Safety, Tolerability, and Pharmacodynamics of the ADAMTS-5 Nanobody M6495: Two Phase 1, Single-Center, Double-Blind, Randomized, Placebo-Controlled Studies in Healthy Subjects and Patients With Osteoarthritis.

OBJECTIVE: To assess the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of single and multiple injections of M6495, a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS-5)  nanobody, in healthy volunteers and patients with osteoarthritis. METHODS: Two randomized, placebo-controlled, double-blind studies were performed. Study 1 enrolled 54 healthy male volunteers who received one subcutaneous (s.c.) injection of M6495 (1-300 mg) or placebo (ratio 2:1), evaluating safety, PK, and PD as changes in the serum aggrecan fragment alanine-arginine-glycine-serine (ARGS). Study 2 enrolled 32 patients with osteoarthritis with Kellgren-Lawrence grades 2 to 4 and pain greater than or equal to 40 on the Western Ontario and McMaster Universities Arthritis Index pain subscale at screening and evaluated the safety, PK, and PD of three doses every two weeks (75-300 mg per dose) or six once-weekly M6495 s.c. doses (300 mg) or placebo (ratio 3:1) over 106 days' follow-up. RESULTS: M6495 in single and multiple doses of less than or equal to 300 mg s.c. weekly was well tolerated with no clinically significant changes in any safety parameter. Adverse events more frequently reported in the M6495 groups were mostly mild cases of injection site reactions, myalgia, and nausea, which resolved after treatment cessation. The elimination half-life of single s.c. doses of M6495 ranged from 79 to 267 hours. M6495 administration substantially reduced serum ARGS levels, indicative of target engagement and indicating disease-modifying potential of M6495. CONCLUSION: Treatment with M6495 in single and multiple doses up to and including 300 mg s.c. was found to be well tolerated and adequately safe for further clinical evaluation of potential disease-modifying effects.

From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.

INTRODUCTION: Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA. AREAS COVERED: We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies. EXPERT OPINION: As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA.

Association of biochemical markers with bone marrow lesion changes on imaging-data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

BACKGROUND: To assess the prognostic value of short-term change in biochemical markers as it relates to bone marrow lesions (BMLs) on MRI in knee osteoarthritis (OA) over 24 months and, furthermore, to assess the relationship between biochemical markers involved with tissue turnover and inflammation and BMLs on MRI. METHODS: Data from the Foundation for the National Institutes of Health OA Biomarkers Consortium within the Osteoarthritis Initiative (n = 600) was analyzed. BMLs were measured according to the MRI Osteoarthritis Knee Score (MOAKS) system (0-3), in 15 knee subregions. Serum and urinary biochemical markers assessed were as follows: serum C-terminal crosslinked telopeptide of type I collagen (CTX-I), serum crosslinked N-telopeptide of type I collagen (NTX-I), urinary CTX-Iα and CTX-Iß, urinary NTX-I, urinary C-terminal cross-linked telopeptide of type II collagen (CTX-II), serum matrix metalloproteinase (MMP)-degraded type I, II, and III collagen (C1M, C2M, C3M), serum high sensitivity propeptide of type IIb collagen (hsPRO-C2), and matrix metalloproteinase-generated neoepitope of C-reactive protein (CRPM). The association between change in biochemical markers over 12 months and BMLs over 24 months was examined using regression models adjusted for covariates. The relationship between C1M, C2M, C3M, hsPRO-C2, and CRPM and BMLs at baseline and over 24 months was examined. RESULTS: Increases in serum CTX-I and urinary CTX-Iß over 12 months were associated with increased odds of changes in the number of subregions affected by any BML at 24 months. Increase in hsPRO-C2 was associated with decreased odds of worsening in the number of subregions affected by any BML over 24 months. C1M and C3M were associated with BMLs affected at baseline. CONCLUSIONS: Short-term changes in serum CTX-I, hsPRO-C2, and urinary CTX-Iß hold the potential to be prognostic of BML progression on MRI. The association of C1M and C3M with baseline BMLs on MRI warrants further investigation.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P â€‹+ â€‹S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P â€‹+ â€‹S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions.

Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).

Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort.

BACKGROUND: The IMI-APPROACH cohort is an exploratory, 5-centre, 2-year prospective follow-up study of knee osteoarthritis (OA). Aim was to describe baseline multi-tissue semiquantitative MRI evaluation of index knees and to describe change for different MRI features based on number of subregion-approaches and change in maximum grades over a 24-month period. METHODS: MRIs were acquired using 1.5 T or 3 T MRI systems and assessed using the semi-quantitative MRI OA Knee Scoring (MOAKS) system. MRIs were read at baseline and 24-months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. In descriptive fashion, the frequencies of MRI features at baseline and change in these imaging biomarkers over time are presented for the entire sample in a subregional and maximum score approach for most features. Differences between knees without and with structural radiographic (R) OA are analyzed in addition. RESULTS: Two hundred eighty-nine participants had readable baseline MRI examinations. Mean age was 66.6 ± 7.1 years and participants had a mean BMI of 28.1 ± 5.3 kg/m2. The majority (55.3%) of included knees had radiographic OA. Any change in total cartilage MOAKS score was observed in 53.1% considering full-grade changes only, and in 73.9% including full-grade and within-grade changes. Any medial cartilage progression was seen in 23.9% and any lateral progression on 22.1%. While for the medial and lateral compartments numbers of subregions with improvement and worsening of BMLs were very similar, for the PFJ more improvement was observed compared to worsening (15.5% vs. 9.0%). Including within grade changes, the number of knees showing BML worsening increased from 42.2% to 55.6%. While for some features 24-months change was rare, frequency of change was much more common in knees with vs. without ROA (e.g. worsening of total MOAKS score cartilage in 68.4% of ROA knees vs. 36.7% of no-ROA knees, and 60.7% vs. 21.8% for an increase in maximum BML score per knee). CONCLUSIONS: A wide range of MRI-detected structural pathologies was present in the IMI-APPROACH cohort. Baseline prevalence and change of features was substantially more common in the ROA subgroup compared to the knees without ROA. TRIAL REGISTRATION: Clinicaltrials.gov identification: NCT03883568.

Animal Models of Osteoarthritis Part 1-Preclinical Small Animal Models: Challenges and Opportunities for Drug Development.

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.

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.

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.

Osteoarthritis endotype discovery via clustering of biochemical marker data.

OBJECTIVES: Osteoarthritis (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. METHOD: Data 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. RESULTS: Three 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. CONCLUSIONS: This 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 NUMBER: NCT03883568.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping.

OBJECTIVES: Osteoarthritis (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. METHODS: Baseline 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. RESULTS: OA 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 (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. CONCLUSIONS: OA 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.

Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort.

OBJECTIVES: To describe the relations between baseline clinical characteristics of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) participants and their predicted probabilities for knee osteoarthritis (OA) structural (S) progression and/or pain (P) progression. METHODS: Baseline clinical characteristics of the IMI-APPROACH participants were used for this study. Radiographs were evaluated according to Kellgren and Lawrence (K&L grade) and Knee Image Digital Analysis. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Numeric Rating Scale (NRS) were used to evaluate pain. Predicted progression scores for each individual were determined using machine learning models. Pearson correlation coefficients were used to evaluate correlations between scores for predicted progression and baseline characteristics. T-tests and χ2 tests were used to evaluate differences between participants with high versus low progression scores. RESULTS: Participants with high S progressions score were found to have statistically significantly less structural damage compared with participants with low S progression scores (minimum Joint Space Width, minJSW 3.56 mm vs 1.63 mm; p<0.001, K&L grade; p=0.028). Participants with high P progression scores had statistically significantly more pain compared with participants with low P progression scores (KOOS pain 51.71 vs 82.11; p<0.001, NRS pain 6.7 vs 2.4; p<0.001). CONCLUSIONS: The baseline minJSW of the IMI-APPROACH participants contradicts the idea that the (predicted) course of knee OA follows a pattern of inertia, where patients who have progressed previously are more likely to display further progression. In contrast, for pain progressors the pattern of inertia seems valid, since participants with high P score already have more pain at baseline compared with participants with a low P score.

Long-term structural and symptomatic effects of intra-articular sprifermin in patients with knee osteoarthritis: 5-year results from the FORWARD study.

OBJECTIVE: The FORWARD (FGF-18 Osteoarthritis Randomized Trial with Administration of Repeated Doses) trial assessed efficacy and safety of the potential disease-modifying osteoarthritis drug (DMOAD) sprifermin in patients with knee osteoarthritis. Here, we report 5-year efficacy and safety results. METHODS: Patients were randomised to intra-articular sprifermin 100 µg or 30 µg every 6 months (q6mo) or 12 months, or placebo, for 18 months. The primary analysis was at year 2, with follow-up at years 3, 4 and 5. Additional post hoc exploratory analyses were conducted in patients with baseline minimum radiographic joint space width 1.5-3.5 mm and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain 40-90, a subgroup at risk (SAR) of progression. RESULTS: 378 (69%) patients completed the 5-year follow-up. A significant dose-response in total femorotibial joint cartilage thickness with sprifermin (trend test, p<0.001) and a 0.05 mm mean difference with sprifermin 100 µg q6mo versus placebo (95% CI 0.00 to 0.10; p=0.015) were sustained to year 5. WOMAC pain scores improved ~50% from baseline in all groups. No patient in the 100 µg q6mo group had replacement of the treated knee. 96%-98% of patients receiving sprifermin and 98% placebo reported adverse events, most were mild or moderate and deemed unrelated to treatment. Adverse event-related study withdrawals were <10%. Differentiation in WOMAC pain between sprifermin 100 µg q6mo and placebo in the SAR (n=161) at year 3 was maintained to year 5 (-10.08; 95% CI -25.68 to 5.53). CONCLUSION: In the longest DMOAD trial reported to date, sprifermin maintained long-term structural modification of articular cartilage over 3.5 years post-treatment. Potential translation to clinical benefit was observed in the SAR. TRIAL REGISTRATION NUMBER: NCT01919164.

Making the patient voice heard in a research consortium: experiences from an EU project (IMI-APPROACH).

APPROACH is an EU-wide research consortium with the goal to identify different subgroups of knee osteoarthritis to enable future differential diagnosis and treatment. During a 2-year clinical study images, biomarkers and clinical data are collected from people living with knee osteoarthritis and data are analyzed to confirm patterns that can indicate such different subgroups. A Patient Council (PC) has been set up at project initiation and consists of five people from Norway, The Netherlands and UK. Initially, this group of individuals had to learn how to effectively work with each other and with the researchers. Today, the PC is a strong team that is fully integrated in the consortium and acknowledged by researchers as an important sounding board. The article describes this journey looking at formal processes of involvement - organizational structure, budget, meetings - and more informal processes such as building relationships and changing researcher perceptions. It describes how the PC helped improve the experience and engagement of study participants by providing input to the clinical protocol and ensuring effective communication (e.g. through direct interactions with participants and newsletters). Furthermore, the PC is helping with dissemination of results and project advocacy, and overall provides the patient perspective to researchers. Additionally, the authors experienced and describe the intangible benefits such as a shift in researcher attitudes and a sense of community and purpose for PC members. Importantly, learnings reported in this article also include the challenges, such as effective integration of the PC with researchers' work in the early phase of the project. TRIAL REGISTRATION: US National Library of Medicine, NCT03883568 , retrospectively registered 21 March 2019.

This article describes the activities and lessons learned from the involvement of a Patient Council in APPROACH, a 5-year European clinical research project focusing on osteoarthritis, the most common form of joint disease. The Patient Council is a group of five people from different EU countries who live with osteoarthritis. They use their knowledge of life with the disease and their own past experience as participants in clinical studies to help improve the experience of people who participate in the APPROACH clinical study. In addition, they provide the overall patient perspective to the researchers within the project.When the project started, the Patient Council was a group of individuals who didn't know each other. They had to find a way to work together with each other as a team, and with the researchers to ensure their involvement was integrated effectively into the project. The authors (current members of the Patient Council and other selected project members) describe in this article what was needed to successfully work together, the process of becoming fully engaged and involved and describe the impact that their activities have made on the clinical study during the project and beyond. They share their lessons learned with the goal to help other research projects to integrate the patient perspective effectively, and to encourage people living with a medical condition to share their experience with researchers through patient involvement activities.

Serum C-reactive protein metabolite (CRPM) is associated with incidence of contralateral knee osteoarthritis.

The heterogeneous nature of osteoarthritis (OA) and the need to subtype patients is widely accepted in the field. The biomarker CRPM, a metabolite of C-reactive protein (CRP), is released to the circulation during inflammation. Blood CRPM levels have shown to be associated with disease activity and response to treatment in rheumatoid arthritis (RA). We investigated the level of blood CRPM in OA compared to RA using data from two phase III knee OA and two RA studies (N = 1591). Moreover, the association between CRPM levels and radiographic progression was investigated. The mean CRPM levels were significantly lower in OA (8.5 [95% CI 8.3-8.8] ng/mL, n = 781) compared to the RA patients (12.8 [9.5-16.0] ng/mL, n = 60); however, a significant subset of OA patients (31%) had CRPM levels (≥ 9 ng/mL) comparable to RA. Furthermore, OA patients (n = 152) with CRPM levels ≥ 9 ng/mL were more likely to develop contra-lateral knee OA assessed by X-ray over a two-year follow-up period with an odds ratio of 2.2 [1.0-4.7]. These data suggest that CRPM is a blood-based biochemical marker for early identification OA patients with an inflammatory phenotype.

The effects of sprifermin on symptoms and structure in a subgroup at risk of progression in the FORWARD knee osteoarthritis trial.

OBJECTIVE: To assess pain outcomes and cartilage thickness change in a subgroup at risk (SAR) of further progression in the FORWARD trial of knee osteoarthritis patients treated with sprifermin. METHODS: Patients were randomised 1:1:1:1:1 to: sprifermin 100 µg every 6 months (q6mo), 100 µg q12mo, 30 µg q6mo, 30 µg q12mo, or placebo for 18 months. SAR was defined as baseline medial or lateral minimum joint-space width (mJSW) 1.5-3.5 mm and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score 40-90 units. Follow-up to 3 years was included in the analysis. Treatment benefit was explored by repeated measures, linear dose-effect trends by timepoint. RESULTS: The SAR comprised 161 (29%) of 549 patients. Mean difference (95% CI) in WOMAC pain at year 3 for sprifermin 100 µg q6mo vs placebo SAR was -8.75 (-22.42, 4.92) for SAR vs 0.97 (-6.22, 8.16) for the intent-to-treat population. SAR placebo patients lost more cartilage over 2 years than the modified ITT (mITT) placebo arm (mean change from baseline, mm [SD]: -0.05 [0.10] vs -0.02 [0.07]). Net total femorotibial joint thickness gain with sprifermin 100 µg q6mo (adjusted mean difference from placebo [95% CI] was similar in the SAR and in the mITT group: 0.06 [0.01, 0.11] vs 0.05 [0.03, 0.07]). CONCLUSIONS: Selection for low mJSW and moderate-to-high pain at baseline resulted in more rapid disease progression and demonstrated translation of structure modification (with maintained net benefit on total cartilage thickness) into symptomatic benefit. This subgroup may represent a target population for future trials. CLINICAL TRIAL REGISTRATION: NCT01919164.

The Anti-ADAMTS-5 Nanobody® M6495 Protects Cartilage Degradation Ex Vivo.

Osteoarthritis (OA) is associated with cartilage breakdown, brought about by ADAMTS-5 mediated aggrecan degradation followed by MMP-derived aggrecan and type II collagen degradation. We investigated a novel anti-ADAMTS-5 inhibiting Nanobody® (M6495) on cartilage turnover ex vivo. Bovine cartilage (BEX, n = 4), human osteoarthritic - (HEX, n = 8) and healthy-cartilage (hHEX, n = 1) explants and bovine synovium and cartilage were cultured up to 21 days in medium alone (w/o), with pro-inflammatory cytokines (oncostatin M (10 ng/mL) + TNFα (20 ng/mL) (O + T), IL-1α (10 ng/mL) or oncostatin M (50 ng/mL) + IL-1ß (10 ng/mL)) with or without M6495 (1000-0.46 nM). Cartilage turnover was assessed in conditioned medium by GAG (glycosaminoglycan) and biomarkers of ADAMTS-5 driven aggrecan degradation (huARGS and exAGNxI) and type II collagen degradation (C2M) and formation (PRO-C2). HuARGS, exAGNxI and GAG peaked within the first culture week in pro-inflammatory stimulated explants. C2M peaked from day 14 by O + T and day 21 in co-culture experiments. M6495 dose dependently decreased huARGS, exAGNxI and GAG after pro-inflammatory stimulation. In HEX C2M was dose-dependently reduced by M6495. M6495 showed no effect on PRO-C2. M6495 showed cartilage protective effects by dose-dependently inhibiting ADAMTS-5 mediated cartilage degradation and inhibiting overall cartilage deterioration in ex vivo cartilage cultures.

Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers.

PURPOSE: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development. PARTICIPANTS: APPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression. FINDINGS TO DATE: Selection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: p<0.001, for selected versus excluded patients, respectively. Based on the selection model, this cohort has a predicted higher chance of progression. FUTURE PLANS: Patients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy. TRIAL REGISTRATION NUMBER: NCT03883568.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.