A localized supply of NO is of a particular interest due to the pleiotropic biological effects of this diatomic compound. Depending on the generated NO flux, the surface can mimic a physiological release profile to provide an activity on the vascular endothelium or an antibacterial activity. Three requirements are considered to describe the various strategies leading to a surface delivering NO. Firstly, the coating must be selected in accordance with the properties of the substrate (nature, shape, dimensions…). Secondly, the releasing and/or generating kinetics of NO should match the targeted biological application. Currently, the most promising structures are developed to provide an adaptable NO supply driven by pathophysiological needs. Finally, the biocompatibility and the stability of the surface must also be considered regarding the expected residence time of the device. A critical point of view is proposed to help readers in the design of the NO delivering surface according to its expected requirement and therapeutic purpose.

Polyarginine as a Simultaneous Antimicrobial, Immunomodulatory, and miRNA Delivery Agent within Polyanionic Hydrogel.

Implantation of biomedical devices is followed by immune response to the implant, as well as occasionally bacterial, yeast, and/or fungal infections. In this context, new implant materials and coatings that deal with medical device-associated complications are required. Antibacterial and anti-inflammatory materials are also required for wound healing applications, especially in diabetic patients with chronic wounds. In this work, hyaluronic acid (HA) hydrogels with triple activity: antimicrobial, immunomodulatory, and miRNA delivery agent, are presented. It is demonstrated that polyarginine with a degree of polymerization of 30 (PAR30), which is previously shown to have a prolonged antibacterial activity, decreases inflammatory response of lipopolysaccharide-stimulated macrophages. In addition, PAR30 accelerates fibroblast migration in macrophage/fibroblast coculture system, suggesting a positive effect on wound healing. Furthermore, PAR30 allows to load miRNA into HA hydrogels, and then to deliver them into the cells. To the authors knowledge, this study is the first describing miRNA-loaded hydrogels with antibacterial effect and anti-inflammatory features. Such system can become a tool for the treatment of infected wounds, e.g., diabetic ulcers, as well as for foreign body response modulation.

Antibody response to oral biofilm is a biomarker for acute coronary syndrome in periodontal disease.

Cumulative evidence over the last decades have supported the role of gum infections as a risk for future major cardiovascular events. The precise mechanism connecting coronary artery disease (CAD) with periodontal findings has remained elusive. Here, we employ next generation phage display mimotope-variation analysis (MVA) to identify the features of dysfunctional immune system that associate CAD with periodontitis. We identify a fine molecular description of the antigenic epitope repertoires of CAD and its most severe form - acute coronary syndrome (ACS) by profiling the antibody reactivity in a patient cohort with invasive heart examination and complete clinical oral assessment. Specifically, we identify a strong immune response to an EBV VP26 epitope mimicking multiple antigens of oral biofilm as a biomarker for the no-CAD group. With a 2-step biomarker test, we stratify subjects with periodontitis from healthy controls (balanced accuracy 84%), and then assess the risk for ACS with sensitivity 71-89% and specificity 67-100%, depending on the oral health status. Our findings highlight the importance of resolving the immune mechanisms related to severe heart conditions such as ACS in the background of oral health. Prospective validation of these findings will support incorporation of these non-invasive biomarkers into clinical practice.

From 3D printing to 3D bioprinting: the material properties of polymeric material and its derived bioink for achieving tissue specific architectures.

The application of 3D printing technologies fields for biological tissues, organs, and cells in the context of medical and biotechnology applications requires a significant amount of innovation in a narrow printability range. 3D bioprinting is one such way of addressing critical design challenges in tissue engineering. In a more general sense, 3D printing has become essential in customized implant designing, faithful reproduction of microenvironmental niches, sustainable development of implants, in the capacity to address issues of effective cellular integration, and long-term stability of the cellular constructs in tissue engineering. This review covers various aspects of 3D bioprinting, describes the current state-of-the-art solutions for all aforementioned critical issues, and includes various illustrative representations of technologies supporting the development of phases of 3D bioprinting. It also demonstrates several bio-inks and their properties crucial for being used for 3D printing applications. The review focus on bringing together different examples and current trends in tissue engineering applications, including bone, cartilage, muscles, neuron, skin, esophagus, trachea, tympanic membrane, cornea, blood vessel, immune system, and tumor models utilizing 3D printing technology and to provide an outlook of the future potentials and barriers.

The role of biomaterials and scaffolds in immune responses in regenerative medicine: macrophage phenotype modulation by biomaterial properties and scaffold architectures.

Scaffolds are an integral part of the regenerative medicine field. The contact of biomaterials with tissue, as was clearly observed over the years, induces immune reactions in a material and patient specific manner, where both surface and bulk properties of scaffolds, together with their 3D architecture, have a significant influence on the outcome. This review presents an overview of the reactions to the biomaterials with a specific focus on clinical complications with the implants in the context of immune reactions and an overview of the studies involving biomaterial properties and interactions with innate immune system cells. We emphasize the impact of these studies on scaffold selection and upscaling of microenvironments created by biomaterials from 2D to 3D using immune cell encapsulation, seeding in a 3D scaffold and co-culture with relevant tissue cells. 3D microenvironments are covered with a specific focus on innate cells since a large proportion of these studies used innate immune cells. Finally, the recent studies on the incorporation of adaptive immune cells in immunomodulatory systems are covered in this review. Biomaterial-immune cell interactions are a critical part of regenerative medicine applications. Current efforts in establishing the ground rules for such interactions following implantation can control immune response during all phases of inflammation. Thus, in the near future for complete functional recovery, tissue engineering and control over biomaterials must be considered at the first step of immune modulation and this review covers these interactions, which have remained elusive up to now.

Prediction of coating thickness for polyelectrolyte multilayers via machine learning.

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.

An In-Silico Corrosion Model for Biomedical Applications for Coupling With In-Vitro Biocompatibility Tests for Estimation of Long-Term Effects.

The release of metal particles and ions due to wear and corrosion is one of the main underlying reasons for the long-term complications of implantable metallic implants. The rather short-term focus of the established in-vitro biocompatibility tests cannot take into account such effects. Corrosion behavior of metallic implants mostly investigated in in-vitro body-like environments for long time periods and their coupling with long-term in-vitro experiments are not practical. Mathematical modeling and modeling the corrosion mechanisms of metals and alloys is receiving a considerable attention to make predictions in particular for long term applications by decreasing the required experimental duration. By using such in-silico approaches, the corrosion conditions for later stages can be mimicked immediately in in-vitro experiments. For this end, we have developed a mathematical model for multi-pit corrosion based on Cellular Automata (CA). The model consists of two sub-models, corrosion initialization and corrosion progression, each driven by a set of rules. The model takes into account several environmental factors (pH, temperature, potential difference, etc.), as well as stochastic component, present in phenomena such as corrosion. The selection of NiTi was based on the risk of Ni release from the implant surface as it leads to immune reactions. We have also performed experiments with Nickel Titanium (NiTi) shape memory alloys. The images both from simulation and experiments can be analyzed using a set of statistical methods, also investigated in this paper (mean corrosion, standard deviation, entropy etc.). For more widespread implementation, both simulation model, as well as analysis of output images are implemented as a web tool. Described methodology could be applied to any metal provided that the parameters for the model are available. Such tool can help biomedical researchers to test their new metallic implant systems at different time points with respect to ion release and corrosion and couple the obtained information directly with in-vitro tests.

Biomechanical and functional comparison of moulded and 3D printed medical silicones.

Modern 3D printing of implantable devices provides an important opportunity for the development of personalized implants with good anatomical fit. Nevertheless, 3D printing of silicone has been challenging and the recent advances in technology are provided by the systems which can print medical grade silicone via extrusion. However, the potential impacts of the 3D printing process of silicone on its biomechanical properties has not been studied in sufficient detail. Therefore, the present study compares 3D printed and moulded silicone structures for their cytotoxicity, surface roughness, biomechanical properties, and in vivo tissue reaction. The 3D printing process creates increased nanoscale roughness and noticeably changes microscale topography. Neither the presence of these features nor the differences in processes were found to result in an increase in cytotoxicity or tissue reaction for 3D printed structures, exhibiting limited inflammatory reaction and cell viability above the threshold values. On the contrary, the biomechanical properties have demonstrated significant differences in static and dynamic conditions, and in thermal expansion. Our results demonstrate that 3D printing can be used for establishing a better biomechanical microenvironment for the surrounding tissue of the implant particularly for fragile soft tissue like epithelial mucosa without having any negative effect on the cytotoxicity or in vivo reaction to silicone. For engineering of the implants, however, one must consider the differences in mechanical properties to result in correct and personalized geometry and proper physical interaction with tissues.

Reference method for off-line analysis of nitrogen oxides in cell culture media by an ozone-based chemiluminescence detector.

Nitric oxide (NO) and its by-products are important biological signals in human physiology and pathology particularly in the vascular and immune systems. Thus, in situ determination of the NO-related molecule (NOx) levels using embedded sensors is of high importance particularly in the context of cellular biocompatibility testing. However, NOx analytical reference method dedicated to the evaluation of biomaterial biocompatibility testing is lacking. Herein, we demonstrate a PAPA-NONOate-based reference method for the calibration of NOx sensors. After, the validation of this reference method and its potentialities were demonstrated for the detection of the oxidative stress-related NO secretion of vascular endothelial cells in a 3D tissue issued from 3D printing. Such NOx detection method can be an integral part of cell response to biomaterials. Graphical abstract.

New Smart Antimicrobial Hydrogels, Nanomaterials, and Coatings: Earlier Action, More Specific, Better Dosing?

To fight against antibiotic-resistant bacteria adhering and developing on medical devices, which is a growing problem worldwide, researchers are currently developing new "smart" materials and coatings. They consist in delivery of antimicrobial agents in an intelligent way, i.e., only when bacteria are present. This requires the use of new and sophisticated tools combining antimicrobial agents with lipids or polymers, synthetic and/or natural. In this review, three classes of innovative materials are described: hydrogels, nanomaterials, and thin films. Moreover, smart antibacterial materials can be classified into two groups depending on the origin of the stimulus used: those that respond to a nonbiological stimulus (light, temperature, electric and magnetic fields) and those that respond to a biological stimulus related to the presence of bacteria, such as changes in pH or bacterial enzyme secretion. The bacteria presence can induce a pH change that constitutes a first potential biological trigger allowing the system to become active. A second biological trigger signal consists in enzymes produced by bacteria themselves. A complete panel of recent studies will be given focusing on the design of such innovative smart materials that are sensitive to biological triggers.