(M-066) Model-informed Assessment of the Impact of Relaxing the high-sensitivity C-reactive protein Inclusion Criteria on Patient Enrollment and Efficacy Signal in Rheumatoid Arthritis Clinical Trial Design

Jonathan Sidi – Sanofi; Xiaomei Liao – Sanofi; Anna Fishbein – Sanofi; Zhaoling Meng – Sanofi

Objectives: hsCRP (high-sensitivity) C-reactive protein (CRP) is a commonly performed laboratory test used to predict the clinical course and progression of structural changes in psoriatic arthritis (PsA) and rheumatoid arthritis (RA). The American College of Rheumatology (ACR) response influences treat-to-target algorithms and third-party payer criteria for reimbursing treatment costs. It is included in the most common efficacy outcomes for PsA and RA recommended by the FDA and EMA.

Given the importance of hsCRP as a biomarker of disease severity, it is a commonly used screening criteria for inclusion in RA clinical trials with a wide range of cut off values, most commonly 3-8 mg/L. Higher cutoff thresholds can make recruitment more challenging but may also make treatment responses easier to detect. However, there remains a gap in knowledge regarding the optimal hsCRP cutoff for inclusion in RA clinical trials.

In this simulation study we aim to identify an optimal hsCRP screening cut off for inclusion in RA clinical trials, while controlling for the statistical power of a superiority efficacy outcome defined as the placebo adjusted response rate of ACR20, which indicates 20% improvement of the ACR response criteria.

Methods: The sensitivity of the efficacy outcome as a function of lowering the inclusion hsCRP threshold was evaluated through in-silico clinical trial simulations (ICTS) to quantify the clinical risk-benefit trade-off of enrolling subjects from populations with lower levels of hsCRP. Leveraging literature [1] and historical placebo-controlled trials (15 studies), we characterized the data generating process of hsCRP at screening and derive a functional transformation to predict baseline hsCRP levels from screening levels. Historical trial summary outcomes were used to construct assumptions for treatment effect impacts. Using these characterizations, simulation scenarios were designed to evaluate the uncertainty, effect, and power of the target endpoint due to patient inclusion threshold hsCRP levels.

Results: At the pre-defined treatment effect levels and Go/NoGo decision making thresholds, there is low risk expanding the screening criteria from hsCRP≥6 to hsCRP≥4 without lowering the probability of success of the endpoint of interest.

Conclusions: The application of ICTS to systematically quantify the impact of key biomarkers on the inclusion criteria of patient enrollment and efficacy signal in RA provides a model informed mechanism to impact clinical trial design and decision making. The broader impact can be applied to other therapeutic areas that depend on biomarkers with well-defined data generating processes.

Citations: [1] Houttekiet, Charlotte et al. “Systematic review of the use of CRP in clinical trials for psoriatic arthritis: a concern for clinical practice?” RMD open vol. 8,1 (2022): e001756. doi:10.1136/rmdopen-2021-001756

Keywords: In-silico clinical trials, Trial design optimization, Simulation-based decision making