Development of 3D Printed pNIPAM-Chitosan Scaffolds for Dentoalveolar Tissue Engineering.

While available treatments have addressed a variety of complications in the dentoalveolar region, associated challenges have resulted in exploration of tissue engineering techniques. Often, scaffold biomaterials with specific properties are required for such strategies to be successful, development of which is an active area of research. This study focuses on the development of a copolymer of poly (N-isopropylacrylamide) (pNIPAM) and chitosan, used for 3D printing of scaffolds for dentoalveolar regeneration. The synthesized material was characterized by Fourier transform infrared spectroscopy, and the possibility of printing was evaluated through various printability tests. The rate of degradation and swelling was analyzed through gravimetry, and surface morphology was characterized by scanning electron microscopy. Viability of dental pulp stem cells seeded on the scaffolds was evaluated by live/dead analysis and DNA quantification. The results demonstrated successful copolymerization, and three formulations among various synthesized formulations were successfully 3D printed. Up to 35% degradability was confirmed within 7 days, and a maximum swelling of approximately 1200% was achieved. Furthermore, initial assessment of cell viability demonstrated biocompatibility of the developed scaffolds. While further studies are required to achieve the tissue engineering goals, the present results tend to indicate that the proposed hydrogel might be a valid candidate for scaffold fabrication serving dentoalveolar tissue engineering through 3D printing.

Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications.

Automated technologies are attractive for enhancing the robust manufacturing of tissue-engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes, and imaging of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features as noninvasive quality attributes. As a result, empty microwells were identified with a 96% accuracy, and dice coefficient of 0.84 for segmentation. Design of experiment are used for the optimization of liquid handling parameters to minimize empty microwells during long-term differentiation protocols. We found no significant effect of aspiration or dispension speeds at and beyond manual speed. Instead, repeated media changes and time in culture were the driving force or microtissue displacements. As the ovine model is the preclinical model of choice for large skeletal defects, we used ovine periosteum-derived cells to form cartilage-intermediate microtissues. Increased expression of COL2A1 confirms chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis shows an increased secretion of cartilaginous extracellular matrix and glycosaminoglycans in larger microtissues. Furthermore, microtissue-based implants are capable of forming mineralized tissues and bone after 4 weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues and anticipate the progressive substitution of manual operations with automated solutions for the manufacturing of microtissue-based living implants.

COMMBINI: an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse.

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.

Model-Based Design to Enhance Neotissue Formation in Additively Manufactured Calcium-Phosphate-Based Scaffolds.

In biomaterial-based bone tissue engineering, optimizing scaffold structure and composition remains an active field of research. Additive manufacturing has enabled the production of custom designs in a variety of materials. This study aims to improve the design of calcium-phosphate-based additively manufactured scaffolds, the material of choice in oral bone regeneration, by using a combination of in silico and in vitro tools. Computer models are increasingly used to assist in design optimization by providing a rational way of merging different requirements into a single design. The starting point for this study was an in-house developed in silico model describing the in vitro formation of neotissue, i.e., cells and the extracellular matrix they produced. The level set method was applied to simulate the interface between the neotissue and the void space inside the scaffold pores. In order to calibrate the model, a custom disk-shaped scaffold was produced with prismatic canals of different geometries (circle, hexagon, square, triangle) and inner diameters (0.5 mm, 0.7 mm, 1 mm, 2 mm). The disks were produced with three biomaterials (hydroxyapatite, tricalcium phosphate, and a blend of both). After seeding with skeletal progenitor cells and a cell culture for up to 21 days, the extent of neotissue growth in the disks' canals was analyzed using fluorescence microscopy. The results clearly demonstrated that in the presence of calcium-phosphate-based materials, the curvature-based growth principle was maintained. Bayesian optimization was used to determine the model parameters for the different biomaterials used. Subsequently, the calibrated model was used to predict neotissue growth in a 3D gyroid structure. The predicted results were in line with the experimentally obtained ones, demonstrating the potential of the calibrated model to be used as a tool in the design and optimization of 3D-printed calcium-phosphate-based biomaterials for bone regeneration.

Cartilaginous endplates: A comprehensive review on a neglected structure in intervertebral disc research.

The cartilaginous endplates (CEP) are key components of the intervertebral disc (IVD) necessary for sustaining the nutrition of the disc while distributing mechanical loads and preventing the disc from bulging into the adjacent vertebral body. The size, shape, and composition of the CEP are essential in maintaining its function, and degeneration of the CEP is considered a contributor to early IVD degeneration. In addition, the CEP is implicated in Modic changes, which are often associated with low back pain. This review aims to tackle the current knowledge of the CEP regarding its structure, composition, permeability, and mechanical role in a healthy disc, how they change with degeneration, and how they connect to IVD degeneration and low back pain. Additionally, the authors suggest a standardized naming convention regarding the CEP and bony endplate and suggest avoiding the term vertebral endplate. Currently, there is limited data on the CEP itself as reported data is often a combination of CEP and bony endplate, or the CEP is considered as articular cartilage. However, it is clear the CEP is a unique tissue type that differs from articular cartilage, bony endplate, and other IVD tissues. Thus, future research should investigate the CEP separately to fully understand its role in healthy and degenerated IVDs. Further, most IVD regeneration therapies in development failed to address, or even considered the CEP, despite its key role in nutrition and mechanical stability within the IVD. Thus, the CEP should be considered and potentially targeted for future sustainable treatments.

Position paper From the digital twins in healthcare to the Virtual Human Twin: a moon-shot project for digital health research.

The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly focused patient-specific models able to predict specific quantities of clinical relevance. While it has facilitated harvesting the low-hanging fruits, this narrow focus is, in the long run, leaving some significant challenges that slow the adoption of digital twins in healthcare. This position paper lays the conceptual foundations for developing the Virtual Human Twin (VHT). The VHT is intended as a distributed and collaborative infrastructure, a collection of technologies and resources (data, models) that enable it, and a collection of Standard Operating Procedures (SOP) that regulate its use. The VHT infrastructure aims to facilitate academic researchers, public organisations, and the biomedical industry in developing and validating new digital twins in healthcare solutions with the possibility of integrating multiple resources if required by the specific context of use. Healthcare professionals and patients can also use the VHT infrastructure for clinical decision support or personalised health forecasting. As the European Commission launched the EDITH coordination and support action to develop a roadmap for the development of the Virtual Human Twin, this position paper is intended as a starting point for the consensus process and a call to arms for all stakeholders.

Coupling biomechanical models of implants with biodegradation models: A case study for biodegradable mandibular bone fixation plates.

In fracture fixation, biodegradable implant materials are an interesting alternative to conventional non-biodegradable materials as the latter often require a second implant removal surgery to avoid long-term complications. In this study, we present an in silico strategy to design/study biodegradable metal implants focusing on mandibular fracture fixation plates of WE43 (Mg alloy). The in silico strategy is composed of an orchestrated interaction between three separate computational models. The first model simulates the mass loss of the degradable implant based on the chemistry of Mg biodegradation. A second model estimates the loading on the jaw plate in the physiological environment, incorporating a phenomenological dynamic bone regeneration process. The third model characterizes the mechanical behavior of the jaw plate and the influence of material degradation on the mechanical behavior. A sensitivity analysis was performed on parameters related to choices regarding numerical implementation and parameter dependencies were implemented to guarantee robust and correct results. Different clinical scenarios were tested, related to the amount of screws used to fix the plate. The results showed a lower initial strength when more screw holes were left open, as well as a faster decrease over time in strength due to the increased area available for surface degradation. The obtained degradation results were found to be in accordance with previously reported data of in vivo studies with biodegradable plates. The combination of these three models allows for the design of patient-specific biodegradable fixation implants able to deliver the desired mechanical behavior tuned to the bone regeneration process.

A simple geometrical model of the electrostatic environment around the catalytic center of the ribosome and its significance for the elongation cycle kinetics.

The central function of the large subunit of the ribosome is to catalyze peptide bond formation. This biochemical reaction is conducted at the peptidyl transferase center (PTC). Experimental evidence shows that the catalytic activity is affected by the electrostatic environment around the peptidyl transferase center. Here, we set up a minimal geometrical model fitting the available x-ray solved structures of the ribonucleic cavity around the catalytic center of the large subunit of the ribosome. The purpose of this phenomenological model is to estimate quantitatively the electrostatic potential and electric field that are experienced during the peptidyl transfer reaction. At least two reasons motivate the need for developing this quantification. First, we inquire whether the electric field in this particular catalytic environment, made only of nucleic acids, is of the same order of magnitude as the one prevailing in catalytic centers of the proteic enzymes counterparts. Second, the protein synthesis rate is dependent on the nature of the amino acid sequentially incorporated in the nascent chain. The activation energy of the catalytic reaction and its detailed kinetics are shown to be dependent on the mechanical work exerted on the amino acids by the electric field, especially when one of the four charged amino acid residues (R, K, E, D) has previously been incorporated at the carboxy-terminal end of the peptidyl-tRNA. Physical values of the electric field provide quantitative knowledge of mechanical work, activation energy and rate of the peptide bond formation catalyzed by the ribosome. We show that our theoretical calculations are consistent with two independent sets of previously published experimental results. Experimental results for E.coli in the minimal case of the dipeptide bond formation when puromycin is used as the final amino acid acceptor strongly support our theoretically derived reaction time courses. Experimental Ribo-Seq results on E. coli and S. cerevisiae comparing the residence time distribution of ribosomes upon specific codons are also well accounted for by our theoretical calculations. The statistical queueing time theory was used to model the ribosome residence time per codon during nascent protein elongation and applied for the interpretation of the Ribo-Seq data. The hypo-exponential distribution fits the residence time observed distribution of the ribosome on a codon. An educated deconvolution of this distribution is used to estimate the rates of each elongation step in a codon specific manner. Our interpretation of all these results sheds light on the functional role of the electrostatic profile around the PTC and its impact on the ribosome elongation cycle.

An Empirical Model Linking Physico-Chemical Biomaterial Characteristics to Intra-Oral Bone Formation.

Facial trauma, bone resection due to cancer, periodontal diseases, and bone atrophy following tooth extraction often leads to alveolar bone defects that require bone regeneration in order to restore dental function. Guided bone regeneration using synthetic biomaterials has been suggested as an alternative approach to autologous bone grafts. The efficiency of bone substitute materials seems to be influenced by their physico-chemical characteristics; however, the debate is still ongoing on what constitutes optimal biomaterial characteristics. The purpose of this study was to develop an empirical model allowing the assessment of the bone regeneration potential of new biomaterials on the basis of their physico-chemical characteristics, potentially giving directions for the design of a new generation of dental biomaterials. A quantitative data set was built composed of physico-chemical characteristics of seven commercially available intra-oral bone biomaterials and their in vivo response. This empirical model allowed the identification of the construct parameters driving optimized bone formation. The presented model provides a better understanding of the influence of driving biomaterial properties in the bone healing process and can be used as a tool to design bone biomaterials with a more controlled and custom-made composition and structure, thereby facilitating and improving the clinical translation.

Why Animal Experiments Are Still Indispensable in Bone Research: A Statement by the European Calcified Tissue Society.

Major achievements in bone research have always relied on animal models and in vitro systems derived from patient and animal material. However, the use of animals in research has drawn intense ethical debate and the complete abolition of animal experimentation is demanded by fractions of the population. This phenomenon is enhanced by the reproducibility crisis in science and the advance of in vitro and in silico techniques. 3D culture, organ-on-a-chip, and computer models have improved enormously over the last few years. Nevertheless, the overall complexity of bone tissue cross-talk and the systemic and local regulation of bone physiology can often only be addressed in entire vertebrates. Powerful genetic methods such as conditional mutagenesis, lineage tracing, and modeling of the diseases enhanced the understanding of the entire skeletal system. In this review endorsed by the European Calcified Tissue Society (ECTS), a working group of investigators from Europe and the US provides an overview of the strengths and limitations of experimental animal models, including rodents, fish, and large animals, as well the potential and shortcomings of in vitro and in silico technologies in skeletal research. We propose that the proper combination of the right animal model for a specific hypothesis and state-of-the-art in vitro and/or in silico technology is essential to solving remaining important questions in bone research. This is crucial for executing most efficiently the 3R principles to reduce, refine, and replace animal experimentation, for enhancing our knowledge of skeletal biology, and for the treatment of bone diseases that affect a large part of society. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Mapping the use of computational modelling and simulation in clinics: A survey.

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms "Personalised medicine" and "Patient-specific modelling" were the most well-known within the respondents. "In silico clinical trials" and "Digital Twin" were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.

Potentials of a Plasma-Aerosol System for Wound Healing Advanced by Drug Introduction: An In Vitro Study.

Cold plasmas have found their application in a wide range of biomedical fields by virtue of their high chemical reactivity. In the past decades, many attempts have been made to use cold plasmas in wound healing, and within this field, many studies have focused on plasma-induced cell proliferation mechanisms. In this work, one step further has been taken to demonstrate the advanced role of plasma in wound healing. To this end, the simultaneous ability of plasma to induce cell proliferation and permeabilize treated cells has been examined in the current study. The driving force was to advance the wound healing effect of plasma with drug delivery. On this subject, we demonstrate in vitro the healing effect of Ar, Ar+N2 plasma, and their aerosol counterparts. A systematic study has been carried out to study the role of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in cell adhesion, signaling, differentiation, and proliferation. An additional investigation was also performed to study the permeabilization of cells and the delivery of the modeled drug carrier fluorescein isothiocyanate (FITC) labeled dextran into cells upon plasma treatment. Short 35 s plasma treatments were found to promote fibroblast adhesion, migration, signaling, proliferation, and differentiation by means of reactive oxygen and nitrogen species (RONS) created by plasma and deposited into the cell environment. The impact of the plasma downstream products NO2- and NO3- on the expressions of the focal adhesion's genes, syndecans, and collagens was observed to be prominent. On the other hand, the differentiation of fibroblasts to myofibroblasts was mainly initiated by ROS produced by the plasma. In addition, the ability of plasma to locally permeabilize fibroblast cells was demonstrated. During proliferative cell treatment, plasma can simultaneously induce cell membrane permeabilization (d ∼ 7.3 nm) by the species OH and H2O2. The choice for a plasma or a plasma-aerosol configuration thus allows the possibility to change the spatial chemistry of drug delivery molecules and thus to locally deliver drugs. Accordingly, this study offers a pivotal step toward plasma-assisted wound healing advanced by drug delivery.

Stirred culture of cartilaginous microtissues promotes chondrogenic hypertrophy through exposure to intermittent shear stress.

Cartilage microtissues are promising tissue modules for bottom up biofabrication of implants leading to bone defect regeneration. Hitherto, most of the protocols for the development of these cartilaginous microtissues have been carried out in static setups, however, for achieving higher scales, dynamic process needs to be investigated. In the present study, we explored the impact of suspension culture on the cartilage microtissues in a novel stirred microbioreactor system. To study the effect of the process shear stress, experiments with three different impeller velocities were carried out. Moreover, we used mathematical modeling to estimate the magnitude of shear stress on the individual microtissues during dynamic culture. Identification of appropriate mixing intensity allowed dynamic bioreactor culture of the microtissues for up to 14 days maintaining microtissue suspension. Dynamic culture did not affect microtissue viability, although lower proliferation was observed as opposed to the statically cultured ones. However, when assessing cell differentiation, gene expression values showed significant upregulation of both Indian Hedgehog (IHH) and collagen type X (COLX), well known markers of chondrogenic hypertrophy, for the dynamically cultured microtissues. Exometabolomics analysis revealed similarly distinct metabolic profiles between static and dynamic conditions. Dynamic cultured microtissues showed a higher glycolytic profile compared with the statically cultured ones while several amino acids such as proline and aspartate exhibited significant differences. Furthermore, in vivo implantations proved that microtissues cultured in dynamic conditions are functional and able to undergo endochondral ossification. Our work demonstrated a suspension differentiation process for the production of cartilaginous microtissues, revealing that shear stress resulted to an acceleration of differentiation towards hypertrophic cartilage.

Cracking the genetic code with neural networks.

The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons and amino acids and build the complete deciphering dictionary upon presentation of transcripts proteins data training pairs. We compared different Deep Learning neural network architectures and estimated quantitatively the size of the required human transcriptomic training set to achieve the best possible accuracy in the codon-to-amino-acid mapping. We also investigated the effect of a codon embedding layer assessing the semantic similarity between codons on the rate of increase of the training accuracy. We further investigated the benefit of quantifying and using the unbalanced representations of amino acids within real human proteins for a faster deciphering of rare amino acids codons. Deep neural networks require huge amount of data to train them. Deciphering the genetic code by a neural network is no exception. A test accuracy of 100% and the unequivocal deciphering of rare codons such as the tryptophan codon or the stop codons require a training dataset of the order of 4-22 millions cumulated pairs of codons with their associated amino acids presented to the neural network over around 7-40 training epochs, depending on the architecture and settings. We confirm that the wide generic capacities and modularity of deep neural networks allow them to be customized easily to learn the deciphering task of the genetic code efficiently.

Engineering bone-forming biohybrid sheets through the integration of melt electrowritten membranes and cartilaginous microspheroids.

Bone fractures are one of the most common traumatic large-organ injuries and although many fractures can heal on their own, 2-12% of fractures are slow healing or do not heal (non-unions). Autologous grafts are currently used for treatment of non-unions but are associated with limited healthy bone tissue. Tissue engineered cell-based products have promise for an alternative treatment method. It was previously demonstrated that cartilaginous microspheroids of periosteum-derived cells could be assembled into scaffold-free constructs and heal murine critically-sized long bone defects (non-unions). However, the handleability of such scaffold-free implants can be compromised when scaling-up. In this work, cartilaginous spheroids were combined with melt electrowritten (MEW) meshes to create an engineered cell-based implant, able to induce in vivo bone formation. MEW polycaprolactone meshes were tailored to contain pores (116 ± 28 µm) of a size that captured microspheroids (180 ± 15 µm). Periosteum-derived microspheroids pre-cultured for 4 days, were seeded on MEW meshes and gene expression analysis demonstrated up-regulation of chondrogenic (SOX9, COL2) and prehypertrophic (VEGF) gene markers after 14 days, creating a biohybrid sheet. When implanted subcutaneously (4 weeks), the biohybrid sheets mineralized (23 ± 3% MV/TV) and formed bone and bone marrow. Bone formation was also observed when implanted in a murine critically-sized long bone defect, though a high variation between samples was detected. The high versatility of this biofabrication approach lies in the possibility to tailor the scaffolds to shape and dimensions corresponding to the large bone defects and the individual patient using robust bone forming building blocks. These strategies are instrumental in the development of personalized regenerative therapies with predictive clinical outcomes. STATEMENT OF SIGNIFICANCE: Successful treatments for healing of large long bone defects are still limited and 2-12% of fractures do not heal properly. We combined a novel biofabrication technique: melt electrowriting (MEW), with robust biology: bone forming cartilaginous spheroids to create biohybrid sheets able to form bone upon implantation. MEW enabled the fabrication of scaffolds with micrometer-sized fibers in defined patterns which allowed the capturing of and merging with cartilaginous spheroids which had the potency to mature into bone via the developmental process of endochondral ossification. The present study contributes to the rapidly growing field of "Biofabrication with Spheroid and Organoid Materials'' and demonstrates design considerations that are of great importance for biofabrication of functional tissues through the assembly of cellular spheroids.

Curvature in Biological Systems: Its Quantification, Emergence, and Implications across the Scales.

Surface curvature both emerges from, and influences the behavior of, living objects at length scales ranging from cell membranes to single cells to tissues and organs. The relevance of surface curvature in biology is supported by numerous experimental and theoretical investigations in recent years. In this review, first, a brief introduction to the key ideas of surface curvature in the context of biological systems is given and the challenges that arise when measuring surface curvature are discussed. Giving an overview of the emergence of curvature in biological systems, its significance at different length scales becomes apparent. On the other hand, summarizing current findings also shows that both single cells and entire cell sheets, tissues or organisms respond to curvature by modulating their shape and their migration behavior. Finally, the interplay between the distribution of morphogens or micro-organisms and the emergence of curvature across length scales is addressed with examples demonstrating these key mechanistic principles of morphogenesis. Overall, this review highlights that curved interfaces are not merely a passive by-product of the chemical, biological, and mechanical processes but that curvature acts also as a signal that co-determines these processes.

An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis.

BACKGROUND: Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes, while in silico modeling can help unravel that complexity. In this study, we aim to develop a virtual articular chondrocyte to guide experiments in order to rationalize the identification of potential drug targets via screening of combination therapies through computational modeling and simulations. RESULTS: We developed a signal transduction network model using knowledge-based and data-driven (machine learning) modeling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions potentially affecting the hypertrophic switch. A selection of promising combinations was further tested in a murine cell line and primary human chondrocytes, which notably highlighted a previously unreported synergistic effect between the protein kinase A and the fibroblast growth factor receptor 1. CONCLUSIONS: Here, we provide a virtual articular chondrocyte in the form of a signal transduction interactive knowledge base and of an executable computational model. Our in silico-in vitro strategy opens new routes for developing osteoarthritis targeting therapies by refining the early stages of drug target discovery.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises.

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Toward A Regulatory Pathway for the Use of in Silico Trials in the CE Marking of Medical Devices.

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing.

Immuno-Modulatory Effects of Intervertebral Disc Cells.

Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.