THU0698-HPR MORE EFFICIENT DMOAD TRIALS THROUGH INNOVATIVE SCREENING STRATEGIES
Authors: Thibault Helleputte, Anne-Christine Hick, Yves Henrotin
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> In randomized Clinical Trials (RCT), the number of patients to recruit for assessing effectiveness of disease-modifying OA drug (DMOAD) depends on the proportion of progressors in the population. The higher this proportion, the smaller the cohort to be recruited. In the general OA population, this proportion is quite low (10-30%) [1] requiring large cohorts, hence long and expensive trials. <h3>Objectives</h3> We study how predictive modeling based on early cartilage degradation biomarkers can be used to screen patients, make individual predictions about the likelihood of future progression, and recruit on this basis a progressor-enriched cohort. By doing so, DMOAD RCTs could achieve same or better success rates based on smaller cohorts and lower budgets, even taking into account the extra cost of this innovative screening. <h3>Methods</h3> Two biomarkers (Coll2-1 &amp; Coll2-1-NO2, Artialis, Belgium) have been measured at baseline on 182 OA patients from the placebo arm of a previous RCT. For this cohort, progression at 30M is defined as in [2]. We developed two predictive models of the progression of the patients over 30 months through regularized logistic regressions: one based on Coll2-1 only, the other on Coll2-1 and Coll2-1-NO2. Based on these models, we design a strategy to produce cohorts of patients with a higher rate of progressors than the natural proportion. To do so, we make hypotheses on the expected drug effect (from 30% to 50%), on the costs generated by a trial designed according to a regular strategy (i.e. not using baseline markers for patients inclusion/exclusion), and on the costs generated by this innovative screening strategy (a.o. nb patients screened, enrolled, costs of visits, imaging, biomarkers when applicable). Based on these hypotheses, we optimize sensitivity/specificity trade-offs of each model, so as to reduce the sample size required to prove the expected effect. <h3>Results</h3> When we apply this strategy to realistic scenarios, both models produce substantial cost reductions (up to 30%) even considering the extra cost of the biomarker-based screening of a higher number of candidate participants. They also lead to cohort size reductions: almost 40% less patients needed. As an example, we considered a natural progressor rate of 20%, an actual effect of 50% for the DMOAD to be tested, and a study consistsµing in a screening visit, 4 follow-up visits, 2 x-rays, 2 MRI, and a final visit. Hypotheses have been made about costs of each of these items, according to Belgian practice. In the standard case, all screened patients satisfying inclusion criteria are recruited. In the modified version, only patients for which inclusion criteria are met and the tuned biomarker-based model turns positive are enrolled. In the standard version of such a study, 554 patients have to be recruited, with a cost of 1,767,260€. Through the optimized strategy, 1190 patients are screened, but among them only 344 are recruited in the study, showing a progression rate of about 35% instead of 20%, for a total cost of 1,229,280€. <h3>Conclusion</h3> This work demonstrates the interest for biomarker-based OA-progressors cohort enrichment in DMOAD RCTs. It simulates the gains it could represent for the conduct of these RCTs. The cohorts used in this work (for biomarker measurement and predictive modeling) have a high proportion of women with very large BMI, with K&amp;L grade II-III. The results might thus be replicated on other cohorts. In order to achieve yet stronger cohort enrichment and cost reductions, additional markers and/or clinical factors could be considered for the models. <h3>References</h3> [1] One-year follow-up of Coll2-1, Coll2-1NO2 and myeloperoxydase serum levels in osteoarthritis patients after hip or knee replacement. Deberg M, Dubuc JE, Labasse A, Sanchez C, Quettier E, Bosseloir A, Crielaard JM, Henrotin Y. Ann Rheum Dis. 2008 Feb;67(2):168-74. [2] Ornetti, et al., 2009; OARSI-OMERACT <h3>Disclosure of Interests</h3> Thibault Helleputte Shareholder of: Founder of DNAlytics., Grant/research support from: The data analysis included in this work has been financially supported by Sanofi Genzyme., Anne-Christine Hick Employee of: Artialis SA, Yves Henrotin Shareholder of: Artialis SA