Introduction

We conducted a retrospective cohort study to evaluate the risk of serious infections in patients with Ulcerative Colitis (UC) initiating advanced biological or small molecule therapies

Aims & Methods

Using a large U.S. administrative, Optum Labs® database, we identified adult UC patients who initiated treatment with either tumor necrosis factor-α (TNF) antagonists (infliximab, adalimumab, and certolizumab pegol), IL-12/23 antagonists (ustekinumab), IL-23 antagonists (risankizumab), anti-integrin agents (vedolizumab), S1PR modulators (ozanimod), or Janus kinase (JAK) inhibitors (tofacitinib, upadacitinib) between 2016-2023. Eligible patients had at least one year of continuous insurance enrollment before and after treatment initiation. The primary outcome was the incidence and risk of serious infections requiring hospitalization. We used stabilized inverse probability of treatment weighting (IPW) based on multinomial propensity scores to balance baseline patient and disease characteristics. Comparative risk of serious infections was assessed through Cox proportional hazards models incorporating stabilized weights.

Results

A total of 9,430 UC patients were included (mean age: 47.5 ± 17.7 years; 49.2% female; 50% on concomitant corticosteroids), followed for a mean duration of 2.3 ± 0.8 years. The incidence rates (IR) per 100 person-years of serious infections were: TNF antagonists (n = 4,111; IR: 6.08 [5.47; 6.70]), vedolizumab (n = 3,165; IR: 5.03 [0.44; 5.65]), IL-12/23 or IL-23 antagonists (n = 1,342; IR: 4.25 [3.34; 5.21]), S1PR modulators (n = 111; IR: 5.17 [1.03; 10.34]) and JAK inhibitors (n = 701; IR: 4.09 [2.79; 5.49]). Risk of serious infections was lower with JAK inhibitors and IL-12/23, or IL-23 antagonists compared to TNF antagonists, and lower with IL-12/23 or IL-23 antagonists compared to vedolizumab. Hazard ratios are represented in the table 1.

Table 1: Risk of serious infections in patients with ulcerative colitis taking advanced biological therapies

Conclusion

In this large real-world cohort of UC patients, initiation with newly developed efficaeous agents like JAK inhibitors and drugs inhibiting the IL-23 pathway was associated with a lower risk of serious infection compared to TNF antagonists.

Disclosure

CM: Consulting fees from AbbVie, Alimentiv, Amgen, AVIR Pharma Inc, Bristol Myers Squibb, Celltrion, Eli Lilly, Ferring, Forte Biosciences, Fresenius Kabi, Gilead, Janssen, McKesson, Mirador Therapeutics, Mylan, Pendopharm, Pfizer, Prometheus Biosciences Inc., Roche, Sanofi, Takeda, Tillotts Pharma; speaker's fees from AbbVie, Amgen, AVIR Pharma Inc, Alimentiv, Bristol Myers Squibb, Eli Lilly, Ferring, Fresenius Kabi, Janssen, Merck, Organon, Pendopharm, Pfizer, Sanofi, Takeda, Tillotts Pharma; royalties from Springer Publishing; research support from AbbVie, Eli Lilly, Ferring, Pfizer.
VJ: Consulting/advisory board fees from AbbVie, Alimentiv Inc, Arena pharmaceuticals, Asahi Kasei Pharma, Asieris, Astra Zeneca, Bristol Myers Squibb, Celltrion, Eli Lilly, Ferring, Flagship Pioneering, Fresenius Kabi, Galapagos, GlaxoSmithKline, Genentech, Gilead, Janssen, Merck, Mylan, Pandion, Pendopharm, Pfizer, Protagonist, Prometheus, Reistone Biopharma, Roche, Sandoz, Second Genome,Takeda, Teva, Topivert, Ventyx, Vividion; speaker’s fees from, Abbvie, Ferring, Galapagos, Janssen Pfizer Shire, Takeda, Fresenius Kabi
SS: Research grants from Pfizer

Introduction

Paneth cells (PC) are involved in the regulation of intestinal microbiota, and their function in intestinal lymph- and angiogenesis has been recently described. Uptake of dietary lipids is largely dependent on intestinal lymphatic vessels, and impairments in their density or function may result in significant depletion of lipids in the liver. In this study, we assessed the effects of PC deficiency in a model of metabolic dysfunction associated steatotic liver disease (MASLD).

Aims & Methods

We induced functional inactivation of PC in male and female mice (Sox9^lox/loxVilCre^ERT2) by intraperitoneal injection of 1mg/day of tamoxifen for three consecutive days. One week later, all mice, PC-deficient, and control littermates were randomly subjected to a high sucrose/fructose - high fat (60% lard) (HFD) or standard (SD) diet for 16 weeks. Intraepithelial leakage of FITC-albumin was measured in vivo using laser-probe endomicroscopy. Several tissues were harvested for histological examination, molecular biology analyses, and imaging mass cytometry (IMC). Fecal samples were collected at both the start and end of the experiment to assess intestinal dysbiosis and fecal fat content.

Results

All mice fed HFD gained weight compared to SD, but the weight gain in PC-deficient-HFD was significantly lower than in HFD-controls. Consistent with these findings, the severity of hepatic steatosis was significantly lower in PC-deficient mice compared to HFD-control. IMC of liver tissue revealed lower levels of collagen deposition and steatosis associated with fewer macrophages and neutrophils activation/infiltration in PC-deficient-HFD than in control-HFD mice. In vivo, microscopy showed a lower disruption of intestinal vascular barriers in PC deficient-HFD mice than in HFD controls. The intestinal expression of distinct lymphangiogenic genes (VEGFC, VEGFR3, Prox1) was reduced in the PC-deficient HFD or SD. Moreover, PC-deficient-HFD showed a higher level of fecal fat excretion than HFD controls. The alpha diversity of the fecal microbiota showed no significant differences based on gender or PC deficiency in the SD groups. Nevertheless, the beta diversity showed significant differences in PC-deficient fed SD after 16 weeks. Furthermore, we found significant alpha- and -beta diversity associated with decreased Bacteroidota and increased Firmicutes in HFD and PC-deficient mice.

Conclusion

PC deficiency attenuated the severity of steatosis and fibrosis in mice, suggesting their regulatory role on intestinal lymphatics contributing to the pathogenesis of MASLD. Thus, Paneth cells may represent a novel target for intervention in MASLD.

Introduction

Histological assessment in Inflammatory Bowel Disease (IBD) is often required with endoscopic diagnoses to establish disease activity. This is evident in patients where histological healing is a major therapeutic goal and when endoscopic healing can also exhibit microscopic disease. IBD can manifest in numerous landmark features which are structural and inflammatory related. Histological activity is determined through erosion/ulceration, architectural changes in tissue morphology, mucin depletion and presence of varied inflammatory cells the colonic crypts, Muscularis Mucosae (MM) and Lamina Propria (LP). Histological scoring systems vary in complexity and methodology and subject to discrepancies in inter-rater consensus. Our model aims to provide accurate measurements of key histological features in Ulcerative Colitis (UC) used in scoring systems as both a decision support tool for improving pathologist scoring alignment and sensitive quantitative metrics of inflammatory cells to evaluate disease activity.

Aims & Methods

We developed an ensemble of convolutional neural networks to identify cell types, tissue foreground, crypts, MM, and haemorrhage in H&E images of IBD biopsy data. Models were trained with expert-labelled annotations comprising 34,289 image-annotation pairs (80% training, 20% validation) from in-house and publicly available data. Model accuracy was assessed with 6,464 held-out image-annotation pairs from 26 Whole Slide Images (WSIs) from all sites of the large intestine commonly sampled during endoscopic investigation, while ground truth annotations were reviewed by GI-trained pathologists to certify accuracy. We used geospatial methods to derive boundaries of the LP from other predicted domains, associate cells to delineated regions, measure architectural changes and derive spatially differentiated metrics of inflammatory burden in 66 patients with confirmed UC.

Results

The AI model spatially differentiates and segments cell types and tissue compartments. This produces metrics for inflammatory cell counts in regions of interest such as crypts, LP, and MM. Goblet cell segmentation within crypts allows for measurement of mucin depletion. This can be visualized for pathologists where each inflammatory cell type across the WSI is represented as density metrics. Automated quantification of inflammatory cells across AI-identified tissue domains throughout the bowels of UC patients revealed large intra- and inter-patient variability.

Conclusion

Our AI model can measure histological healing by detecting architectural change, inflammatory cell number, goblet cell reduction and erosions and ulceration. This allows for metrics and visual overlays to assist with pathologist reading and quantifying changes in mucosal biopsies in a clinical trial setting which requires extremely sensitive tools only AI can provide.

Disclosure

DW/AM/RL/PA/PW and CL are employees at Perspectum Ltd. EF and RG are consultants for Perspectum Ltd. All other co-authors have no conflicts of interest to declare relevant to this work.