S62798, a potent TAFIa inhibitor, accelerates endogenous fibrinolysis in a murine model of pulmonary thromboembolism.

Enhancement of fibrinolysis constitutes a promising approach to treat thrombotic diseases. Venous thrombosis and thromboembolism risks are associated with increased plasma levels of TAFI (Thrombin Activatable Fibrinolysis Inhibitor) as well as its active form TAFIa. A new TAFIa inhibitor, namely S62798 has been identified. Its ability to enhance fibrinolysis was investigated both in vitro and in vivo in a mouse model of pulmonary thromboembolism, as well as its effect on bleeding. S62798 is a highly selective human, mouse and rat TAFIa inhibitor (IC50 = 11; 270; 178 nmol/L, respectively). It accelerates lysis of a human clot in vitro, evaluated by thromboelastometry (EC50 = 27 nmol/L). In a rat tail bleeding model, no effect of S62798 treatment was observed up to 20 mg/kg. Enhancement of endogenous fibrinolysis by S62798 was investigated in a mouse model of Tissue Factor-induced pulmonary thromboembolism. Intravenous administration of S62798 decreased pulmonary fibrin clots with a minimal effective dose of 0.03 mg/kg. Finally, effect of S62798 in combination with heparin was evaluated. When treatment of heparin was done in a curative setting, no effect was observed whereas a significantly decreased pulmonary fibrin deposition was observed in response to S62798 alone or in combination with heparin. This study demonstrates that S62798 is a potent TAFIa inhibitor with minimal risk of bleeding. In vivo, curative S62798 intravenous treatment, alone or associated with heparin, accelerated clot lysis by potentiating endogenous fibrinolysis and thus decreased pulmonary fibrin clots. S62798 is expected to be a therapeutic option for pulmonary embolism patients on top of anticoagulants.

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.

Novel Approach to Estimate Osteoarthritis Progression: Use of the Reliable Change Index in the Evaluation of Joint Space Loss.

OBJECTIVE: Osteoarthritis-related changes in joint space measurements over time are small and sensitive to measurement error. The Reliable Change Index (RCI) determines whether the magnitude of change observed in an individual can be attributed to true change. This study aimed to examine the RCI as a novel approach to estimating osteoarthritis progression. METHODS: Data were from 167 men and 392 women with knee osteoarthritis (diagnosed using the American College of Rheumatology criteria) randomized to the placebo arm of the 3-year Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA) and assessed annually. The RCI was used to determine whether the magnitude of change in joint space width (JSW) on radiographs between study years was likely to be true or due to measurement error. RESULTS: Between consecutive years, 57-69% of participants had an apparent decrease (change <0) in JSW, while 31-43% of participants had annual changes indicating improvement in JSW. The RCI identified JSW decreases in only 6.0% of patients between baseline and year 1, and in 4.5% of patients between the remaining study years. The apparent increases in JSW were almost eliminated between baseline and year 1, and between years 1 and 2 only 1.3% of patients had a significant increase, dropping to 0.9% between years 2 and 3. CONCLUSION: The RCI provides a method to identify change in JSW, removing many apparent changes that are likely to be due to measurement error. This method appears to be useful for assessing change in JSW from radiographs in clinical and research settings.

Statin use and knee osteoarthritis progression: Results from a post-hoc analysis of the SEKOIA trial.

OBJECTIVE: Epidemiological and experimental studies have suggested that lipid disorders might be involved in the pathophysiology of knee osteoarthritis (OA). Studies assessing the effect of statins on knee OA progression have shown conflicting results. We investigated the impact of statin use on radiological progression in patients with radiological and symptomatic knee OA. METHODS: In total, 336 patients from the placebo arm of SEKOIA trial completed the 3-year follow-up and were included in this post-hoc analysis. Statin use was recorded at baseline interview. Minimal medial tibiofemoral joint space was measured on plain radiographs by an automated method at baseline and then annually. Radiologic progression was defined as joint space narrowing≥0.5mm over 3 years. RESULTS: Overall, 71 patients were statin users (21.1%). They had a higher BMI (31.1±5.3 vs. 29.3±5.2kg/m2, P=0.008), a higher sum of metabolic factors (≥3 factors: 43.7% vs 7.2%; P for trend<0.001) and a higher rate of radiological progression (49.3% vs. 32.1%, P=0.007) as compared to statin non-users. The significant association between radiological progression and statin use was independent of age, gender, WOMAC global score, disease duration, baseline joint space width, hypertension, type 2 diabetes, obesity (BMI>30kg/m2) and cardiovascular diseases [relative risk 1.49 (95% CI: 1.10-2.02), P=0.010]. CONCLUSION: Among patients with knee OA, statin use was associated with radiological worsening over 3 years, regardless of other potential confounding factors (obesity, type 2 diabetes, hypertension, disease duration, symptom intensity and radiological severity).

Can We Identify Patients with High Risk of Osteoarthritis Progression Who Will Respond to Treatment? A Focus on Biomarkers and Frailty.

Osteoarthritis (OA), a disease affecting different patient phenotypes, appears as an optimal candidate for personalized healthcare. The aim of the discussions of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group was to explore the value of markers of different sources in defining different phenotypes of patients with OA. The ESCEO organized a series of meetings to explore the possibility of identifying patients who would most benefit from treatment for OA, on the basis of recent data and expert opinion. In the first meeting, patient phenotypes were identified according to the number of affected joints, biomechanical factors, and the presence of lesions in the subchondral bone. In the second meeting, summarized in the present article, the working group explored other markers involved in OA. Profiles of patients may be defined according to their level of pain, functional limitation, and presence of coexistent chronic conditions including frailty status. A considerable amount of data suggests that magnetic resonance imaging may also assist in delineating different phenotypes of patients with OA. Among multiple biochemical biomarkers identified, none is sufficiently validated and recognized to identify patients who should be treated. Considerable efforts are also being made to identify genetic and epigenetic factors involved in OA, but results are still limited. The many potential biomarkers that could be used as potential stratifiers are promising, but more research is needed to characterize and qualify the existing biomarkers and to identify new candidates.