Defining Trajectories of Response in Patients With Psoriasis Treated With Biologic Therapies

N. Geifman; N. Azadbakht; J. Zeng; T. Wilkinson; N. Dand; I. Buchan; D. Stocken; P. Di Meglio; R.B. Warren; J.N. Barker; N.J. Reynolds; M.R. Barnes; C.H. Smith; C.E.M. Griffiths; N. Peek


The British Journal of Dermatology. 2021;185(4):825-835. 

In This Article

Abstract and Introduction


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.


Biologic therapies have revolutionized outcomes for people with severe psoriasis. However, these drugs are expensive and while many patients respond well to treatment there are subgroups who are considered 'primary failures' or secondary (responding initially but then losing response) failures, or who suffer adverse effects.[1,2] A recent network meta-analysis of 41 randomized controlled trials indicated significant heterogeneity in response between biologics for psoriasis with respect to effectiveness and tolerability;[3] however, traditional methods used to understand this heterogeneity in response have largely been uninformative.[4–6]

Relatively little attention has been paid to the trajectories of disease progression and treatment response, which may differ greatly between patients. For example, the examination of response trajectories to clozapine in a secondary analysis of pivotal trials has supported the use of treatment-response trajectories to subtype patients with schizophrenia;[7] and a simple post hoc analysis of methotrexate trial data hints at subtypes of response in psoriasis with the description of 'early' and 'late' responders.[8]

With the vision of the Psoriasis Stratification to Optimise Relevant Therapy (PSORT) consortium being to further personalized medicine in psoriasis by gaining a better understanding of the global patterns of disease,[9] we took a data-driven approach with an unsupervised statistical learning method to identify latent classes, or subgroups, of patients with psoriasis that display similar patterns of disease severity over time (i.e. trajectories) following biologic treatment. Latent class mixed modelling (LCMM) has previously been successful in the identification of patterns of predisease obesity in type 2 diabetes.[10] Here, we applied latent class trajectory analysis to analyse a large-scale, longitudinal, patient-level, real-world, cohort – the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) – to test (i) whether we could identify universal subgroups of patients with psoriasis with distinct patterns of change in disease following the initiation of biologic therapies; (ii) assess baseline characteristics and differences between identified subgroups; (iii) identify differential risk of treatment failure that is associated with the identified trajectory subgroups; and (iv) determine the association of any identified subgroups with a known genetic marker (HLA-C*06:02). We validated this approach by identifying similar trajectory-based subgroups in a second, independent cohort of patients with psoriasis treated with biologic therapies in clinical trials.