Joint longitudinal model development: application to exposure–response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab

Abstract

Exposure–response modeling is important to optimize dose and dosing regimen in clinical drug development. The joint modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript presents the results of joint modeling of continuous and ordered categorical endpoints in the latent variable IDR modeling framework through the sharing of model parameters, with an application to the exposure–response modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose–response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.