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Low-grade inflammation and pathological endochondral ossification are key processes underlying the progression of osteoarthritis, the most prevalent joint disease worldwide. In this study, we employed a multi-faceted approach, integrating publicly available datasets, in silico analyses, in vitro experiments and in vivo models to identify new therapeutic candidates targeting these processes. Data mining of transcriptomic datasets identified EPHA2, a receptor tyrosine kinase associated with cancer, as being linked to both inflammation and endochondral ossification in osteoarthritis. A computational model of cellular signaling networks in chondrocytes predicted that in silico activation of EPHA2 in healthy chondrocytes increases inflammatory mediators and induces hypertrophic differentiation, a hallmark of endochondral ossification. We then evaluated the effect of EPHA2 inhibition using the tyrosine kinase inhibitor ALW-II-41-27 in cultured human chondrocytes from individuals with osteoarthritis, demonstrating significant reductions in both inflammation and hypertrophy. Additionally, systemic subcutaneous administration of ALW-II-41-27 in a mouse osteoarthritic model attenuated joint degeneration by reducing local inflammation and pathological endochondral ossification. Collectively, this study demonstrates a novel drug discovery pipeline that integrates computational, experimental, and animal models, paving the way for the development of disease-modifying treatments for osteoarthritis., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Gerjo van Osch reports financial support was provided by European Union Horizon 2020. Gerjo van Osch reports financial support was provided by Reumafonds ReumaNederland. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objectives: Calcium phosphate-based biomaterials (CaP) are the most widely used biomaterials to enhance bone regeneration in the treatment of alveolar bone deficiencies, cranio-maxillofacial and periodontal infrabony defects, with positive preclinical and clinical results reported. This systematic review aimed to assess the influence of the physicochemical properties of CaP biomaterials on the performance of bone regeneration in preclinical animal models., Methods: The PubMed, EMBASE and Web of Science databases were searched to retrieve the preclinical studies investigating physicochemical characteristics of CaP biomaterials. The studies were screened for inclusion based on intervention (physicochemical characterization and in vivo evaluation) and reported measurable outcomes., Results: A total of 1532 articles were retrieved and 58 studies were ultimately included in the systematic review. A wide range of physicochemical characteristics of CaP biomaterials was found to be assessed in the included studies. Despite a high degree of heterogeneity, the meta-analysis was performed on 39 studies and evidenced significant effects of biomaterial characteristics on their bone regeneration outcomes. The study specifically showed that macropore size, Ca/P ratio, and compressive strength exerted significant influence on the formation of newly regenerated bone. Moreover, factors such as particle size, Ca/P ratio, and surface area were found to impact bone-to-material contact during the regeneration process. In terms of biodegradability, the amount of residual graft was determined by macropore size, particle size, and compressive strength., Conclusion: The systematic review showed that the physicochemical characteristics of CaP biomaterials are highly determining for scaffold's performance, emphasizing its usefulness in designing the next generation of bone scaffolds to target higher rates of regeneration., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Liesbet Geris reports financial support was provided by 10.13039/501100000781European Research Council under the European Union's Horizon Europe programme/ERC Consolidator Grant No. 101088919. Liesbet Geris and France Lambert report financial support was provided by Walloon Region via the BIOWIN-BIOPTOS and Win2Wal-B2Bone projects. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiating event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and information, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advancement of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)