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.

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.

Turning Nature's own processes into design strategies for living bone implant biomanufacturing: a decade of Developmental Engineering.

A decade after the term developmental engineering (DE) was coined to indicate the use of developmental processes as blueprints for the design and development of engineered living implants, a myriad of proof-of-concept studies demonstrate the potential of this approach in small animal models. This review provides an overview of DE work, focusing on applications in bone regeneration. Enabling technologies allow to quantify the distance between in vitro processes and their developmental counterpart, as well as to design strategies to reduce that distance. By embedding Nature's robust mechanisms of action in engineered constructs, predictive large animal data and subsequent positive clinical outcomes can be gradually achieved. To this end, the development of next generation biofabrication technologies should provide the necessary scale and precision for robust living bone implant biomanufacturing.

Dissecting the effects of preconditioning with inflammatory cytokines and hypoxia on the angiogenic potential of mesenchymal stromal cell (MSC)-derived soluble proteins and extracellular vesicles (EVs).

Mesenchymal stromal cells (MSCs) are characterized by a regulatory phenotype and respond promptly to the environmental signals modulating their secretory activity. An appropriate preconditioning may induce MSCs to release secretomes with an enhanced regenerative potential. However, it fails to take into account that secretomes are composed by both soluble factors and extracellular vesicles (EVs), whose functions could be altered differently by the preconditioning approach. Here we demonstrate that the MSC secretome is strongly modulated by the simultaneous stimulation with hypoxia and pro-inflammatory cytokines, used to mimic the harsh environment present at the site of injury. We observed that the environmental variations strongly influenced the angiogenic potential of the different secretome fractions. Upon inflammation, the pro-angiogenic capacity of the soluble component of the MSC secretome was strongly inhibited, regardless of the oxygen level, while the EV-encapsulated component was not significantly affected by the inflammatory stimuli. These effects were accompanied by the modulation of the secreted proteins. On one hand, inflammation-activated MSCs release proteins mainly involved in the interaction with innate immune cells and in tissue remodeling/repair; on the other hand, when MSCs are not exposed to an inflamed environment, they respond to the different oxygen levels modulating the expression of proteins involved in the angiogenic process. The cargo content (in terms of miRNAs) of the corresponding EV fractions was less sensitive to the influence of the external stimuli. Our findings suggest that the therapeutic efficacy of MSC-based therapies could be enhanced by selecting the appropriate preconditioning approach and carefully discriminating its effects on the different secretome components.

Human pluripotent stem cell-derived cartilaginous organoids promote scaffold-free healing of critical size long bone defects.

BACKGROUND: Bones have a remarkable capacity to heal upon fracture. Yet, in large defects or compromised conditions healing processes become impaired, resulting in delayed or non-union. Current therapeutic approaches often utilize autologous or allogeneic bone grafts for bone augmentation. However, limited availability of these tissues and lack of predictive biological response result in limitations for clinical demands. Tissue engineering using viable cell-based implants is a strategic approach to address these unmet medical needs. METHODS: Herein, the in vitro and in vivo cartilage and bone tissue formation potencies of human pluripotent stem cells were investigated. The induced pluripotent stem cells were specified towards the mesodermal lineage and differentiated towards chondrocytes, which subsequently self-assembled into cartilaginous organoids. The tissue formation capacity of these organoids was then challenged in an ectopic and orthotopic bone formation model. RESULTS: The derived chondrocytes expressed similar levels of collagen type II as primary human articular chondrocytes and produced stable cartilage when implanted ectopically in vivo. Upon targeted promotion towards hypertrophy and priming with a proinflammatory mediator, the organoids mediated successful bridging of critical size long bone defects in immunocompromised mice. CONCLUSIONS: These results highlight the promise of induced pluripotent stem cell technology for the creation of functional cartilage tissue intermediates that can be explored for novel bone healing strategies.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine.

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine.

Discrete adipose-derived stem cell subpopulations may display differential functionality after in vitro expansion despite convergence to a common phenotype distribution.

BACKGROUND: Complex immunophenotypic repertoires defining discrete adipose-derived stem cell (ASC) subpopulations may hold a key toward identifying predictors of clinical utility. To this end, we sorted out of the freshly established ASCs four subpopulations (SPs) according to a specific pattern of co-expression of six surface markers, the CD34, CD73, CD90, CD105, CD146, and CD271, using polychromatic flow cytometry. METHOD: Using flow cytometry-associated cell sorting and analysis, gating parameters were set to select for a CD73+CD90+CD105+ phenotype plus one of the four following combinations, CD34-CD146-CD271- (SP1), CD34-CD146+CD271- (SP2), CD34+CD146+CD271- (SP3), and CD34-CD146+CD271+ (SP4). The SPs were expanded 700- to 1000-fold, and their surface repertoire, trilineage differentiation, and clonogenic potential, and the capacity to support wound healing were assayed. RESULTS: Upon culturing, the co-expression of major epitopes, the CD73, CD90, and CD105 was maintained, while regarding the minor markers, all SPs reverted to resemble the pre-sorted population with CD34-CD146-CD271- and CD34-CD146+CD271- representing the most prevalent combinations, followed by less frequent CD34+CD146-CD271- and CD34+CD146+CD271- variants. There was no difference in the efficiency of adipo-, osteo-, or chondrogenesis by cytochemistry and real-time RT-PCR or the CFU capacity between the individual SPs, however, the SP2CD73+90+105+34-146+271- outperformed others in terms of wound healing. CONCLUSIONS: Our study shows that ASCs upon culturing inherently maintain a stable distribution of immunophenotype variants, which may potentially disguise specific functional properties of particular downstream lines. Furthermore, the outlined approach suggests a paradigm whereby discrete subpopulations could be identified to provide for a therapeutically most relevant cell product.