• Defining trajectories of response in patients with psoriasis treated with biologic therapies

      Geifman, N.; orcid: 0000-0003-2956-6676; email: nophar.geifman@manchester.ac.uk; Azadbakht, N.; Zeng, J.; Wilkinson, T.; Dand, N.; Buchan, I.; Stocken, D.; Di Meglio, P.; orcid: 0000-0002-2066-7780; Warren, R.B.; Barker, J.N.; et al. (2021-06-04)
      Summary: Background: The effectiveness and cost‐effectiveness of biologic therapies for psoriasis are significantly compromised by variable treatment responses. Thus, more precise management of psoriasis is needed. Objectives: To identify subgroups of patients with psoriasis treated with biologic therapies, based on changes in their disease activity over time, that may better inform patient management. Methods: We applied latent class mixed modelling to identify trajectory‐based patient subgroups from longitudinal, routine clinical data on disease severity, as measured by the Psoriasis Area and Severity Index (PASI), from 3546 patients in the British Association of Dermatologists Biologics and Immunomodulators Register, as well as in an independent cohort of 2889 patients pooled across four clinical trials. Results: We discovered four discrete classes of global response trajectories, each characterized in terms of time to response, size of effect and relapse. Each class was associated with differing clinical characteristics, e.g. body mass index, baseline PASI and prevalence of different manifestations. The results were verified in a second cohort of clinical trial participants, where similar trajectories following the initiation of biologic therapy were identified. Further, we found differential associations of the genetic marker HLA‐C*06:02 between our registry‐identified trajectories. Conclusions: These subgroups, defined by change in disease over time, may be indicative of distinct endotypes driven by different biological mechanisms and may help inform the management of patients with psoriasis. Future work will aim to further delineate these mechanisms by extensively characterizing the subgroups with additional molecular and pharmacological data.