Introduction

Per STRIDE II recommendations, the normalization of high-sensitivity C-reactive protein (hs-CRP) and fecal calprotectin (FCP) should be an intermediate treatment target for patients (pts) with moderate to severe Crohn’s disease (CD).¹ The safety and efficacy of risankizumab (RZB), an interleukin (IL)23 p19 inhibitor, and ustekinumab (UST), an IL12/23p40 inhibitor, in pts with CD have been reported previously.^2–4 Using data from the phase 3 SEQUENCE study, we compared RZB versus UST for the achievement of biomarker reduction and normalization (alone or in combination with disease remission).

Aims & Methods

SEQUENCE was an open-label, multicenter, randomized, efficacy assessor-blinded study that directly compared the efficacy and safety of RZB vs UST in pts with moderate to severe CD and prior anti-TNF failure (NCT04524611). Pts were randomized 1:1 to receive recommended induction and maintenance doses of RZB (N=255) or UST (N=265). A mandatory steroid taper began at week (W)2. Randomization was stratified by baseline (BL) steroid use and number of failed anti-TNFs. In this post hoc analysis, pts with elevated FCP (> 250 mg/kg) or hs-CRP (> 5 mg/L) at BL were assessed for achievement of normalized FCP (≤ 250 mg/kg)) or hs-CRP (≤ 5 mg/L) at week W8, week W24, and week W48. At week W24 and W48 week 48, achievement of normalized FCP or hs-CRP in combination with CD activity index (CDAI) clinical remission (CDAI < 150) or with inflammatory bowel disease questionnaire (IBDQ) remission (IBDQ total score ≥ 170), as well as achievement of a 50% reduction in hs-CRP or FCP and CDAI response (reduction of CDAI ≥100 points from BL), were examined. Endpoints were analyzed using non-responder imputation, while incorporating multiple imputation to handle missing data due to COVID-19 and/or geopolitical conflict. All P values are nominal.

Results

BL, baseline; CD, Crohn's disease; CDAI, CD activity index; CI, confidence interval; FCP, fecal calprotectin; hs-CRP, high-sensitivity C-reactive protein; IBDQ, inflammatory bowel disease questionnaire. CDAI clinical remission: CDAI < 150; IBDQ remission: IBDQ total score ≥ 170. aNormalization of hs-CRP is defined as ≤ 5 mg/L. bElevated hs-CRP is defined > 5 mg/L. cNormalization of FCP is defined as ≤ 250 mg/kg. dElevated FCP is defined > 250 mg/kg. Efficacy population includes all patients randomized to either ustekinumab (i.e., weight-based induction dose IV followed by maintenance dose 90-mg SC), or the selected dosing regimen for risankizumab (600 mg for induction, 360 mg for maintenance), and who received at least one dose of study drug. 95% CI for response rate is the synthetic result based on Student's t-distribution from PROC MIANALYZE procedure if there are missing data due to COVID-19 and/or geopolitical conflict or is based on the normal approximation to the binomial distribution if there are no missing data due to COVID-19 and/or geopolitical conflict. Response rate difference = (risankizumab - ustekinumab); 95% CI for difference is calculated based on normal approximation to the binomial distribution for the comparison of two treatment groups. P-value is based on Chi-square test. The calculations are based on non-responder imputation incorporating multiple imputation to handle missing data due to COVID-19 and/or geopolitical conflict. Pooled p-value from Chi-square is calculated following Li et al., Statistica Sinica. 1991; 1, 68-92.
Endpoint n/N % (95% CI)	Risankizumab Week 8	Ustekinumab Week 8	Response Rate Difference % (95% CI) P value	Risankizumab Week 24	Ustekinumab Week 24	Response Rate Difference % (95% CI) P value	Risankizumab Week 48	Ustekinumab Week 48	Response Rate Difference % (95% CI) P value
Normalization of hs-CRPa in patients with elevated hs-CRPb at BL	59/163; 35.6% (28.2, 42.9)	56/171; 32.7% (25.7, 39.8)	2.8% (-7.3, 13.0) P =.6	72/163; 44.2% (36.6, 51.9)	48/171; 28.1% (21.3, 34.8)	16.2% (6.0, 26.3) P <.01	71/163; 43.6% (35.9, 51.2)	38/171; 22.2% (16.0, 28.5)	21.3% (11.5, 31.2) P <.0001
Normalization of FCPc in patients with elevated FCPd at BL	48/170; 28.8% (22.0, 35.6)	25/180; 13.9% (8.8, 18.9)	14.9% (6.5, 23.4) P <.001	65/170; 38.2% (30.9, 45.5)	35/180; 19.4% (13.7, 25.2)	18.8 (9.5, 28.1) P <.0001	72/170 42.4% (34.9, 49.8)	24/180; 13.3% (8.4, 18.3)	29.0 (20.1, 38.0) P <.0001
Normalization of hs-CRPa or FCPcAND CDAI clinical remission In patients with elevated hs-CRPb or FCPd at BL				84/211; 39.9% (33.3, 46.5)	53/218; 24.3% (18.6, 30.0)	15.6% (6.9, 24.3) P <.001	97/211 46.0% (39.2, 52.7)	54/218; 24.8% (19.0, 30.5)	21.2% (12.4, 30.0) P <.0001
Normalization of hs-CRPa or FCPcAND IBDQ remission in patients with elevated hs-CRPb or FCPd at BL				57/211 27.0% (21.0, 33.0)	59/218; 27.1% (21.2, 33.0)	-0.1 (-8.5, 8.4) P = 1.0	47/211; 22.3% (16.7, 27.9)	34/218; 15.6% (10.8, 20.4)	6.7 (-0.7, 14.1) P = 1.0
≥ 50% decrease from BL in hs-CRP or FCP AND CDAI clinical response in patients with elevated hs-CRPb or FCPd at BL				111/211; 52.6% (45.8, 59.3)	74/218 33.9% (27.7, 40.2)	18.6 (9.4, 27.8) P <.0001	119/211; 56.4% (49.7, 63.1)	68/218; 31.2% (25.0, 37.3)	25.2 (16.1, 34.3) P <.0001

A greater proportion of pts receiving RZB vs UST achieved normalization of hs-CRP at W24 weeks 24 and W48, as well as normalization of FCP at weeks W8, W24, and 48 (P <.01 for all; Table). Rates of normalization of FCP or hs-CRP plus CDAI clinical remission were also greater at weeks W24 and W48 in pts receiving RZB vs UST, as were rates of CDAI clinical response and a ≥ 50% reduction in FCP or hs-CRP (P <.001 for all; Table). Rates of normalization of FCP or hs-CRP plus IBDQ remission were similar between pts receiving RZB vs UST (Table). The safety profiles of RZB and UST in CD have been previously reported.^2–4

Conclusion

A greater proportion of pts achieved normalization of biomarkers FCP or hs-CRP with RZB vs UST. Composite endpoints of CDAI clinical remission and biomarker normalization, as well as CDAI clinical response and biomarker reduction (≥ 50%) favored RZB vs UST at weeks 24 and 48.

References

1 Turner, D et al. Gastroenterology. 2021; 160, 1570–1583.
2 D’Haens, G et al. The Lancet. 2022; 399, 2015–2030.
3 Ferrante, M et al. The Lancet. 2022; 399, 2031–2046.
4 Feagan, BG et al. N Engl J Med. 2016; 375, 1946–1960.

Disclosure

Jean-Frederic Colombel: Research grants: AbbVie, Janssen Pharmaceuticals, and Takeda; lecture payments: AbbVie, Allergan, Amgen, Ferring Pharmaceuticals, Shire, and Takeda; consulting fees: AbbVie, Amgen, Arena Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Celgene Corporation, Ferring Pharmaceuticals, Galmed Research, Genentech, GlaxoSmithKline, Imedex, Immunic, Iterative Scopes, Janssen Pharmaceuticals, Kaleido Biosciences, Lilly, Merck, Microbia, Novartis, PBM Capital, Pfizer, Protagonist Therapeutics, Sanofi, Takeda, TiGenix, and Vifor; and holds stock options in Intestinal Biotech Development.
Geert D’Haens: Adviser: AbbVie, Ablynx, Active Biotech AB, Agomab Therapeutics, Alimentiv, Allergan, Alphabiomics, Amakem, Amgen, AM Pharma, Applied Molecular Therapeutics, Arena Pharmaceuticals, AstraZeneca, Biogen, Bristol Myers Squibb, Boehringer Ingelheim, Celltrion, DSM Pharma; Echo Pharmaceuticals, Engene, Exeliom Biosciences, Ferring, Dr Falk Pharma, Galapagos, Genentech/Roche, Gilead, GlaxoSmithKline, Gossamerbio, Immunic, Johnson and Johnson, Kintai Therapeutics, Lilly, Lument, Lycera, Medtronic, Merck Sharp & Dome, Mitsubishi Tanabe Pharma, Novo Nordisk, Otsuka, Pfizer, Photopill, ProciseDx, Prodigest, Progenity, Prometheus laboratories/Nestlé, Protagonist, RedHill, Samsung Bioepis, Sandoz, Seres, Setpoint, Shire, Takeda, Teva, Tillotts, Topivert, Versant, and Vifor.
Bram Verstockt: Research support: AbbVie, Biora Therapeutics, Landos, Pfizer, Sossei Heptares and Takeda; speaker’s fees: Abbvie, Biogen, Bristol Myers Squibb, Celltrion, Chiesi, Falk, Ferring, Galapagos, Janssen, Lily, MSD, Pfizer, R-Biopharm, Sandoz, Takeda, Tillots Pharma, Truvion and ViatrisMSD, Pfizer, R-Biopharm, Takeda, Truvion and Viatris.; Consultancy fees: Abbvie, Alfasigma, Alimentiv, Applied Strategic, Atheneum, BenevolentAI, Biora Therapeutics, Boxer Capital, Bristol Myers Squibb, Galapagos, Guidepont, Landos, Lily, Mylan, Inotrem, Ipsos, Janssen, Pfizer, Progenity, Sandoz, Sanofi, Santa Ana Bio, Sapphire Therapeutics, Sosei Heptares, Takeda, Tillots Pharma, Viatris, Alimentiv, Applied Strategic, Atheneum, Biora Therapeutics, Bristol Myers Squibb, Galapagos, Guidepont, Mylan, Inotrem, Ipsos, Janssen, Pfizer, Progenity, Sandoz, Sosei Heptares, Takeda, Tillots Pharma and Viatris; stock options: Vagustim.
Peter Irving: Honoraria: AbbVie, BMS, Celgene, Celltrion, Dr Falk Pharma, Ferring, Galapagos, Gilead, MSD, Janssen, Lilly, Pfizer, Takeda, Tillotts, Sapphire Medical, Sandoz, Shire, and Warner Chilcott; advisor: AbbVie, Arena, Boehringer Ingelheim, BMS, Celgene, Celltrion, Connect Biopharma, Genentech, Gilead, Hospira, Janssen, Lilly, MSD, Pfizer, Pharmacosmos, Prometheus, Roche, Sandoz, Samsung Bioepis, Takeda, Topivert, VH2, Vifor Pharma, and Warner Chilcott.
Alessandro Armuzzi: Consulting/advisory fees: AbbVie, Allergan, Amgen, Arena, Biogen, Bristol Myers Squibb, Celgene, Celltrion, Eli Lilly, Ferring, Galapagos, Gilead, Janssen, MSD, Mylan, Pfizer, Protagonist Therapeutics, Roche, Samsung Bioepis, Sandoz, and Takeda; speaker’s fees: AbbVie, Amgen, Arena, Biogen, Bristol Myers Squibb, Eli Lilly, Ferring, Galapagos, Gilead, Janssen, MSD, Novartis, Pfizer, Roche, Samsung Bioepis, Sandoz, Takeda, and Tigenix; research support: MSD, Takeda, Pfizer, and Biogen.
Benjamin Cohen: Consultant: Abbvie, Celgene-Bristol Myers Squibb, Lilly, Pfizer, Sublimity Therapeutics, Takeda, and Target RWE; DSMB for Emmes Biopharma Services LLC; educational grant from Pfizer.
Britta Siegmund: Consultant for AbbVie, Abivax, Arena, BMS, Boehringer, CED Service GmbH, Celgene, CT Scout, Endpoint Health, Falk, Forga Software, Galapagos, Janssen, Lilly, Materia Prima, Pfizer, Takeda, Pharma Insight, Predictimmune, PsiCro; speaker fees: AbbVie, BMS, CED Service GmbH, Chiesi, Falk, Ferring, Gilead, Janssen, Lilly, Materia Prima, Takeda, Pfizer; grant support: Arena/Pfizer (served as representative of the Charité).
Laurent Peyrin-Biroulet: Personal fees from AbbVie, Adacyte, Alimentiv, Alma Bio Therapeutics, Amgen, Applied Molecular Transport, Arena, Biogen, BMS, Celltrion, CONNECT Biopharm, Cytoki Pharma, Enthera, Ferring, Fresenius Kabi, Galapagos, Genentech, Gilead, Gossamer Bio, GSK, HAC-Pharma, IAG Image Analysis, Index Pharmaceuticals, Inotrem, Janssen, Lilly, Medac, Mopac, Morphic, MSD, Norgine, Nordic Pharma, Novartis, OM Pharma, ONO Pharma, OSE Immunotherapeutics, Pandion Therapeutics, Par’Immune, Pfizer, Prometheus, Protagonist, Roche, Sanofi, Sandoz, Takeda, Theravance, Thermo Fisher, Tigenix, Tillots, Viatris, Vifor, Ysopia, Abivax, Samsung, Ventyx, Roivant, and Vectivbio
Colla Cunneen, Andrew Garrison, Kristina Kligys, Javier Zambrano, and Toni Anschutz are full-time employees of AbbVie and may own AbbVie stock or options.

Introduction

Vedolizumab is one of the current treatments for patients with inflammatory bowel disease (IBD). The efficacy and safety, along with its gut specificity, make this drug an appealing therapeutic option for IBD patients with moderate to severe disease. However, as observed for other biologic treatments, a significant proportion of patients do not have an initial response to vedolizumab treatment. Currently, there is a lack of reliable predictive tools for vedolizumab treatment response, although this would help to alleviate the socioeconomic costs derived from this disease.

Aims & Methods

The primary aim of this study is to show putative transcriptional signatures associated with vedolizumab treatment response.
Methods: For the realization of this study, we used RNA-seq datasets from the Gene Expression Omnibus (GEO) database: GSE191328. This dataset includes samples from peripheral blood cells from responder and non-responder ulcerative colitis (UC) patients treated with vedolizumab and infliximab at baseline. Gene Set Enrichment Analysis (GSEA) was performed to compare the differential expression and the enrichment analysis was calculated using ssGSEA of the different groups under study.

Results

The analysis of the transcriptome of peripheral blood cells from responder (R) and non-responder (NR) vedolizumab-treated patients at baseline revealed interesting enrichment results. We found 20 gene sets positively enriched in NR versus R and only 9 gene sets negatively enriched in the same phenotypes. In order to find vedolizumab specific transcriptional signatures, we performed a similar analysis in samples from infliximab-treated patients. After that second round of analysis, we observed that around 50% of the enriched gene sets were similar for both biological treatments. Nevertheless, several interesting biological functions such as peroxisome function, reactive oxygen function or beta-catenin signalling are specifically enriched in NR patients to vedolizumab treatment.

Conclusion

Our data suggest that vedolizumab NR patients specifically show a transcriptional enrichment profile, which differs from vedolizumab R patients. Also, the comparative analysis with infliximab-treated patients reveals that those specific transcripts are modulated in response to this α4β7 integrin antibodies in peripheral blood cells. Therefore, we found a solid system to search for vedolizumab therapy predictive response markers using low invasive techniques.

Introduction

Inflammatory bowel disease (IBD) is a chronic intestinal disease with unknown causes. Symptoms include pain and bloody diarrhoea. Little is known about environmental factors that trigger flare-ups, including the effects of commonly used medications. Recent studies suggest a link between microbial factors and IBD, making it important to investigate the predictive level of antibiotics on flare-ups. New machine learning methods may be able predict IBD flare-ups by analysing patient data to identify contributing factors.

Aims & Methods

We aimed to investigate whether certain antibiotics increase the risk of flares in patients with IBD. We utilized a previously described¹ cohort of all patients with IBD in Denmark between 1974 and 2018. The cohort is based on the Danish Nationwide Patient Registry, which registers all healthcare related visits, treatments and procedures on an individual level. We utilized data between 1994 and 2018, and two distinct cohorts were created for the purpose of identifying flares. The first cohort was characterized by having an IBD-related hospital stay. The second cohort was characterized by experiencing a flare of IBD in need of systemic steroids. Patients were matched 1:5 using K-nearest neighbour. Antibiotics were grouped into the 3rd level of the Anatomical Therapeutic Chemical (ATC) classification system, with anthelmintics grouped into the 2nd level due to low numbers. All were given within 60 days before the outcome.
Cohorts were divided into an 80/20 training/testing set and trained using a 5-fold cross-validation with eXtreme Gradient Boosted decision tree (GBDT) framework with variables including year of remission/flare, sex, age at diagnosis, year of diagnosis, type of IBD, socioeconomic status², subtypes of IBD-medication, subtypes of biologics and subtypes of antibiotics. The final model was chosen based on the area under the receiver operating characteristic curve (AUROC). The final model was evaluated on the test set (i.e. unseen data for the model). Evaluation of the models was reported as accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV).

Results

There were in total 69,908 IBD patients in the original cohort. After applying the abovementioned algorithm, a total of 15,636 and 5,178 patients were included in the cohort for hospitalisation and steroid use, respectively.
The models achieved an AUROC of 0.71 (SD: 0.03) and 0.85 (SD: 0.008) for predicting a course of steroids and need for IBD-related hospitalization, respectively, on the training set. On the test-set, the models achieved an ACC of 82.72%, with a PPV of 36.36% and a NPV of 83.73% on predicting a course of steroids, and an ACC of 85.23%, with a PPV of 60.14% and a NPV of 87.80% on predicting IBD-related hospitalization. Quinolones and agents against amoebiasis and protozoal were identified as top 10 most important variables for making accurate predictions and for splitting decision trees. Other antibiotics, such as tetracyclines, betalactam, and intestinal anti-infectives, were also useful as splitting variables.

Conclusion

This study demonstrated the importance of antibiotics, particularly quinolones and agents against amoebiasis and protozoal, in predicting an IBD-related flare. The GDBT-trained models achieved a high accuracy in predicting hospitalization and steroid courses, with AUROC of 0.85 and 0.71, respectively. These findings highlight the impact antibiotics on the subsequent risk for a severer flare-up of IBD flare and may guide future research on the role of microbial factors in IBD.

References

¹ Lo B, Zhao M, Burisch J. Identifying patients with inflammatory bowel disease in the Danish National Patient Register. Dan Med J. 2023 Mar 27;70(4):A07220458. PMID: 36999818.
² https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/socio

Disclosure

An unrestricted grant was received from the Aage og Johanne Louis-Hansens Fond to support this study.

Introduction

OZA is approved in multiple countries for treatment of moderately to severely active UC in adults. In the phase 3 TN study, OZA was efficacious for up to 52 wk in pts with moderately to severely active UC. CRP is a widely used serum indicator of inflammation and prognosis in inflammatory bowel disease.

Aims & Methods

The effect of OZA on CRP levels and the association of CRP levels with OZA efficacy were assessed in all TN pts. During the 10-wk induction period, pts in Cohort 1 were randomized to OZA 0.92 mg or placebo (PBO) or received open-label OZA 0.92 mg in Cohort 2. Pts with clinical response to OZA at Week (W) 10 were rerandomized to OZA or PBO for maintenance through W52. Serum CRP levels were assessed at baseline (BL) through W52. Disease activity scores (rectal bleeding subscore [RBS], stool frequency subscore, Physician’s Global Assessment subscore, endoscopy subscore, and partial and total Mayo scores) were assessed at BL and W10. Efficacy endpoints (clinical remission, clinical response, endoscopic improvement, mucosal healing, and histologic remission) were assessed at W10. Associations of CRP levels with disease activity and efficacy endpoints were assessed using Spearman’s correlation and logistic regression, respectively.

Results

645 pts were randomized to OZA (n=429) or PBO (n=216) and 367 pts received open-label OZA for 10 wk; 230 and 227 OZA-treated pts who responded were rerandomized to OZA and PBO, respectively, for maintenance through W52 (69 PBO-treated pts who responded continued on PBO). Pts on OZA showed reductions from BL in CRP levels at W10, which was significant vs PBO (P<0.001, both cohorts) (Table). Reductions in CRP levels were significantly greater in pts with vs without clinical response at W10 in all treatment groups (P<0.001, all groups) (Table). Significantly greater reductions in CRP levels occurred with OZA vs PBO at W10 regardless of prior biologic or anti–tumor necrosis factor exposure (P<0.05, all groups). Reductions in CRP levels were maintained through W52; pts who continued OZA had similar reductions at W52 as pts who switched to PBO (Table). Decreases in CRP levels in W52 responders were maintained through W52 of treatment, while W52 nonresponder CRP levels started to rise following induction toward BL values (Table). BL CRP levels were significantly positively correlated with most BL disease activity scores (Spearman’s rho 0.2–0.3, P<0.05, all scores) except RBS. BL CRP levels and change in CRP levels at W5 were prognostic for W10 response. Higher BL CRP levels were associated with lower endoscopic improvement, mucosal healing, and histologic remission at W10 and greater reductions in CRP levels at W5 were associated with better clinical response at W10, independent of treatment. Change in CRP levels at W10 was significantly positively correlated with change in most disease activity scores at W10 with OZA and PBO (Spearman’s rho 0.1–0.3, P<0.05, all scores in both groups) except endoscopy with PBO.

Adjusted mean percent change from BL in CRP levels (95% CI)	Induction (Week 10)			Maintenance (Week 52)
	PBO (C1) (n=216)	OZA (C1) (n=429)	OZA (C2) (n=367)	OZA/PBO (n=227)	OZA/OZA (n=230)​​​​​​​​​
All pts	–1.2 (–13.3, 12.7) n=192	–26.8 (–33.3, –19.8) n=407	–25.7 (–32.7, –18.1) n=336	–19.9 (–30.9, –7.1) n=125	–23.5 (–32.7, –13.0) n=181
Respondersa	–39.9 (–52.5, –24.0) n=55	–45.9 (–52.2, –38.9) n=204	–40.4 (–47.5, –32.3) n=189	–33.1 (–44.1, –20.0) n=91	–30.9 (–40.4, –20.0) n=135
Nonrespondersb	12.0 (–3.5, 29.9) n=137	–10.7 (–20.9, 0.9) n=203	–7.4 (–19.8, 7.0) n=147	3.1 (–20.3, 33.4) n=34	–2.9 (–23.0, 22.6) n=46
aResponders are pts who achieved clinical response at W10 for the induction period and at W52 for the maintenance period. bNonresponders are pts who did not achieve clinical response at W10 for the induction period and at W52 for the maintenance period. C1, Cohort 1; C2, Cohort 2.

Conclusion

In moderately to severely active UC pts, OZA led to reductions in CRP levels. Higher BL CRP levels were associated with worse disease outcomes and greater reductions in CRP levels were associated with better disease outcomes regardless of treatment, supporting CRP levels as a prognostic biomarker for UC.

Disclosure

This study was funded by Bristol Myers Squibb, Princeton, NJ, USA
SH, RM, CW, YH, and AP: employees and/or shareholders of Bristol Myers Squibb.
DTR: received grant support from Takeda; consulted for AbbVie, AltruBio, Aslan Pharmaceuticals, Athos Therapeutics, Bellatrix Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chronicles, ClostraBio, Connect BioPharma, EcoR1, Eli Lilly, Genentech/Roche, Gilead Sciences, Iterative Health, Janssen, Kaleido Biosciences, Pfizer, Prometheus Biosciences, Reistone, Seres Therapeutics, Syneos, Takeda, Target RWE, and Trellus Health.

Introduction

Crohn's disease (CD) location would influence the risk of complications and therapeutic strategies. The objective of this study was to compare the clinical presentation at diagnosis and the natural history of colonic CD in comparison to ileal CD and ulcerative colitis (UC) in pediatric-onset IBD.

Aims & Methods

All children (< 17 years) with a diagnosis of CD or UC made between 1988 and 2011 in a population-based registry were followed retrospectively until 2013. The presentation at diagnosis, the risks of complications, surgery, hospitalization and exposure to different treatments in ileal CD (L1), colonic CD (L2) and UC were compared. Disease location was defined at diagnosis. In case of extension of the disease (L1 or L2 to L3), follow-up was censored.

Results

A total of 215 L1 patients, 234 L2 patients and 337 UC patients were included. At diagnosis, the proportion of male (L1 53.5%; L2 53.4%; UC 42.7%; p=0.012), age at diagnosis (15.0; 13.7; 14.0 years; p=0.003), family history of IBD (12.6%; 12.7%; 5.0%; p=0.005), diagnostic delay (3.0; 3.0; 2.0 months; p=0.001) and smoking prevalence (11.6%; 8.1%; 5.6%; p=0.041) were different between the three groups. Over the study period, incidence of L1, L2 and UC were 0.65 [0.57-0.74], 0.71 [0.62-0.81] and 1.02 [0.92-1.14] per 100,000 person-years, respectively. Bloody stool and diarrhea at diagnosis was observed in 14.9% respectively; 44.4%; and 91.1% (p < 0.001) and 47.0%; 71.8%; and 65.0% (p < 0.001) of L1, L2 and UC. At diagnosis, granuloma was identified in 12.6% of L1 patients and 31.2% of L2 patients (p<0.001). The risk of extension to L3 was significantly higher in the L2 group than in the L1 group (at 5 years - 37% vs. 14%, p<0.001). L2 location was associated with a lower risk of luminal fistula (HR 0.41 [0.21-0.79], p=0.008) but a higher risk of anoperineal lesion (HR 2.11 [1.29-3.45], p=0.003). The prevalence of extra-intestinal manifestations, articular (p<0.001) and cutaneous (p<0.001) was higher in L2 CD. While the risk of surgery was significantly higher in case of L1 location (at 5 years - 37%, 13% and 13%; p<0.001), the risk of hospitalization (at 5 years - 22%, 39% and 33%; p=0.01), exposure to corticosteroids (at 5 years - 55%, 69% and 67%; p<0.001), immunosuppressants (at 5 years - 47%, 61% and 42% - p<0.001) and anti-TNF (at 5 years - 16%, 35% and 22%; p<0.001) were higher in case of L2 location.

Conclusion

The clinical presentation and evolution of ileal and colonic CD at diagnosis differ significantly. Colonic location is associated with a high risk of perianal CD, extra-intestinal manifestations, hospitalization and exposure to steroids, immunosuppressants and anti-TNFs. These differences could justify different therapeutic approaches in the future.

Introduction

Mirikizumab [(miri), a p19-directed IL-23 antibody] has been shown to be effective in patients with moderately to severely active ulcerative colitis (UC) in Phase 3, randomised, double-blind, placebo-controlled trials (LUCENT-1 NCT03518086; LUCENT-2 NCT03524092).¹ Given the importance of understanding treat-to-target strategies, the relationship between improved histologic and endoscopic endpoints and improvement of the inflammatory biomarkers faecal calprotectin (fCal) and C-reactive protein (CRP) was studied within miri-treated patients enrolled in the programme.

Aims & Methods

This analysis focused on miri-treated patients (n=868) from the induction study receiving intravenous (IV) every 4 weeks (Q4W) until week (W)12 and miri induction responders at week W12, who were rerandomised for the maintenance period, receiving subcutaneous miri (n=365) Q4W up to W52. The relationship between achieving histologic-endoscopic mucosal improvement (HEMI), histologic-endoscopic mucosal remission (HEMR) (definitions in Tables) and improvement of fCal (≤250µg/g) and CRP (≤6mg/L) levels at W12 and W52 was explored using Fisher’s exact tests.

Results

At W12, a significantly higher percentage of miri-treated patients achieving HEMI (n=235/868) had normalised fCal and CRP values (76.2% and 88.5%), compared to miri-treated patients who did not achieve HEMI (n=633/868) (21.8% and 72.7%, both p<0.001; Table). Similarly, a higher proportion of patients achieving HEMR (n=193/868) had normalised fCal and CRP values (79.3% and 89.6%), compared to those who did not achieve HEMR (n=675/868) (24.3% and 73.3%, both p<0.001; Table). At W52, a significantly higher percentage of the miri induction responder patients rerandomised to miri achieving HEMI (174/365) had normalised fCal and CRP values (74.1% and 87.9%), compared to patients not achieving HEMI (191/365) (32.5% and 61.3%; both p<0.001). Similarly, at W52, a higher proportion of patients achieving HEMR (n=158/365) had normalised fCal and CRP values (76.6% and 88.0%), versus patients not achieving HEMR (n=207/365) (33.8% and 63.3%; both p<0.001, Table).

Relationship between mirikizumab treated patients achieving HEMI and HEMR at end of induction (W12) and normalised faecal calprotectin and C-reactive protein values				Relationship between mirikizumab responder patients rerandomised to mirikizumab achieving HEMI and HEMR at end of maintenance (W52) and normalised faecal calprotectin and C-reactive protein values
Mirikizumab treated patients (N=868)				Mirikizumab induction responder patients rerandomised to mirikizumab (n=365)
End of induction- Week 12	Patients achieving HEMI (n=235)	Patients not achieving HEMI (n=633)	p-value	End of maintenance- Week 52	Patients achieving HEMI (n=174)	Patients not achieving HEMI (n=191)	p-value
% fCal ≤ 250µg/g	179 (76.2%)	138 (21.8%)	<0.001	% fCal≤250µg/g	129 (74.1%)	62 (32.5%)	<0.001
% CRP ≤ 6mg/L	208 (88.5%)	460 (72.7%)	<0.001	% CRP≤6mg/L	153 (87.9%)	117 (61.3%)	<0.001
End of induction- Week 12	Patients achieving HEMR (n=193)	Patients not achieving HEMR (n=675)	p-value	End of maintenance-Week 52	Patients achieving HEMR (n=158)	Patients not achieving HEMR (n=207)	p-value
% fCal ≤ 250µg/g	153 (79.3%)	164 (24.3%)	<0.001	% fCal≤250µg/g	121 (76.6%)	70 (33.8%)	<0.001
% CRP ≤ 6mg/L	173 (89.6%)	495 (73.3%)	<0.001	% CRP≤6mg/L	139 (88.0%)	131 (63.3%)	<0.001
Abbreviations: CRP=C-reactive protein; fCal=faecal calprotectin; HEMI=Histologic-endoscopic mucosal improvement defined as Mayo Endoscopic Subscore (ES)=0 or 1 (excluding friability) and Geboes≤3.1; HEMR Histologic-endoscopic mucosal remission defined as ES=0 or 1 (excluding friability) + Geboes≤2b; N=patient population; n=patient subpopulation; W=week

Conclusion

At both W12 and W52, patients treated with miri who achieved HEMI or HEMR showed statistically significant improvements in fCal and CRP levels. This suggests that CRP and fCal may be useful markers of histology and endoscopy outcomes after induction and maintenance with miri.

References

1. Magro et al., MP245 Efficacy of mirikizumab in resolving active histologic inflammation in Ulcerative Colitis in Lucent-1 induction and Lucent-2 maintenance trials (2022), UEG Week Moderated Posters. United European Gastroenterol J, 10: 185-472.

Disclosure

R. Panaccione has received speaker fees and/or served as a consultant and/or advisory board member for: Abbott, AbbVie, Alimentiv, Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Cosmo Pharmaceuticals, Eisai, Elan Pharma, Eli Lilly and Company, Ferring Pharmaceuticals, Galapagos NV, Genentech, Gilead Sciences, GlaxoSmithKline, Janssen, Merck, Mylan, Oppilan Pharma, Pandion Therapeutics, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda, Theravance Biopharma and UCB Pharma; and has received research and/or educational support from: AbbVie, Ferring Pharmaceuticals, Janssen, Pfizer and Takeda; C. Sapin, F. Chan-Diehl and R. Moses are employees and shareholders of: Eli Lilly and Company;
B. Siegmund has served as a consultant and/or speaker for: AbbVie, Arena Pharmaceuticals, Bristol Myers Squibb, Boehringer Ingelheim, CED Service GmbH, Celgene, Dr Falk Pharma, Eli Lilly and Company, Ferring Pharmaceuticals, Galapagos NV, Janssen, Novartis, Pfizer, Prometheus Therapeutics and Takeda;
A. Walsh has received grant and/or research support from: AbbVie and Pfizer; and has received consultancy fees from: AbbVie, Eli Lilly and Company, Novartis and UCB Pharma;
T. Kobayashi has served as a speaker, consultant and advisory board member for: AbbVie, Ajinomoto Bio-Pharma, Alfresa Pharma, Asahi Kasei Pharma, Astellas, Bristol Myers Squibb, Celltrion, Covidien, EA Pharma, Eisai, Eli Lilly and Company, Ferring Pharmaceuticals, Galapagos NV, Gilead Sciences, Janssen, JIMRO, Kyorin, Mitsubishi Tanabe Pharma, Mochida Pharmaceutical, Nippon Kayaku, Pfizer, Takeda, Thermo Fisher Scientific and Zeria Pharmaceutical; and has received research funding from: AbbVie, ActivAid, Alfresa Pharma, Asahi Kasei Pharma, EA Pharma, Google, JMDC, Kyorin, Mochida Pharmaceutical, Nippon Kayaku, Otsuka, Sekisui Medical, Thermo Fisher Scientific and Zeria Pharmaceutical;
P. S. Dulai has served as a consultant for: AbbVie, Abivax, Addiso, Bristol Myers Squibb, Eli Lilly and Company, Janssen, Pfizer, Precidiag, Prometheus and Takeda;
S. Travis has received grants from: AbbVie, BUHLMANN Diagnostics, ECCO, Eli Lilly and Company, Ferring Pharmaceuticals, International Organization for the Study of Inflammatory Bowel Disease, Janssen, Merck Sharp & Dohme, Norman Collision Foundation, Pfizer, Procter & Gamble, Schering-Plough, Takeda, UCB Pharma, Vifor Pharma and Warner Chilcott; and reports other disclosures from: Abacus Pharma, AbbVie, Actial Farmaceutica, ai4gi, Alcimed, Allergan, Amgen, Aptel, Arena Pharmaceuticals, Asahi Kasei Pharma, Aspen Pharmacare, Astellas, AstraZeneca, Atlantic Pharmaceuticals, Barco, Biocare Medical, Biogen, BL Pharma, Boehringer Ingelheim, Bristol Myers Squibb, BUHLMANN Diagnostics, Calcico Therapeutics, Celgene, Cellerix, Cerimon Pharmaceuticals, ChemoCentryx, Chiesi, Cisbio, Comcast, Coronado Biosciences, Cosmo Pharmaceuticals, Dr Falk Pharma, Ducentis BioTherapeutics, Dynavax Technologies, Elan, Eli Lilly and Company, Enterome, Equillium, Ferring Pharmaceuticals, Galapagos NV, Genentech/Roche, Genzyme, Gilead Sciences, GlaxoSmithKline, Glenmark Pharmaceuticals, Grünenthal, GW Pharmaceuticals, Immunocore, Indigo Biosciences, Janssen, Lexicon Pharmaceuticals, Medarex, MedTrix, Merck, Merrimack Pharmaceuticals, Mestag Therapeutics, Millennium Pharmaceuticals, Neovacs, Novartis, Novo Nordisk, NPS Pharmaceuticals/Nycomed, Ocera Therapeutics, OPTIMA Pharma, Origin Pharma, Otsuka, Procter & Gamble, Palau Pharma, Pentax Medical, Pfizer, PharmaVentures, Phesi, Phillips Pharma Group, Pronota, Proximagen, Resolute, Robarts Clinical Trials, Sandoz, Santarus, Satisfai Health, Sensyne Health, Shire, Sigmoid Pharma, Sorriso Pharmaceuticals, Souffinez, SynDermix, Synthon, Takeda, Theravance Biopharma, TiGenix, Tillotts Pharma AG, Topivert, Trino Therapeutics with Wellcome Trust, TxCell, UCB Pharma, Vertex Pharma, VHsquared, Vifor Pharma, Warner Chilcott and Zeria Pharmaceutical

Introduction

Inflammatory bowel disease (IBD) is an idiopathic and chronic disorder that includes ulcerative colitis (UC) and Crohn’s disease (CD). Both diseases are different but show an uncontrolled intestinal immune response that generates tissue inflammation. Dendritic cells (DC) are phagocytic professional antigen presenting cells that link the innate and adaptive immune systems. Indeed, they are the only cell type able to stimulate naïve T cells generating an antigen-specific immune response. In humans, DC can be divided into plasmacytoid (pDC) and c conventional type 1 and 2 (cDC1, cDC2). Although cDC migration towards the GI mucosa is enhanced in IBD, the mechanisms underlying these migrations and whether biological drugs can modulate them are currently unknown.

Aims & Methods

This study aimed to analyze DC migratory capacity towards the GI mucosa, and whether biological drugs with different mechanisms of action can modulate this process in health and IBD.This is a cross-sectional observational postmarketing study where the modulatory effect of different biological treatments on samples from patients with an endoscopic diagnosis of IBD and healthy controls was studied ex vivo. Peripheral blood mononuclear cells (PBMC) were obtained from 15 patients per group and were cultured in presence of golimumab or ustekinumab. Following culture, surface markers expression was determined, assessing the differential effect of biological drugs (if any) over the DC subsets in the different groups. After that, the migratory capacity of circulating DC towards CCL2, CCL25, and MadCam1 from the different study groups was determined. Finally, we evaluated the DC migratory capacities in presence of biological drugs or with culture media alone.

Results

The cDC2 subset showed higher expression levels of β7 and CCR5 compared to cDC1. Golimumab and ustekinumab did not modify the expression of the homing markers in any studied subset in pDC and cDC2.On the contrary, in cDC1 both golimumab and ustekinumab increased CCR6 expression in qCD patients and CCR9 in aUC patients. Regarding to the migratory capacities of the different DC subsets toward intestinal chemoattractants ustekinumab decreased the migratory capacity of cDC2 from CD patients towards a medium supplemented with MadCam1 and increases toward CCL2 in case of cDC1 from CU. In the case of migratory capacities of cDC2 from HC were increased by ustekinumab and golimumab toward CCL2 and CCL25 just in case of golimumab.

Conclusion

cDC2 showed more migratory profile markers than other DC populations. Golimumab and ustekinumab modify the expression of the homing markers just in cDC1 subset. Nevertheless, ustekinumab and golimumab modify the migratory capacities of cDC in IBD patients and HC, but do not exert any effect on pDC. These effects occur in a different manner in CD and UC indicates that the behavior of cDC is different in CD and UC, and that the migration capacity of this subset is not dependent exclusively on homing markers.