Response Types and Factors Associated with Response Types to Biologic Therapies in Patients with Moderate-to-Severe Plaque Psoriasis from Two Randomized Clinical Trials

In these post hoc analyses of data from two multicenter randomized controlled clinical trials, patients in favorable SRTs and LRTs tended to be younger, have lower baseline BMI and aPASI scores, and have shorter duration of psoriasis than those in less favorable RTs. In multivariable logistic regression analyses, lower week 16 aPASI scores were consistently associated with a higher chance of achieving the most favorable RTs (SRT1 in ECLIPSE and VOYAGE 1, and LRT1 in the VOYAGE 1 OLE). In VOYAGE 1, but not ECLIPSE, shorter disease duration and never having smoked (versus being a former smoker) were also associated with a higher probability of achieving SRT1; no baseline demographics were associated with achieving LRT1.

As expected, on the basis of the efficacy results from the ECLIPSE and VOYAGE 1 studies, more patients receiving guselkumab were classified into favorable SRTs versus secukinumab or adalimumab. At the end of the VOYAGE 1 OLE, most patients treated with guselkumab had low disease activity (LRT2). These analyses identified differences in characteristics that help to explain these results. For example, short-term treatment with adalimumab resulted in categorization into less favorable SRTs among current smokers versus patients who had never smoked, whereas smoking status did not appear to impact SRT classification for patients receiving secukinumab or guselkumab. Higher baseline BMI appeared to result in categorization into less favorable SRTs among patients receiving secukinumab or adalimumab, but no clear pattern was observed with guselkumab. Validation studies are required to confirm the association between variables identified and the RTs defined here and to support use of these RTs for the guidance of treatment in clinical practice.

These results contribute to the ongoing effort to personalize treatment of psoriasis, which requires an understanding of the factors that may affect response to different treatments [11, 15]. The RTs used were author-defined and based on clinical experience, but generally appear consistent with wider interpretation of aPASI scores. For example, in an analysis of data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR), including more than 13,000 patients with psoriasis, achieving aPASI score ≤ 4 was concordant with PASI 75 response in 88% of cases [16]. Additionally, in a population-based cohort study including 2034 patients with moderate-to-severe psoriasis and 3 years’ sustained treatment with adalimumab, etanercept, infliximab, certolizumab pegol, ustekinumab, secukinumab, ixekizumab, or brodalumab, predictive prognostic models were used to identify optimal biologic treatment [17]. Modeling predicted which cytokine targets were associated with a successful treatment outcome with 59.2–63.6% accuracy overall and 93.9–95.9% accuracy for the two most successful treatments. Therefore, predicted response to treatment could potentially be used to guide a holistic approach for managing patients with psoriasis, which may allow physicians to optimize the balance of treatment benefit and the risk of side effects on an individual basis [11, 15].

The association of smoking with the onset of psoriasis and disease severity is well established [18‐20]. Data from real-world registries [7‐9], retrospective analyses [10], and a recent meta-analysis [21] have shown that smokers have a worse response to biologic therapy than non-smokers. However, biologics have more commonly been studied either by mechanism of action or as an overall class, but differences between individual biologics have not been established. In a recent pooled analysis evaluating the effect of baseline factors on outcomes with secukinumab, there were more non-smokers in groups with better responses to treatment; a similar, but weaker, trend was also seen in patients treated with etanercept [22]. The value of smoking cessation for reducing psoriasis severity remains unclear, and its impact on treatment outcomes in patients with psoriasis has yet to be explored [21]. Nonetheless, smoking cessation programs should be considered for patients with psoriasis [20].

The less favorable outcomes observed in this study in patients with a high BMI treated with secukinumab and adalimumab are also consistent with observations in real-world and retrospective analyses [7‐10]. Additionally, in an analysis of pooled phase III data from patients with psoriasis (FIXTURE, ERASURE, and CLEAR trials), lower BMI was associated with a better response to treatment with secukinumab [22]. In the prospective GUIDE study, which analyzed factors leading to complete skin clearance following guselkumab treatment, each kg/m² increase in BMI negatively affected the likelihood of achieving clearance [23]. However, consistent with real-world studies [24, 25], our analyses found that the effect of BMI on efficacy may be less with guselkumab treatment compared with other treatments. Weight loss has been shown to improve treatment outcomes in patients with psoriasis [26, 27], and weight loss interventions should also be considered when treating patients.

Finally, prior biologic therapy may be associated with a poor short-term response to guselkumab and secukinumab. This is consistent with other studies showing that prior systemic or biologic therapy can negatively affect the efficacy of TNF inhibitors [28, 29], ustekinumab [28, 30], secukinumab [22], and guselkumab [25].

The primary strength of this analysis is that it is based on robust datasets from two randomized clinical trials that included a total of almost 2000 participants. Our analysis therefore includes comparison of three different treatments. In addition, these datasets included long-term follow-up, to a maximum of 252 weeks, allowing differentiation of short-term and long-term response. It is also a strength that the RT definitions were based on thresholds deemed clinically relevant by psoriasis experts. In contrast, a limitation of the analysis is that the RT definitions did not consider patient views. A recent consensus study, including patients with psoriasis, dermatologists, and nurses, described freedom from disease as multifaceted, with five domains identified as management of clinical symptoms, considerations beyond the skin, treatment burden, quality of care, and well-being [31]. Hence, this analysis may have missed certain patient-relevant considerations by focusing on clinical responses, evaluating aPASI ± DLQI alone. Other limitations include the post hoc nature of these analyses, use of descriptive statistics, and the lack of statistical analyses comparing adjustment for baseline characteristics across different RTs. Long-term data were only available for guselkumab, with no comparator. Additionally, with the exception of obesity, baseline cardiometabolic parameters were not evaluated; diabetes, metabolic syndrome, and hypertension have all been shown to be associated with response to biologics in other studies [22]. While similar results were found with RTs defined on the basis of aPASI score alone or aPASI and DLQI scores, patient-reported outcomes are important measures of treatment success [31]. Covariates using logistic regression were derived on the basis of a relatively small number of patients, and correlation/agreement within covariates was not evaluated. Further, aPASI score was used to define RTs and was also a covariate and therefore, as expected, was associated with SRT1/LRT1. Most covariates were measured at baseline with the response variable based on a time interval; it is possible that a given characteristic could have changed (e.g., smoking status) during the time interval used to define the RT. Lastly, the performance of the models was never compared.