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Osteoarthritis (OA) is the most common form of arthritis and a major source of pain and disability in the adult population. There is a significant unmet medical need for the development of effective pharmacological therapies for the treatment of OA. In addition to spontaneously occurring animal models of OA, many experimental animal models have been developed to provide insights into mechanisms of pathogenesis and progression. Many of these animal models are also being used in the drug development pipeline. Here, we provide an overview of commonly used and emerging preclinical small animal models of OA and highlight the strengths and limitations of small animal models in the context of translational drug development. There is limited information in the published literature regarding the technical reliability of these small animal models and their ability to accurately predict clinical drug development outcomes. The cost and complexity of the available models however is an important consideration for pharmaceutical companies, biotechnology startups, and contract research organizations wishing to incorporate preclinical models in target validation, discovery, and development pipelines. Further considerations relevant to industry include timelines, methods of induction, the key issue of reproducibility, and appropriate outcome measures needed to objectively assess outcomes of experimental therapeutics. Preclinical small animal models are indispensable tools that will shine some light on the pathogenesis of OA and its molecular endotypes in the context of drug development. This paper will focus on small animal models used in preclinical OA research. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Objectives The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden’s index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). Conclusion The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. Trial registration ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.

ObjectivesOsteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.MethodsBaseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.ResultsOA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (pConclusionsOA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.Trial registration numberThe study is registered under clinicaltrials.gov nr: NCT03883568.

ObjectivesOsteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.MethodData quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression.ResultsThree dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain.ConclusionsThis work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.Trial registration numberNCT03883568.

Cracking in massive concrete structures due to temperature-induced stresses is a problem almost as old as concrete itself. Thermal gradients in bridge elements have historically been ignored in the U.S. As element size increases for structural, traffic, or aesthetic reasons, thermal gradients and thermal cracking have become serious concerns for bridge engineers. Accurate prediction of thermal gradients requires models that characterize the adiabatic temperature rise of concrete. The most common model for this purpose is the Arrhenius equation, which is widely used to characterize the temperature dependency of reaction rate. The Arrhenius equation uses activation energy (Eₐ) to define the sensitivity of a particular reaction to temperature. For portland cement, this constant is computed using either isothermal calorimeter data or mortar cube compressive strength. There is a large disparity in literature as to the proper method to determine Eₐ. This study examined twenty different cementitious pastes. Eₐ was computed for four of these mixtures using different computational methods. This study suggests a systematic computational method for characterizing Eₐ for any cementitious system. This work lays the groundwork for more extensive studies of a wider variety of cementitious systems