Investigation of plaque psoriasis relapse after secukinumab withdrawal in patients from two phase III studies

Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study

BACKGROUND

Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.

OBJECTIVE

To develop and validate a personalized prediction model for psoriasis relapse following the discontinuation of biologics.

METHODS

This study enrolled patients who achieved remission following biologic therapy. Relapse predictors were identified using the Boruta algorithm combined with multivariate Cox regression. A nomogram and an online calculator were created to aid in the visualization and computation of outcomes. The model's performance was thoroughly assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), C-statistics, calibration plots, and Decision Curve Analysis (DCA).

RESULTS

The study included 597 patients, with 534 in the derivation cohort and 63 in the validation cohort. Anxiety, disease duration, prior biologic treatments, treatment duration, time to achieve PASI 75, and maximum PASI response were identified as influential factors for relapse and were incorporated into the model. Both internal and external evaluations indicate that the model exhibits good predictive accuracy.

CONCLUSION

A multivariate model leveraging standard clinical data can relatively accurately predict the risk of psoriasis relapse post-biologic discontinuation, guiding personalized treatment strategies.

Disease Modification in Psoriasis: Future Prospects for Long-Term Remission

A subset of patients with moderate-to-severe psoriasis show long-term remission after drug withdrawal lasting well beyond several half-life times of the drug, particularly following effective treatment with modern biologics such as interleukin-23 inhibitors. Furthermore, evidence suggests that the development of comorbidities, including psoriatic arthritis, a key comorbidity causing irreversible damage, can be prevented or delayed in a subgroup of patients with psoriasis receiving these therapies. This implies that psoriasis treatments may alter the underlying disease mechanisms in some individuals, extending beyond their direct pharmacological effects. However, this concept of disease modification remains controversial, as predicting the natural clinical course of an individual patient with psoriasis is challenging, and typically, no permanent clinically detectable changes occur in psoriatic skin inflammation. This article aims to provide an overview of the current evidence on disease modification in psoriasis and discusses clinical and molecular markers that could be used to predict or monitor disease modification in psoriasis.

Association of disease duration and PASI response rates at week 12 in patients with moderate-to-severe plaque psoriasis receiving biologics in the real-world psoriasis study of health outcomes (PSoHO)

PURPOSE

Currently, in the treatment of moderate-to-severe psoriasis (PsO) there is a lack of evidence demonstrating optimal biologic treatment response with respect to disease duration. The aim of this post-hoc analysis, using real world data from the Psoriasis Study of Health Outcomes (PSoHO), is to provide evidence if early intervention with biologics is associated with better treatment outcomes and if there is any difference among drug classes or individual biologics.

MATERIALS AND METHODS

For this post-hoc analysis patients were categorised into two subgroups according to shorter (≤2 years) or longer (>2 years) disease duration. Analysis was performed on anti-interleukin (IL)-17A cohort vs other biologics cohort, anti-IL-17A vs other drug classes, and pairwise comparisons of ixekizumab vs individual biologics, provided that the statistical models converged. Analysis investigated the association of disease duration with the proportion of patients achieving 100% improvement in Psoriasis Area Severity Index score (PASI 100) at week 12. Adjusted comparative analyses, reported as odds ratio (OR), were performed using Frequentist Model Averaging (FMA) for each cohort or treatments within each subcategory of the subgroups.

RESULTS

At week 12, anti-IL-17A and other biologics cohorts displayed minimal differences in numerical response rate for PASI 100 with respect to disease duration. The anti-IL-17A cohort showed a higher numerical PASI 100 response rate compared to the other biologic cohort irrespective of disease duration (≤2 years: 36.7% vs 21.8%; >2 years: 35.8% vs 21.9%).

CONCLUSION

Overall, the results do not clearly indicate that treating patients early is critical in achieving optimal patient outcomes. Furthermore, patients treated with ixekizumab show numerically higher response rates relative to other individual biologics irrespective of disease duration.

Development and external validation of a prediction model for the risk of relapse in psoriasis after discontinuation of biologics

Background

Some patients with psoriasis experience relapses shortly after discontinuation of biologics. However, there is a lack of risk prediction tools to identify those at high risk of relapse.

Objective

To develop and validate a risk prediction model for psoriasis relapse after biologics discontinuation.

Methods

Publications from PubMed, EMBASE, Medline, and the Cochrane Library were systematically searched and meta-analyses were conducted to identify risk factors for psoriasis relapse after biologics discontinuation. Statistically significant risk factors were identified and used to create a risk assessment model weighted by the impact of each factor. The model was externally validated using a cohort of 416 Chinese psoriasis patients.

Results

Eight studies (N = 2066) were included in the meta-analysis. Body mass index (BMI), smoking, disease duration, comorbid psoriatic arthritis (PsA), remission speed and extent during treatment, history of biologic therapy, and therapy duration were identified as correlates of relapse in the meta-analysis and were incorporated into the prediction model. The median age of the 416 patients in the validation cohort was 41.5 (IQR 32, 53) years, with 63% male, and a baseline PASI score of 15.4 (IQR 10.5, 21). It was verified that the area under the curve (AUC) of the prediction model was 0.796 (95% CI, 0.753-0.839), with an optimal cut-off value of 11.25 points, sensitivity of 65.1%, and specificity of 82.2%.

Conclusion

Multivariate models using available clinical parameters can predict relapse risk in psoriasis patients after biologics discontinuation. Early individual identification of patients at risk of relapse, and screening of candidate cohorts for long-term treatment or dose reduction may benefit both patients and physicians.

Comparison of Drug-Free Remission after the End of Phase III Trials of Three Different Anti-IL-23 Inhibitors in Psoriasis

INTRODUCTION

Knowing the remission duration after biologics discontinuation in patients with psoriasis is important, especially when disease relapse is defined as the restart of systemic agents, because it also reflects the real-world clinical practice when topical treatment alone is not adequate for disease control, and a systemic treatment, including biologic, is needed. Biologics are currently indicated for patients with psoriasis who are candidates for systemic treatments.

METHODS

We included 42 patients who were followed up with regularly after the end of risankizumab, guselkumab and mirikizumab trials and investigated the drug-free remission (DFR). A Kaplan-Meier survival analysis and Cox regression model were employed to identify the possible risk factors for relapse.

RESULTS

Overall, 38/42 (90.5%) patients experienced relapses after discontinuing trial biologics during the follow-up period of at least 96 weeks and up to 227 weeks. In all patients with relapse, the median DFR was 104 days. Kaplan-Meier survival analysis revealed a significant 1-year drug-free survival (DFS) difference between risankizumab (Z) and guselkumab (T) + mirikizumab (M) (p = 0.0462). A difference in DFS curves was noted when patients were categorized by disease duration > or ≤ 2 years (p = 0.1577) and maintenance of a psoriasis area and severity index score (PASI) of 90 at the end of trials (p = 0.1177). Univariate Cox regression model identified that age [hazard ratio (HR) = 1.030 (1.000-1.060), p = 0.0467] and disease duration [HR = 1.046(1.009-1.084), p = 0.0134] were significantly associated with relapse risk. A risk model was established on the basis of multivariable Cox regression results. Risk value = 0.021038 * Age + 0.515628 * Biologic_type (Z = 0,T/M = 1) + 0.025048 * Disease_Duration. The validated patients were divided into two groups by median risk value (1.5). The high-risk group (risk value > 1.5) had a non-significant higher relapse risk than the low-risk group (risk value < 1.5), with a hazard ratio of 1.62 [95% confidence interval (CI) = 0.82-3.23, p = 0.1809].

CONCLUSION

Types of biologics used, disease duration > or ≤ 2 years, and PASI 90 improvement at the end of trial affect the 1-year DFS after biologics discontinuation. Further studies consisting of a larger patient number and longer follow-up period are needed to verify our findings.

TRIAL REGISTRATION

ClinicalTrials.gov identifiers NCT02694523, NCT03047395, NCT02207224, NCT02576431, NCT03482011, and NCT03556202.

Deep resolution of clinical, cellular and transcriptomic inflammatory markers of psoriasis over 52 weeks of interleukin-17A inhibition by secukinumab

BACKGROUND

Secukinumab, an anti-interleukin (IL)-17A monoclonal antibody, induces histological and molecular resolution of psoriatic plaques by 12 weeks. However, the long-term effects of secukinumab on the molecular resolution of psoriatic inflammation remain unknown.

OBJECTIVES

To investigate the molecular resolution of psoriasis following 52 weeks of secukinumab treatment.

METHODS

This was a two-part phase II randomized double-blinded placebo-controlled 52-week study of patients with moderate-to-severe psoriasis receiving secukinumab 300 mg (NCT01537432). Psoriatic lesional and nonlesional skin biopsies were obtained at baseline and at weeks 12 and 52, and the composition of the residual disease genomic profile (RDGP; i.e. 'molecular scar') of biopsies from secukinumab responders analysed.

RESULTS

After 52 weeks of treatment, 14 of 24 enrolled patients were considered to be clinical responders [≥ 75% improvement in Psoriasis Area and Severity Index (PASI 75)], 4 of 24 were considered to be nonresponders (< PASI 75) and 6 of 24 patients were lost to follow-up; both the histological and transcriptomic profiles of PASI 75 responders improved from week 12 to week 52. RDGP transcripts of histological responders only partially overlapped between weeks 12 and 52, despite a similar number of transcripts in each RDGP; specifically, four novel transcript subsets showed distinct expression dynamics between weeks 12 and 52 ('slow-resolving', 'recurring', 'persistent' and 'resolved'), with anti-inflammatory and immunomodulatory genes (e.g. SOCS1, CD207 and IL37) notably restored at week 52. Shorter disease duration prior to secukinumab treatment coincided with greater transcript improvements at weeks 12 and 52.

CONCLUSIONS

Secukinumab improves the histological and molecular phenotype of psoriatic lesional skin up to 52 weeks of treatment; these results suggest possible mechanisms that drive long-term control of psoriasis.

Secukinumab and Dead Sea Climatotherapy Impact Resolved Psoriasis Skin Differently Potentially Affecting Disease Memory

Secukinumab and Dead Sea treatment result in clear skin for many psoriasis patients, through distinct mechanisms. However, recurrence in the same areas after treatments suggests the existence of a molecular scar. We aimed to compare the molecular and genetic differences in psoriasis patients who achieved complete response from secukinumab and Dead Sea climatotherapy treatments. We performed quantitative immunohistochemical and transcriptomic analysis, in addition to digital spatial profiling of skin punch biopsies. Histologically, both treatments resulted in a normalization of the lesional skin to a level resembling nonlesional skin. Interestingly, the transcriptome was not normalized by either treatments. We revealed 479 differentially expressed genes between secukinumab and Dead Sea climatotherapy at the end of treatment, with a psoriasis panel identifying SERPINB4, SERPINB13, IL36G, IL36RN, and AKR1B10 as upregulated in Dead Sea climatotherapy compared with secukinumab. Using digital spatial profiling, pan-RAS was observed to be differentially expressed in the microenvironment surrounding CD103+ cells, and IDO1 was differentially expressed in the dermis when comparing the two treatments. The differences observed between secukinumab and Dead Sea climatotherapy suggest the presence of a molecular scar, which may stem from mechanistically different pathways and potentially contribute to disease recurrence. This may be important for determining treatment response duration and disease memory.

The Role of Epigenetic Factors in the Pathogenesis of Psoriasis

Psoriasis is a chronic inflammatory skin disease, the prevalence of which is increasing. Genetic, genomic, and epigenetic changes play a significant role in the pathogenesis of psoriasis. This review summarizes the impact of epigenetics on the development of psoriasis and highlights challenges for the future. The development of epigenetics provides a basis for the search for genetic markers associated with the major histocompatibility complex. Genome-wide association studies have made it possible to link psoriasis to genes and therefore to epigenetics. The acquired knowledge may in the future serve as a solid foundation for developing newer, increasingly effective methods of treating psoriasis. In this narrative review, we discuss the role of epigenetic factors in the pathogenesis of psoriasis.