Introduction

Biosimilars are used for the treatment of inflammatory bowel disease (IBD). However, data on the switch from one biosimilar to another still lack. We aimed to study, in an IBD cohort, the effects of the CT-P13-to-SB2-switch, in terms of effectiveness, safety, psychological implications, pharmacokinetic and inflammatory marker levels.

Aims & Methods

Consecutive CT-P13-to-SB2-switched IBD patients were enrolled and prospectively followed-up for 12 months (mo). Patient-reported outcomes (PROs) were employed: specific PRO-2 for CD and UC, IBD-Disability Index (IBD-DI), Functional Assessment of Chronic Illness Therapy Fatigue (FACIT), Short-IBD-Questionnaire (SIBDQ). Psychometric tests were administered to assess the psychological impact of the switch: Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Believes About Medicine (BMQ). C-reactive protein (CRP) levels, concomitant steroid use, and adverse events of interest (AEIs) were recorded. Serum samples were obtained before and after the switch to measure: infliximab (IFX) and anti-IFX trough levels (TLs), and cytokine (TNF-α, IL-6, IL-8, IL-17A, IL22) concentrations.

Results

We enrolled 119 (73 CD, 46 UC). Median age was 40 years (IQR:27-52), median disease duration 9 years (IQR:4-16). Median duration of pre-switch infliximab was 45 mo (IQR:24.5-75.5); 50 (42.0%) patients had previously received infliximab originator-to-CT-P13 switch (median CT-P13 duration: 28 mo, IQR:27-29). In UC, mean stool frequency scores were 0.2±0.5, 0.4±0.8, 0.7±1.5, 0.2±0.5, and mean rectal bleeding scores were 0.1±0.4, 0.1±0.4, 0.4±0.8, 0.0±0.0, at baseline, 2, 6 and 12 mo (p=ns for all). In CD, mean stool frequency scores were 1.9±2.0, 2.1±2.2, 1.8±1.9, 1.5±1.8(p=ns for all), and mean abdominal pain scores were 0.5±0.7, 0.4±0.6, 0.3±0.6, 0.2±0.5 (p=ns for all, expect baseline vs.12 mo p=0.01) at baseline, 2, 6 and 12 mo. Patients in SFCR were 100 (84.0%) at baseline, 97 (81.5%) at 2 mo (p=ns vs baseline), 86 (72.3%) at 6 mo (p=0.03 vs baseline) and 90 (75.6%) at 12 mo (p=ns vs baseline). Mean CRP levels were constantly <5mg/L (3.4±8.0, 3.2±5.8, 2.4±4.0, 2.2±5.7, at baseline, 2, 6 and 12 mo, p=ns for all). No significant changes in psychometric assessments were observed during follow-up in regard to: HADS (10.3±6.8 at baseline, 10.6±8.1 at 2 mo, 11.3±8.1 at 6 mo), PSS (14.3±7.8, 15.7±8.8, 14.9±8.1), IBD-DI (-0.7±4.6, -1.3±5.9, 9.0±4.7), FACIT (42.2±8.9, 40.6±10.8, 41.8±8.6), SIBDQ (55.2±11.4, 54.1±11.8, 56.7±10.7), BMQ-S1-SS1 (18.0±3.4, 18.0±2.3, 17.5±2.9), BMQ-S1-SS2 (15.3±2.6, 15.4±2.0, 15.1±1.9), BMQ-S2-SS1 (9.8±3.2, 9.3±2.6, 9.5±2.7), BMQ-S2-SS2 (8.7±2.9, 8.6±3.0, 9.0±2.7). AEIs were observed in 43(36.1%) patients, most commonly infections (17) and arthralgias (15); 3 cancer diagnoses and 2 infusion reactions were recorded. Pre- and post-switch median IFX TLs remained stable (4.07 vs 4.86 µg/mL, p=ns). Anti-IFX antibodies were detected in 5.1% and 2.5% of patients before and after the switch, respectively; de novo antibody formation was observed in one patient after the switch. The median concentration of TNF-α (9.77 vs 9.16 pg/mL, p=ns), IL-8 (9.04 vs 10.10 pg/mL, p=ns) and IL-22 (5.52 vs 6.43, p=ns) remained stable; conversely, a significant increase was observed in IL-6 (1.20 vs 1.73 pg/mL, p=0.01) and IL-17A (0.72 vs 1.08 pg/mL, p=0.02) median concentrations.

Conclusion

Switching from CT-P13 to SB2 can be considered safe, does not significantly affect treatment effectiveness nor drug pharmacokinetic, and is seemingly not associated with major negative psychological implications.

Disclosure

Daniela Pugliese received speaker fees and/or advisory board from AbbVie, MSD, Takeda and Janssen, Pfizer. Giuseppe Privitera received consultancy fees from Alphasigma and Janssen.
Alessandro Armuzzi: consulting and/or advisory board fees from AbbVie, Amgen, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Celltrion, Eli-Lilly, Ferring, Galapagos, Gilead, Janssen, MSD, Mylan, Pfizer, Protagonist Therapeutics, Roche, Samsung Bioepis, Sandoz, Takeda; lecture and/or speaker bureau fees from AbbVie, Amgen, Arena, Biogen, Bristol-Myers Squibb, Eli-Lilly, Ferring, Galapagos, Gilead, Janssen, MSD, Mitsubishi-Tanabe, Novartis, Pfizer, Roche, Sandoz, Samsung Bioepis, Takeda; and research grants from MSD, Pfizer, Takeda and Biogen. The remaining authors declare no competing interests.

Introduction

Tofacitinib is an oral small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). We investigated baseline transcriptomic, proteomic and microbiome features associated with response and created a multi-omic machine learning model to predict response in UC patients treated with tofacitinib.

Aims & Methods

Moderate to severely active UC patients were included in this prospective, open-label study and treated with tofacitinib 10mg BID for 8 weeks. Clinical, biochemical, endoscopic and histological data at baseline and at week 8 were recorded. Histo-endoscopic mucosal improvement (HEMI) was defined as an endoscopic Mayo score (EMS) ≤1 combined with histologic remission (Robarts Histopathology Index ≤3 without mucosal neutrophils). Baseline mucosal biopsies were used for transcriptomic analysis (bulk RNA sequencing), proteomic analysis (Olink Explore 384 panel) and faecal microbial composition (16S). Baseline blood samples were used for transcriptomic analysis (bulk RNA sequencing). All data were integrated in a comprehensive dataset. Using HEMI at week 8 as primary outcome measure, we generated a predictive model by using an extra-trees machine learning algorithm. A two-step approach was used for feature selection using variance thresholding and univariate selection based on ANOVA f-score. The settings for feature selection were optimized on each individual omic layer separately. The multi-omics model was trained with a leave-one-out cross-validation approach and performance was measured on the combined left-out samples by calculating the area under the receiver operating characteristics curve (AUROC). The individual importance of separate multi-omics features was calculated using permutation feature importance.

Results

Forty patients were included, the majority of which (65%) had severe endoscopic disease (EMS 3) at inclusion, involving at least the left colon (E2: 53%, E3: 43%). Prior treatment included anti-tumour necrosis factor agents (78%), vedolizumab (55%) or both (43%). Fifteen patients (38%) achieved HEMI at week 8. Complete omic sampling was available in 27 patients, which included fourteen responders (52%) and thirteen non-responders (48%) according to HEMI. The top 10 most important features to predict response at week 8 for each separate omic layer are shown in Table 1. Our multi-omics model included 50 features, mainly consisting of transcriptomic features in tissue and blood, as these were the most important in predicting response. Our model predicted HEMI with tofacitinib with an AUROC of 0.744 (± 0.034).

Table 1. Most important features for each individual omic layer, from most important (1) to less important (10). Features that are also included in the multi-omics model are shown in bold.

Conclusion

Histo-endoscopic mucosal improvement was achieved in 38% of UC patients after 8 weeks of tofacitinib. A multi-omics machine learning algorithm was able to predict response to tofacitinib in UC patients with good performance. However, before application in clinical practice, further validation is warranted.

Disclosure

This study was sponsored by Amsterdam UMC. The study was funded and supported by Pfizer.

Introduction

Clinical and radiologic factors associated with perianal fistula (PAF) healing in Crohn’s disease (CD) are poorly understood. We aimed to create a risk assessment model (RAM) to predict anti-TNF treatment failure in patients with PAF.

Aims & Methods

We performed a multicenter retrospective cohort study between 2005 and 2021. We included biologic naïve adults (>17 years) with CD who initiated anti-TNF therapy for active PAF after undergoing an MRI pelvis. Our primary outcome was treatment failure at 6 months defined clinically as ongoing PAF drainage. Pre-treatment MRI studies were prospectively re-evaluated centrally by radiologists blinded to clinical outcomes. Variables with p-values <0.1 on univariable analysis were included in our overall RAM and a simplified version consisting of MRI variables alone. Model performance was determined by calculating the area under the receiver operating characteristics curve (AUC) and internally validated using bootstrap methods. Our RAMs were compared with MAGNIFI-CD and modified Van Assche MRI scores (mVAS) by the De Long test. Aggregate scores from weighted variables from our simplified model were divided into tertiles to predict low, moderate and high risk of anti-TNF treatment failure.

Results

Our analysis included 221 patients: 133 (60%) males; mean age 29 years (SD, 21-42); 158 (71%) treated with infliximab and 70 (32%) treated with adalimumab. Treatment failure occurred in 102 (46%) patients. Our overall RAM included 3 clinical characteristics (age at fistula diagnosis, time to initiate biologic therapy and current smoking) and 8 MRI characteristics (non-inter/transsphincteric fistula anatomy, fistula length >4.3cm, primary tracts >1, secondary tracts >1, number of external openings, tract hyperintensity on T1 weighted imaging, horseshoe anatomy, and collections >1.3 cm). Our adjusted overall RAM and simplified MRI version were accurate in predicting fistula healing (AUC (95% CI), 0.75 (0.69-0.82) and 0.73 (0.66-0.79) respectively) (Table 1). The overall and simplified MRI RAMs performed significantly better than MAGNIFI-CD (p = 0.0002 and 0.0005) and the mVAS (p <0.0001 and <0.0001) respectively at predicting anti-TNF treatment failure. Aggregate scores for the simplified MRI RAM ranged between 0-30. Scores of 0-10, 11-20 and 21-30 were associated with treatment failure in 19%, 45% and 71% of patients respectively.

Conclusion

In our multicenter study, we developed RAMs consisting of clinical and/or radiologic variables that can accurately predict treatment failure in patients with PAF. These findings may have important implications in clinical study design to help balance cohorts, and in clinical practice to help set realistic expectations of PAF healing for patients and providers.

Disclosure

Jeffrey McCurdy has received consultancy fees and/or speaker honoraria from Janssen, Abbvie, BMS, Fresenius Kabi, Takeda, and Pfizer.

Introduction

Upper gastrointestinal bleeding (UGIB) poses a significant threat in patients presenting to the emergency department, particularly in those with associated hemodynamic instability. Despite major advancements in medical and endoscopic therapeutic options, mortality rates have not shown a substantial decline over the years. This study aimed to assess clinical practice patterns in order to gain a clearer understanding of the pre-endoscopic assessment for patients with acute UGIB.

Aims & Methods

This study was an international online survey based on a prospective two-phase data collection approach, conducted between April 2023 and November 2023. The survey targeted practicing physicians actively involved in managing patients with UGIB. Data collection and management were performed through Research Electronic Data Capture (REDCap), an electronic data collection tool. The survey comprised four key domains: demographics and clinical practice, definitions of hemodynamic instability, pre-endoscopic assessment, and optimal timing of endoscopy, encompassing a total of 33 questions. The primary analysis provided a descriptive assessment of responses reported as proportions. Secondary analyses explored differences in responses based on years of clinical experience, practice setting, and the estimated annual number of UGIB cases encountered

Results

A total of 533 clinicians from 50 countries completed our survey. Among respondents, 54.6% work in university-based hospitals, and 67.7% reported managing over 100 UGIB patients annually. Regarding clinical experience, 55.7% have less than 10 years, with 44.3% with over 10 years of experience. Only 47.1% of respondents reported that their hospitals implement a triage system for UGIB patients. Additionally, 39.6% indicated that they do not use any risk stratification scores at the time of patient presentation. For initial fluid resuscitation, 0.9% saline (40.9%) and balanced crystalloid fluids (39.5%) were almost equally preferred. The rate of fluid resuscitation was reported to depend on the patient’s hemodynamic status by 40.2% of respondents. Most respondents indicated that hemodynamically unstable patients are primarily managed in the emergency department or intensive care unit. A total of 34.3% of respondents indicated that they do not routinely use prokinetics prior to endoscopy in UGIB patients, while 27.6% reported not commonly using them even in cases involving severe bleeding or known gastric emptying disorders. Proton pump inhibitors are administered prior to upper GI endoscopy in all cases by 70.4% of respondents, while vasoconstricting agents are used by 61% in cases of suspected variceal bleeding.

Conclusion

Our survey found considerable variability among respondents regarding the pre-endoscopic management of patients presenting with upper gastrointestinal hemorrhage. The overall adherence to international guidelines was low, which can potentially result in a significant negative impact on patient outcomes.