Concept Endpoints Informing Design Considerations for Confirmatory Clinical Trials in Osteoarthritis

Time to Total Knee Arthroplasty (TKA) Post Intra-Articular Injection

Background: Disease-modifying treatments are not currently developed to target the underlying causes of knee osteoarthritis (KOA). Corticosteroids (CS), hyaluronic acid (HA), and platelet-rich plasma (PRP) intra-articular (IA) injections are commonly used for patients that do not respond to non-pharmacological treatments, oral nonsteroidal anti-inflammatory, or pain medications to address solely KOA symptoms. Utilizing TKA as an endpoint in the KOA disease progression provides a basis to determine efficacy of this treatment pathway. The primary objective is to evaluate a large national database to determine the time between first injection and total knee arthroplasty in patients solely administered intra-articular IA, CS, and HA. Methods: A retrospective query was performed on a national, all-payer claims database (PearlDiver, Colorado Springs, CO, USA), a composite of over 160 million Health Insurance Portability and Accountability Act compliant orthopedic records across all states and territories of the United States spanning 2016 to 2022. The database was queried to produce three distinct cohorts for analysis (PRP, HA, and CS). A 4:1 case match was conducted to compare cohorts receiving a subsequent TKA. Kaplan-Meier survival analysis analyzed the TKA-free survival of patients within each group at 6 months and 1 to 4 years. The log-rank test was performed for comparisons between survival cohorts. Results: The PRP cohort had a total population of 3240 patients, of which 71 (2.2%) received a subsequent TKA. The corticosteroid cohort had a total population of 1,382,572, of which 81,271 (5.9%) received a subsequent TKA. The HA cohort had a total population of 164,000, of which 13,044 (8.0%) received a subsequent TKA. Due to the low population within the PRP group, this group was excluded from comparison. The mean time to TKA from first injection in the HA group was 377.8 days, while in the corticosteroid group it was 370.0 days. The proportions of TKA-free survival for CS and HA when compared at 4 years post-injection was similar between groups (p = 0.05). Discussion and Conclusion: Patients that received only IA-corticosteroids or IA-hyaluronic acid had a similar length of time between the first injection and the total knee arthroplasty associated with the injected joint. This evidence provides information for clinicians and patients alike when contemplating these non-surgical injection modalities for KOA. The similarity observed between these treatments supports the need for future research to determine whether there is any potential for reduction in healthcare costs for KOA treatment prior to TKA.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI

OBJECTIVE

While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals.

METHODS

This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above. Participants without OA at baseline (BL) (Kellgren-Lawrence (KL) ≤ 1) were followed and classified as incident cases (KL ≥ 2 during follow-up; n = 115) and as non-incident (KL ≤ 1 over 96-month; n = 391). Associations between the predictor-variables sex, age, BMI, race, clinical scoring systems, T2 relaxation times and Whole-Organ Magnetic Resonance Imaging-Score (WORMS) readings at BL and the preservation of healthy knees (KL ≤ 1) during a 96-month follow-up period were assessed using logistic regression models.

RESULTS

Obesity and presence of pain showed a significant inverse association with maintaining radiographically normal joints in patients aged 65 and above. T2 relaxation times of the lateral femur and tibia as well as the medial femur were also significantly associated with maintaining radiographically normal knee joints. Additionally, absence of lesions of the lateral meniscus and absence of cartilage lesions in the medial and patellofemoral compartments were significantly associated with maintaining healthy knee joints.

CONCLUSION

Overall, this study provides protective clinical parameters as well as quantitative and semi-quantitative MR-imaging parameters associated with maintaining radiographically normal knee joints in an older population over 8 years.

Prognostic value of B-score for predicting joint replacement in the context of osteoarthritis phenotypes: Data from the osteoarthritis initiative

Objective

Developing new therapies for knee osteoarthritis (KOA) requires improved prediction of disease progression. This study evaluated the prognostic value of clinical clusters and machine-learning derived quantitative 3D bone shape B-score for predicting total and partial knee replacement (KR).

Design

This retrospective study used longitudinal data from the Osteoarthritis Initiative. A previous study used patients' clinical profiles to delineate phenotypic clusters. For these clusters, the distribution of B-scores was assessed (employing Tukey's method). The value of both cluster allocation and B-score for KR-prediction was then evaluated using multivariable Cox regression models and Kaplan-Meier curves for time-to-event analyses. The impact of using B-score vs. cluster was evaluated using a likelihood ratio test for the multivariable Cox model; global performances were assessed by concordance statistics (Harrell's C-index) and time dependent receiver operating characteristic (ROC) curves.

Results

B-score differed significantly for the individual clinical clusters (p ​< ​0.001). Overall, 9.4% of participants had a KR over 9 years, with a shorter time to event in clusters with high B-score at baseline. Those clusters were characterized clinically by a high rate of comorbidities and potential signs of inflammation. Both phenotype and B-score independently predicted KR, with better prediction if combined (P ​< ​0.001). B-score added predictive value in groups with less pain and radiographic severity but limited physical activity.

Conclusions

B-scores correlated with phenotypes based on clinical patient profiles. B-score and phenotype independently predicted KR surgery, with higher predictive value if combined. This can be used for patient stratification in drug development and potentially risk prediction in clinical practice.

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective

Objective

Knee osteoarthritis (OA) is a disease of the whole joint involving multiple tissue types. MRI-based semi-quantitative (SQ) scoring of knee OA is a method to perform multi-tissue joint assessment and has been shown to be a valid and reliable way to measure structural multi-tissue involvement and progression of the disease. While recent work has described how SQ scoring may be used for clinical trial enrichment and disease phenotyping in OA, less guidance is available for how these parameters may be used to assess study outcomes.

Design

Here we present recommendations for summarizing disease progression within specific tissue types. We illustrate how various methods may be used to quantify longitudinal change using SQ scoring and review examples from the literature.

Results

Approaches to quantify longitudinal change across subregions include the count of number of subregions, delta-subregion, delta-sum, and maximum grade changes. Careful attention should be paid to features that may fluctuate, such as bone marrow lesions, or with certain interventions, for example pharmacologic interventions with anticipated cartilage anabolic effects. The statistical approach must align with the nature of the outcome.

Conclusions

SQ scoring presents a way to understand disease progression across the whole joint. As OA is increasingly recognized as a heterogeneous disease with different phenotypes a better understanding of longitudinal progression across tissue types may present an opportunity to match study outcome to patient phenotype or to treatment mechanism of action.

Unmet need in rheumatology: reports from the Advances in Targeted Therapies meeting, 2023

The Advances in Targeted Therapies meets annually, convening experts in the field of rheumatology to both provide scientific updates and identify existing scientific gaps within the field. To review the major unmet scientific needs in rheumatology. The 23rd annual Advances in Targeted Therapies meeting convened with more than 100 international basic scientists and clinical researchers in rheumatology, immunology, infectious diseases, epidemiology, molecular biology and other specialties relating to all aspects of immune-mediated inflammatory diseases. We held breakout sessions in five rheumatological disease-specific groups including: rheumatoid arthritis (RA), psoriatic arthritis (PsA), axial spondyloarthritis (axSpa), systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and vasculitis, and osteoarthritis (OA). In each group, experts were asked to identify and prioritise current unmet needs in clinical and translational research. An overarching theme across all disease states is the continued need for clinical trial design innovation with regard to therapeutics, endpoint and disease endotypes. Within RA, unmet needs comprise molecular classification of disease pathogenesis and activity, pre-/early RA strategies, more refined pain profiling and innovative trials designs to deliver on precision medicine. Continued scientific questions within PsA include evaluating the genetic, immunophenotypic, clinical signatures that predict development of PsA in patients with psoriasis, and the evaluation of combination therapies for difficult-to-treat disease. For axSpA, there continues to be the need to understand the role of interleukin-23 (IL-23) in pathogenesis and the genetic relationship of the IL-23-receptor polymorphism with other related systemic inflammatory diseases (eg, inflammatory bowel disease). A major unmet need in the OA field remains the need to develop the ability to reliably phenotype and stratify patients for inclusion in clinical trials. SLE experts identified a number of unmet needs within clinical trial design including the need for allowing endpoints that reflect pharmacodynamic/functional outcomes (eg, inhibition of type I interferon pathway activation; changes in urine biomarkers). Lastly, within SSc and vasculitis, there is a lack of biomarkers that predict response or disease progression, and that allow patients to be stratified for therapies. There remains a strong need to innovate clinical trial design, to identify systemic and tissue-level biomarkers that predict progression or response to therapy, endotype disease, and to continue developing therapies and therapeutic strategies for those with treatment-refractory disease. This document, based on expert consensus, should provide a roadmap for prioritising scientific endeavour in the field of rheumatology.

Pain-phenotyping in osteoarthritis: Current concepts, evidence, and considerations towards a comprehensive framework for assessment and treatment

Objectives

Pain as central symptom of osteoarthritis (OA) needs to be addressed as part of successful treatment. The assessment of pain as feature of disease or outcome in clinical practice and drug development remains a challenge due to its multidimensionality and the plethora of confounders. This article aims at providing insights into our understanding of OA pain-phenotypes and suggests a framework for systematic and comprehensive assessments.

Methods

This narrative review is based on a search of current literature for various combinations of the search terms "pain-phenotype" and "knee OA" and summarizes current knowledge on OA pain-phenotypes, putting OA pain and its assessment into perspective of current research efforts.

Results

Pain is a complex phenomenon, not necessarily associated with tissue damage. Various pain-phenotypes have been described in knee OA. Among those, a phenotype with high pain levels not necessarily matching structural changes and a phenotype with low pain levels and impact are relatively consistent. Further subgroups can be differentiated based on patient reported outcome measures, assessments of comorbidities, anxiety and depression, sleep, activity and objective measures such as quantitative sensory testing.

Conclusions

The complexity of both OA as disease and pain in OA prompt the definition of a set of variables that facilitate assessments comparable across studies to maximize our understanding of pain, as central concern for the patient.

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 ​+ ​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 ​+ ​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.

Biomarkers in osteoarthritis: current status and outlook - the FNIH Biomarkers Consortium PROGRESS OA study

Currently, no disease-modifying therapies are approved for osteoarthritis (OA) use. One obstacle to trial success in this field has been our existing endpoints' limited validity and responsiveness. To overcome this impasse, the Foundation for the NIH OA Biomarkers Consortium is focused on investigating biomarkers for a prognostic context of use for subsequent qualification through regulatory agencies. This narrative review describes this activity and the work underway, focusing on the PROGRESS OA study.

Biomarkers for osteoarthritis: Current status and future prospects

Osteoarthritis (OA) is the most common form of arthritis globally and a major cause of pain, physical disability, and loss of economic productivity, with currently no causal treatment available. This review article focuses on current research on OA biomarkers and the potential for using biomarkers in future clinical practice and clinical trials of investigational drugs. We discuss how biomarkers, specifically soluble ones, have a long path to go before reaching clinical standards of care. We also discuss how biomarkers can help in phenotyping and subtyping to achieve enhanced stratification and move toward better-designed clinical trials. We also describe how biomarkers can be used for molecular endotyping and for determining the clinical outcomes of investigational cell-based therapies. Biomarkers have the potential to be developed as surrogate end points in clinical trials and help private-public consortia and the biotechnology and pharmaceutical industries develop more effective and targeted personalized treatments and enhance clinical care for patients with OA.

Evaluation of S201086/GLPG1972, an ADAMTS-5 inhibitor, for the treatment of knee osteoarthritis in ROCCELLA: a phase 2 randomized clinical trial

OBJECTIVE

To evaluate the efficacy and safety of the anti-catabolic ADAMTS-5 inhibitor S201086/GLPG1972 for the treatment of symptomatic knee osteoarthritis.

DESIGN

ROCCELLA (NCT03595618) was a randomized, double-blind, placebo-controlled, dose-ranging, phase 2 trial in adults (aged 40-75 years) with knee osteoarthritis. Participants had moderate-to-severe pain in the target knee, Kellgren-Lawrence grade 2 or 3 and Osteoarthritis Research Society International joint space narrowing (grade 1 or 2). Participants were randomized 1:1:1:1 to once-daily oral S201086/GLPG1972 75, 150 or 300 mg, or placebo for 52 weeks. The primary endpoint was change from baseline to week 52 in central medial femorotibial compartment cartilage thickness (cMFTC) assessed quantitatively by magnetic resonance imaging. Secondary endpoints included change from baseline to week 52 in radiographic joint space width, Western Ontario and McMaster Universities Osteoarthritis Index total and subscores, and pain (visual analogue scale). Treatment-emergent adverse events (TEAEs) were also recorded.

RESULTS

Overall, 932 participants were enrolled. No significant differences in cMFTC cartilage loss were observed between placebo and S201086/GLPG1972 therapeutic groups: placebo vs 75 mg, P = 0.165; vs 150 mg, P = 0.939; vs 300 mg, P = 0.682. No significant differences in any of the secondary endpoints were observed between placebo and treatment groups. Similar proportions of participants across treatment groups experienced TEAEs.

CONCLUSIONS

Despite enrolment of participants who experienced substantial cartilage loss over 52 weeks, during the same time period, S201086/GLPG1972 did not significantly reduce rates of cartilage loss or modify symptoms in adults with symptomatic knee osteoarthritis.

Strategic application of imaging in DMOAD clinical trials: focus on eligibility, drug delivery, and semiquantitative assessment of structural progression

Despite decades of research efforts and multiple clinical trials aimed at discovering efficacious disease-modifying osteoarthritis (OA) drugs (DMOAD), we still do not have a drug that shows convincing scientific evidence to be approved as an effective DMOAD. It has been suggested these DMOAD clinical trials were in part unsuccessful since eligibility criteria and imaging-based outcome evaluation were solely based on conventional radiography. The OA research community has been aware of the limitations of conventional radiography being used as a primary imaging modality for eligibility and efficacy assessment in DMOAD trials. An imaging modality for DMOAD trials should be able to depict soft tissue and osseous pathologies that are relevant to OA disease progression and clinical manifestations of OA. Magnetic resonance imaging (MRI) fulfills these criteria and advances in technology and increasing knowledge regarding imaging outcomes likely should play a more prominent role in DMOAD clinical trials. In this perspective article, we will describe MRI-based tools and analytic methods that can be applied to DMOAD clinical trials with a particular emphasis on knee OA. MRI should be the modality of choice for eligibility screening and outcome assessment. Optimal MRI pulse sequences must be chosen to visualize specific features of OA.

Osteoarthritis, an old wine in a new bottle!

Osteoarthritis (OA) is the most common form of arthritis that has a major impact on patient morbidity and health care services. Despite its prevalence and impact, we do not have any effective management strategy to prevent or control their manifestations. Several decades of pharmacological development have failed to deliver a disease-modifying solution to OA. This editorial article outlines the lacunae in the research efforts of the past, the challenges that we are facing at present, and the exciting opportunities we have in the future for the management of OA. OA research has to be made more personalized concerning the phenotypic and endotypic disease variants. To begin with, robust disease classification criteria need to be defined for early OA, and biomarkers to detect such early diseases to aid in patient stratification. We also need to refine our clinical research design to make them more objective to meet the demands of the patient and the regulatory agencies. Embracing the current technologies such as artificial intelligence along with the use of genomic profiling from the omics platforms, the future of OA is more promising in developing appropriate management of OA.

Targeting Aggrecanases for Osteoarthritis Therapy: From Zinc Chelation to Exosite Inhibition

Osteoarthritis (OA) is the most common degenerative joint disease. In 1999, two members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family of metalloproteinases, ADAMTS4 and ADAMTS5, or aggrecanases, were identified as the enzymes responsible for aggrecan degradation in cartilage. The first aggrecanase inhibitors targeted the active site by chelation of the catalytic zinc ion. Due to the generally disappointing performance of zinc-chelating inhibitors in preclinical and clinical studies, inhibition strategies tried to move away from the active-site zinc in order to improve selectivity. Exosite inhibitors bind to proteoglycan-binding residues present on the aggrecanase ancillary domains (called exosites). While exosite inhibitors are generally more selective than zinc-chelating inhibitors, they are still far from fulfilling their potential, partly due to a lack of structural and functional data on aggrecanase exosites. Filling this gap will inform the design of novel potent, selective aggrecanase inhibitors.

Time to Total Knee Arthroplasty after Intra-Articular Hyaluronic Acid or Platelet-Rich Plasma Injections: A Systematic Literature Review and Meta-Analysis

Intra-articular (IA) hyaluronic acid (HA) and platelet-rich plasma (PRP) injections are increasingly being prescribed for knee osteoarthritis (KOA). However, failure of the medical treatment may result in total knee arthroplasty (TKA). We wondered if IA HA or PRP injections (intervention) may delay the time to TKA (outcome) among KOA patients (population), compared to KOA patients not receiving these injections (comparator). For this systematic literature review (SLR) and meta-analysis, we selected observational studies with at least one group of patients receiving IA HA or PRP and with TKA data available. The main outcome was time from the diagnosis of KOA to TKA. We included 25 articles in the SLR (2,824,401 patients) and four in the meta-analysis. The mean strengthening the reporting of observational studies in epidemiology (STROBE) score was 63%. For patients receiving versus not receiving HA injections, the delay between a declared diagnosis of KOA to TKA was increased by 9.8 months (95% CI (8.2-11.4)). As compared with standard of care, the effect size of HA injections for this outcome was 0.57 (95% CI (0.36-0.76)). Only one study described a median time from PRP injections to TKA of 4.1 years (range 0.3-14.7). IA HA injections were associated with increased time to TKA. Causality cannot be concluded because of missing confounder factors as comorbidities. Data were insufficient to conclude any effect of PRP injections on TKA delay.

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.

New Trends in Pharmacological Treatments for Osteoarthritis

Osteoarthritis (OA) is the leading cause of function loss and disability among the elderly, with significant burden on the individual and society. It is a severe disease for its high disability rates, morbidity, costs, and increased mortality. Multifactorial etiologies contribute to the occurrence and development of OA. The heterogeneous condition poses a challenge for the development of effective treatment for OA; however, emerging treatments are promising to bring benefits for OA management in the future. This narrative review will discuss recent developments of agents for the treatment of OA, including potential disease-modifying osteoarthritis drugs (DMOADs) and novel therapeutics for pain relief. This review will focus more on drugs that have been in clinical trials, as well as attractive drugs with potential applications in preclinical research. In the past few years, it has been realized that a complex interaction of multifactorial mechanisms is involved in the pathophysiology of OA. The authors believe there is no miracle therapeutic strategy fitting for all patients. OA phenotyping would be helpful for therapy selection. A variety of potential therapeutics targeting inflammation mechanisms, cellular senescence, cartilage metabolism, subchondral bone remodeling, and the peripheral nociceptive pathways are expected to reshape the landscape of OA treatment over the next few years. Precise randomized controlled trials (RCTs) are expected to identify the safety and efficacy of novel therapies targeting specific mechanisms in OA patients with specific phenotypes.