Background Risankizumab is a humanized IgG monoclonal antibody that selectively inhibits interleukin-23 through binding the p19 subunit. In Phase 3 trials, risankizumab demonstrated superior efficacy compared with adalimumab and ustekinumab in patients with moderate-to-severe plaque psoriasis. Here, we evaluated the impact of baseline characteristics on efficacy of risankizumab compared with ustekinumab in patients with moderate-to-severe plaque psoriasis. Methods This analysis included all patients initially randomized to risankizumab or ustekinumab from the replicate, double-blinded, randomized, placebo-controlled phase 3 trials, UltIMMa-1 (NCT02684370) and UltIMMa-2 (NCT02684357). Patients received either risankizumab (150 mg) or ustekinumab (weight-based; 45 or 90 mg per label) at weeks 0, 4, 16, 28 and 40. Efficacy was assessed as the proportion of patients achieving ≥90% improvement in Psoriasis Area and Severity Index (PASI 90) at weeks 16 and 52 by baseline patient demographics, disease characteristics and prior biologic exposure. Mean per cent improvement in PASI was calculated by body weight and body mass index at week 52. Missing efficacy data were imputed as non-responders for categorical variables and last observation carried forward for continuous variables. Logistic regression analyses assessed for interactions between treatment and five independent variables (age, sex, weight, baseline PASI score and presence of psoriatic arthritis) at both weeks 16 and 52. Results Baseline patient demographics, disease characteristics and prior biologic exposure were similar between patients randomized to risankizumab (n = 598) and ustekinumab (n = 199). At weeks 16 and 52, risankizumab demonstrated superior efficacy compared with ustekinumab across these patient characteristics (P < 0.01). Logistic regression analyses demonstrated that risankizumab was superior to ustekinumab at weeks 16 and 52 in all models tested (P < 0.0001 for all). Conclusions Risankizumab demonstrated consistent and superior efficacy compared with ustekinumab regardless of patient demographics, disease characteristics or prior biologic exposure.

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Introduction: Patients with moderate-to-severe plaque psoriasis who experience poor clinical outcomes, including patients with obesity or prior treatment, need improved treatment options. Risankizumab specifically inhibits interleukin 23 and has demonstrated superior efficacy in active-comparator studies in patients with moderate-to-severe plaque psoriasis. We compared the efficacy of risankizumab with that of secukinumab across patient subgroups.Methods: Subgroup analyses using data from the phase 3 IMMerge study (NCT03478787) were performed. Efficacy in adults with moderate-tosevere psoriasis treated with risankizumab 150 mg and secukinumab 300 mg was assessed as the proportion of patients who achieved C 90% improvement in Psoriasis Area Severity Index (PASI 90) at week 52 across demographics and disease characteristics. Post hoc analyses evaluated the proportion of patients who achieved PASI 90 and the least-squares mean percent PASI improvement from baseline at week 52 by body weight and body mass index (BMI), PASI 90 by prior treatment, and clinical response [PASI 90, PASI 100, and/or static Physician's Global Assessment (sPGA) score of clear (0) or almost clear (1)] at week 16 and maintained particular

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Ultra high-throughput sequencing of transcriptomes (RNA-Seq) is a widely used method for quantifying gene expression levels due to its low cost, high accuracy and wide dynamic range for detection. However, the nature of RNA-Seq makes it nearly impossible to provide absolute measurements of transcript abundances. Several units or data summarization methods for transcript quantification have been proposed in the past to account for differences in transcript lengths and sequencing depths across different genes and different samples. Nevertheless, further between-sample normalization is still needed for reliable detection of differentially expressed genes. In this paper we propose a unified statistical model for joint detection of differential gene expression and between-sample normalization. Our method is independent of the unit in which gene expression levels are summarized. We also introduce an efficient algorithm for model fitting. Due to the L0-penalized likelihood used in our model, it is able to reliably normalize the data and detect differential gene expression in some cases when more than 50% of the genes are differentially expressed in an asymmetric manner. We compare our method with existing methods using simulated and real data sets.