Pustular psoriasis: Molecular pathways and effects of spesolimab in generalized pustular psoriasis.

BACKGROUND: The IL-36 pathway plays a key role in the pathogenesis of generalized pustular psoriasis (GPP). In a proof-of-concept clinical trial, treatment with spesolimab, an anti-IL-36 receptor antibody, resulted in rapid skin and pustular clearance in patients presenting with GPP flares. OBJECTIVE: We sought to compare the molecular profiles of lesional and nonlesional skin from patients with GPP or palmoplantar pustulosis (PPP) with skin from healthy volunteers, and to investigate the molecular changes after spesolimab treatment in the skin and blood of patients with GPP flares. METHODS: Pre- and post-treatment skin and blood samples were collected from patients with GPP who participated in a single-arm, phase I study (n = 7). Skin biopsies from patients with PPP (n = 8) and healthy volunteers (n = 16) were obtained for comparison at baseline. Biomarkers were assessed by RNA-sequencing, histopathology, and immunohistochemistry. RESULTS: In GPP and PPP lesions, 1287 transcripts were commonly upregulated or downregulated. Selected transcripts from the IL-36 signaling pathway were upregulated in untreated GPP and PPP lesions. In patients with GPP, IL-36 pathway-related signatures, TH1/TH17 and innate inflammation signaling, neutrophilic mediators, and keratinocyte-driven inflammation pathways were downregulated by spesolimab as early as week 1. Spesolimab also decreased related serum biomarkers and cell populations in the skin lesions from patients with GPP, including CD3+ T, CD11c+, and IL-36γ+ cells and lipocalin-2-expressing cells. CONCLUSIONS: In patients with GPP, spesolimab showed rapid modulation of commonly dysregulated molecular pathways in GPP and PPP, which may be associated with improved clinical outcomes.

Bayesian MCPMod.

Multiple comparison procedures and modeling (MCPMod) has established itself as a method for dose-finding under model uncertainty. A downside of MCPMod is that due to its frequentist nature in particular with respect to the multiple comparison part it is tough to incorporate historical information in a systematic fashion. A typical situation where such historical information is available is existing data for the placebo group from previous trials. There are multiple Bayesian concepts for integrating historical data in a systematic and even dynamic fashion like the meta-analytic prior approach. In this article, we define Bayesian MCPMod (BMCPMod) that is build upon these two aspects. BMCPMod is able to mimic the results of the classical MCPMod for non-informative priors. At the same time, it allows for the inclusion of historical data in a systematic fashion. After the definition of BMCPMod related characteristics for a Bayesian approach similar to the MCP-testing part are derived. The BMCPMod is compared to classical MCPMod/non-informative priors via simulations. Aspects of mixture priors, optimal contrast vectors, and impact of allocation ratios are discussed and an example for designing a BMPCMod trial is given.

Phase I studies of BI 1015550, a preferential phosphodiesterase 4B inhibitor, in healthy males and patients with idiopathic pulmonary fibrosis.

Introduction: BI 1015550 is a phosphodiesterase 4 (PDE4) inhibitor that has antifibrotic properties. Phase I and Ic studies were conducted to investigate the safety, tolerability and pharmacokinetics of BI 1015550 in healthy male subjects and patients with idiopathic pulmonary fibrosis (IPF). Methods: In the phase I study, 42 subjects were partially randomised to receive placebo or BI 1015550 in single rising doses of 36 mg and 48 mg, or multiple rising doses of 6 mg and 12 mg twice daily over 14 days. In the phase Ic study, 15 patients with IPF were randomised to receive 18 mg BI 1015550 or placebo twice daily for up to 12 weeks. For both studies, the primary endpoint was the number of subjects with drug-related adverse events (AEs). Results: In the Phase I study, drug-related AEs were reported for 50.0% of healthy male subjects treated with a single dose of BI 1015550, compared with 16.7% receiving placebo. For those receiving multiple doses, drug-related AEs were reported for 37.5% of those treated with BI 1015550 and 12.5% receiving placebo. The most frequently reported AEs by organ class were nervous system disorders, which were largely driven by headache. In the Phase Ic study, drug-related AEs were reported in 90.0% of patients treated with BI 1015550, compared with 60.0% of those receiving placebo. The most frequent AEs by organ class were gastrointestinal AEs. Conclusions: BI 1015550 had an acceptable safety profile in healthy male subjects and male and female patients with IPF, supporting further development in larger trials.

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases.

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the extent of abnormalities using unsupervised clustering algorithms in residue function maps leads to intrinsic normalization and could reduce variability. Purpose: We investigated the reproducibility of perfusion defects in percent (QDP) in clinically stable patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Methods: 15 CF (29.3 ± 9.3y, FEV1%predicted = 66.6 ± 15.8%) and 20 COPD (66.5 ± 8.9y, FEV1%predicted = 42.0 ± 13.3%) patients underwent DCE-MRI twice 1 month apart. QDP, pulmonary blood flow (PBF), and pulmonary blood volume (PBV) were computed from residue function maps using an in-house quantification pipeline. A previously validated MRI perfusion score was visually assessed by an expert reader. Results: Overall, mean QDP, PBF, and PBV did not change within 1 month, except for QDP in COPD (p < 0.05). We observed smaller limits of agreement (± 1.96 SD) related to the median for QDP (CF: ± 38%, COPD: ± 37%) compared to PBF (CF: ± 89%, COPD: ± 55%) and PBV (CF: ± 55%, COPD: ± 51%). QDP correlated moderately with the MRI perfusion score in CF (r = 0.46, p < 0.05) and COPD (r = 0.66, p < 0.001). PBF and PBV correlated poorly with the MRI perfusion score in CF (r =-0.29, p = 0.132 and r =-0.35, p = 0.067, respectively) and moderately in COPD (r =-0.57 and r =-0.57, p < 0.001, respectively). Conclusion: In patients with muco-obstructive lung diseases, QDP was more robust and showed a higher correlation with the MRI perfusion score compared to the traditionally used perfusion metrics PBF and PBV.

The yule approximation for the site frequency spectrum after a selective sweep.

In the area of evolutionary theory, a key question is which portions of the genome of a species are targets of natural selection. Genetic hitchhiking is a theoretical concept that has helped to identify various such targets in natural populations. In the presence of recombination, a severe reduction in sequence diversity is expected around a strongly beneficial allele. The site frequency spectrum is an important tool in genome scans for selection and is composed of the numbers S(1),...,S(n-1), where S(k) is the number of single nucleotide polymorphisms (SNPs) present in k from n individuals. Previous work has shown that both the number of low- and high-frequency variants are elevated relative to neutral evolution when a strongly beneficial allele fixes. Here, we follow a recent investigation of genetic hitchhiking using a marked Yule process to obtain an analytical prediction of the site frequency spectrum in a panmictic population at the time of fixation of a highly beneficial mutation. We combine standard results from the neutral case with the effects of a selective sweep. As simulations show, the resulting formula produces predictions that are more accurate than previous approaches for the whole frequency spectrum. In particular, the formula correctly predicts the elevation of low- and high-frequency variants and is significantly more accurate than previously derived formulas for intermediate frequency variants.