Introduction

Loss of response to anti-TNFα therapy is a significant challenge in the management of inflammatory bowel disease (IBD) patients. Identifying predictive biomarkers for this outcome could aid in treatment optimization.

Aims & Methods

This study aimed to evaluate the predictive value of serum γ-globulin and albumin concentrations for treatment failure to anti-TNFα therapy in patients with IBD.
This was a retrospective analytical study conducted at the Gastroenterology Department of Hedi Chaker University Hospital in Sfax. The study spanned a 16-year period from 2007 to 2023. All patients with inflammatory bowel disease (IBD) treated with anti-TNF therapy were included. Patients using anti-TNF therapy for extra-intestinal manifestations of IBD were excluded. Epidemiological data, characteristics of IBD, biological and endoscopic parameters, as well as fecal calprotectin levels, were collected. Serum albumin and γ-globulin levels were measured as part of the pre-anti-TNF assessment. Therapeutic evaluation followed the STRIDE-II strategy.

Results

​The study included a total of 116 patients, among whom 82 had Crohn's disease (CD) and 34 had ulcerative colitis (UC). The mean age at diagnosis was 30.91 ± 12.227 years for all patients, with slightly lower ages observed in CD patients (30.49 ± 13.137 years) compared to UC patients (31.94 ± 9.792 years).103 patients (88.8%) received infliximab as their first-line anti-TNF therapy while 11.2% patients were initially treated with adalimumab.
Table 1: Demographic, Clinical Characteristics, and Biological Parameters of IBD Patients

Parameter	Total (n=116)	Crohn's (n=82)	UC (n=34)
Age at First Anti-TNF Introduction (years, ± SD)	36.38 ± 13.355	35.96 ± 13.777	37.38 ± 12.417
Gender Male, n (%)	67 (57.8%)	46 (56.1%)	21 (61.8%)
Albuminemia - Number (%)	95 (81.9%)	65 (79.3%)	30 (88.2%)
- Albuminemia (g/l), Mean ± SD	33.484 ± 5.9794	33.269 ± 5.8248	33.95 ± 6.3787
- Hypoalbuminemia (<35 g/l), n (%)	26 (27.4%)	20 (30.8%)	6 (20%)
Gammaglobulinemia - Number (%)	87 (75%)	60 (73.2%)	27 (79.4%)
- Gammaglobulinemia (g/l), Mean ± SD	12.555 ± 3.902	12.48 ± 3.635	12.722 ± 4.5095
- Hypergammaglobulinemia (>13.5 g/l), n (%)	32 (36.8%)	22 (26.7%)	10 (37%)

The study observed a primary non-response rate of 24.1% (n=28) across all patients with IBD. This rate was significantly higher in patients with UC compared to those with CD (47.1% vs 14.6%; p < 0.001). Secondary loss of response was noted in 47.9% of cases (n=46), with a higher frequency observed among CD patients compared to UC patients (52% vs 33.3%), although this difference was not statistically significant. Regarding specific associations with treatment response, in patients with CD, primary non-response was significantly linked to the presence of hypergammaglobulinemia (>13.5g/l) (p=0.009). In contrast, for patients with UC, secondary loss of response was notably associated with moderately elevated gammaglobulinemia levels (p=0.039). Notably, there was no significant association observed between albumin levels and treatment response in either patient group.

Conclusion

this study highlights the potential utility of serum biomarkers, particularly gammaglobulinemia, in predicting response to anti-TNFα therapy in IBD patients. Further research in this area could contribute to personalized treatment strategies aimed at improving therapeutic outcomes in this challenging patient population.

Disclosure

no conflit

Introduction

The therapeutic goal in inflammatory bowel disease (IBD), which includes Crohn´s disease (CD) and ulcerative colitis (UC), is the control of the inflammatory process frequently through biologic and JAK-inhibitor therapies, which are generally expensive and can cause adverse effects. Unfortunately, only about one-third of patients respond to these treatments, and there are no markers that accurately predict this response.

Aims & Methods

The aim of this study was to identify potential serum and urinary metabolic biomarkers predictive of IBD response to different drugs at baseline (before treatment), which could facilitate the selection of the most appropriate therapy for each patient. For this purpose, a prospective multicenter study was conducted including CD and UC patients who initiated biologic (anti-TNF, ustekinumab or vedolizumab) or JAK-inhibitor (tofacitinib) therapies. Patients were assessed at 14 weeks to determine if they had achieved an endoscopic response, based on the SES-CD and on the endoscopic subscore of the Partial Mayo Score. The analyses of the serum and urine metabolome, along with the deconvolution of the serum lipoprotein profile, were performed using nuclear magnetic resonance for a quantitative differential analysis comparing the baseline serum samples of responders and non-responders.

Results

A total of 150 patients were included (30 CD anti-TNF, 30 UC anti-TNF, 30 CD ustekinumab, 30 UC vedolizumab and 30 UC tofacitinib). Metabolomic analysis identified 55 potential predictive biomarkers of IBD treatment response with significantly altered levels (p<0.05) in the baseline urine and serum samples of subsequent responders compared to non-responders. Among them, 44 were drug-specific and none were shared between serum and urine. Moreover, based on ROC curve analysis, some of these biomarkers showed good sensitivity and specificity for distinguishing between responders and non-responders before treatment (Table 1). Finally, the integrated metabolite set enrichment analysis of urine and serum samples identified distinct biological pathways associated with the response to each treatment for IBD, with common pathways highlighted, including the metabolism of short-chain fatty acids, the citrate cycle and the metabolism of amino acids such as alanine, aspartate, glutamate, and arginine.

Conclusion

The present study represents a step towards personalized medicine in the selection of targeted therapies for IBD patients, addressing an important unmet need. Although further validation is needed, we have identified serum and urinary metabolic biomarkers with the potential to predict response to biologic and JAK-inhibitor therapies.

Table 1. Summary of potential metabolic biomarkers identified as predictive of endoscopic response before each IBD treatment.

*Biomarkers with significantly altered levels (p<0.05) in baseline serum or urine samples of responders vs non-responders. **AUC corresponding to the biomarkers with the best sensitivity and specificity for distinguishing between responder and non-responder patients before treatment. AUC: Area Under the Curve calculated using the Metabolanalyst software in univariate analysis; CD: Crohn's disease; IBD: Inflammatory bowel disease; UC: Ulcerative colitis.

Introduction

The complexity of Inflammatory Bowel Diseases (IBD) presents challenges for the management of these diseases. Predicting treatment outcomes remains difficult, leading to suboptimal outcomes and high costs. Emerging evidence suggests the potential of the gut microbiome in predicting response to biologic treatments (1,2). In this prospective study we aimed to predict treatment response to vedolizumab and ustekinumab in 79 IBD patients by integrating clinical data, gut microbiome profiles and fecal metabolites and validating these findings in a replication cohort of 47 IBD patients. 

Aims & Methods

Treatment response was defined as either continuation or discontinuation of biologic therapy after six months. We performed whole genome metagenomic shotgun sequencing on the baseline fecal samples to identify microbial and functional profiles. Additionally, abundances of over 1000 metabolites were captured through untargeted metabolomic profiling. Baseline diversity, dissimilarity and differential abundance analyses compared responder and non-responder groups. The prediction tool CoDaCoRe was used to identify predictive log-ratio biomarkers. We tested the identified ratios in an external cohort and attempted to replicate the microbiome-based signals of previous studies. Finally, we used a neural-network framework to model the relationship between metabolites and microbes, comparing identified clusters in responders and non-responders and tested our approach with different definitions of response.

Results

We identified 7 differentially abundant metabolites between responders and non-responders (FDR < 0.05), and no significant differences in bacterial species or pathways. Our prediction analysis indicated only marginal predictive utility of the gut microbiome and fecal metabolites for treatment response, when compared to a clinical model using fecal calprotectin, disease duration and disease activity, among other factors (AUC only clinical features: 0.71±0.13, AUC microbial and clinical features: 0.73±0.12). The main predictive features of these models were the high abundance Phocaeicola vulgatus, Bacteroides uniformis and Alistipes onderdonkii, and low abundance of Ruminococcus gnavus and Faecalibacterium prausnitzii. Testing our identified ratio of 10 species in an external cohort of 47 IBD patients reinforced the lack of predictive power of the microbiome. No replication of previously published predictive signals of the microbiome was observed. Additionally, we identified two metabolite and one microbiome cluster associated with response, and observed that predictors were highly dependent on the definition of response.

Conclusion

While previous studies of similar size have shown that microbial features can predict response to either vedolizumab (VedoNet, 1), or vedolizumab, ustekinumab and anti-TNF (2), our comprehensive study found no significant differences in the gut microbiome at baseline between responders and non-responders among IBD patients treated with ustekinumab or vedolizumab. Microbial features added no predictive power to drug response in our IBD patient cohort or an independent replication cohort. Addition of metabolite features did not improve predictive power. These findings suggest minimal impact of the gut microbiome on treatment outcomes with these medications in IBD patients at this disease stage. Generalizability beyond initial study cohorts is limited, leaving predictors for individualized IBD-medication selection unidentified, but based on this work, likely do not lie in the fecal microbiome.

References

1. Ananthakrishnan AN, Luo C, Yajnik V, et al. Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases. Cell Host Microbe. 2017;21(5):603-610.e3. doi:10.1016/j.chom.2017.04.010

2. Caenepeel C, Falony G, Machiels K, et al. Dysbiosis and Associated Stool Features Improve Prediction of Response to Biological Therapy in Inflammatory Bowel Disease. Gastroenterology. 2024;166(3):483-495. doi:10.1053/j.gastro.2023.11.304

Disclosure

EAMF is supported by a ZonMW Clinical Fellowship grant (project number 90719075) and has received an unrestricted research grant from Takeda. RG received funding by Janssen Pharmaceuticals (for unrelated research projects), received consulting funding from Esox Biologics (for unrelated research projects). RKW has received unrestricted Research Grants from Takeda, Johnson & Johnson, Ferring and Tramedico and speakers fees from Abbvie, MSD and Boston Scientific and has acted as a consultant for Takeda Pharmaceuticals. BO has received grants from MSD, AbbVie, Takeda, Cablon, Ferring, Falk, and Pfizer.

Introduction

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the digestive tract. The introduction of targeted therapies has significantly improved patient outcomes, although a substantial proportion of patients still do not respond adequately. Genetic factors, including single nucleotide polymorphisms (SNPs), may influence therapeutic efficacy.

Aims & Methods

This study investigated the potential correlation between four SNPs in four cytokine genes and the response to targeted molecular therapies.
This prospective cohort study enrolled 197 patients with IBD (144 with Crohn’s disease CD and 55 with ulcerative colitis UC) receiving molecularly targeted therapies. The following SNPs were analyzed: TGF-β -cod10 T/C (rs1982073), IL-6 -174 G/C (rs1800795), TNF-α -308 G/A (rs1800629), and IL-10 -1082 G/A (rs1800896). These SNPs were assessed in relation to baseline patient characteristics. Therapeutic response to molecularly targeted therapies (adalimumab, infliximab, ustekinumab, vedolizumab and tofacitinib) was evaluated in two cohorts: 134 patients in the per-protocol analysis (who maintained the drug for 12 months) and 155 patients in the intention-to-treat analysis. Response was also assessed in the cohort receiving anti-TNF agents (infliximab and adalimumab), including 73 patients in the per-protocol cohort and 87 in the intention-to-treat cohort. After 12 months, response was defined as biochemical remission (absence of steroid therapy, CRP <5 mg/L, and fecal calprotectin <250 µg/g).

Results

The TNF-α -308 mutated allele was more frequently associated with CD than UC (the GA polymorphism was present in 33% of patients with CD and in 21% of patients with UC, while the AA polymorphism was present only in patients with CD, at 4.3%; p = 0.020). The wild-type IL-6 -174 SNP was associated with an older age at diagnosis, with a median age of 30y (22-41) vs 25y (19-36) (p = 0.049) and showed a partial association with a lower history of surgical interventions (p = 0.076). In the cohort of patients who had calprotectin and CRP values at T12, only the mutated IL-10 -1082 SNP was significantly associated with response in the per-protocol cohort (73.4% of patients who did not respond to therapy had the wild-type genotype; p = 0.041) [OR = 2.14 (95% CI 1.03–4.44)] and showed a trend toward significance in the intention-to-treat cohort (p = 0.053). Regarding anti-TNF therapy response, the mutated IL-10 -1082 SNP was associated with better therapeutic outcomes in the per-protocol cohort (84.8% of patients who did not respond to therapy had the wild-type genotype; p = 0.034) [OR = 3.36 (95% CI 1.07–10.58)], but not in the intention-to-treat cohort (p = 0.103). Additionally, a statistically significant correlation was found between TNF-α -308 SNP genotypes (wild-type, heterozygous mutated, and homozygous mutated) and response to anti-TNF therapy in the per-protocol cohort (p = 0.040) [OR = 2.82 (95% CI 1.01–7.85)].

Conclusion

Although four cytokine SNPs were analyzed, the most significant associations were observed with the IL-6 polymorphism, where the mutation was linked to a more aggressive disease course, and the IL-10 cytokine SNP, where the CC+GC genotypes appeared to be associated with a better response to target therapies.

Introduction

Biologic drugs are key in the current treatment of patients with inflammatory bowel disease (IBD). Identifying patients at high risk of primary non-response or loss of response is key to individualize treatment

Aims & Methods

Aim: To identify risk factors associated with primary nonresponse, loss of response and adverse effects to the first biologic drug used in patients with IBD.
Methoids: Single-centre retrospective cohort study including all consecutive patients with IBD naïve to biologic treatment who have started biologic treatment from 2000 to January 2022. A minimum follow-up of 6 months from the start of the biologic was required. Only the first biologic used in each patient was evaluated. HLA-DQA1 genotyping was done in all patients. Primary response rate was assessed at 24 weeks. Regression analyses were used to assess risk factors. The possible risk factors evaluated were sex, HLADQA1 genotype, smoking at diagnosis, body mass index, age at diagnosis, disease type, location, extent and phenotype disease according to Montreal classification, type biologic drug, disease severity at biologic initiation (Mayo index and Harvey Bradshaw index), combotherapy (≥ 6 months), and history of IBD resective surgery.

Results

A total of 421 patients were included, 290 with Crohn's disease and 131 with Ulcerative Colitis. 51.1% were male. The mean age at biologic initiation was 39 years. The most commonly used biologic was infliximab (IFX) (50.1%), followed by adalimumab (ADA) (46.8%). 46.1% carried HLADQA1*05 allele/s and 24.2% carried HLADQA1*03 allele/s. Almost half of the patients (47.7%) were treated with combotherapy ( biologic + inmmunomodulator). The primary response rate was high: 84.8% (IFX 82.5%, ADA 87.8%). The loss of response rate was 33.3% (IFX 29.9%, ADA 35.5%), with a mean time of 5 years. Adverse effects occurred in 22.1% of patients (IFX 28.9%, ADA 16.2%). In multivariate analysis, carrying HLADQA1*05 allele was the only factor of all evaluated that was associated with an increased risk of loss of response [Odds Ratio: 1.83 (1.16-2.87), p=0.009], while carrying an HLADQA1*03 allele was protective for loss of response [Odds ratio: 0.37 (0.15- 0.89), p=0.026]. Female sex [Odds ratio: 1.61 (1.04-2.49), p=0.032)], the use of infliximab [Odds ratio: 2.00 (1.28-3.14), p=0.003] and the use of higher than standard doses in the IFX induction regimen [Odds Ratio: 3.28 (1.10-9.81), p=0.034] were identified as risk factors for the development of adverse effects.

Conclusion

1) Our results support that carrying HLADQA1*05 increases the risk of secondary loss of response to anti-TNF drugs and suggest that carrying HLADQA1*03 decreases this risk.
2) Female sex is an independent risk factor for the development of adverse effects to anti-TNF drugs.
3) Infliximab is the biologic drug with the highest rate of adverse effects and the use of higher than standard doses during induction is associated with an increased risk of adverse effects.

Introduction

Proactive therapeutic drug monitoring (TDM) has been suggested to improve outcomes in patients with inflammatory bowel disease (IBD) treated with anti-tumour necrosis factor (anti-TNFα) therapy. A systematic review and a meta-analysis of the literature was conducted to assess treatment efficacy and safety, and a systematic review to compare the cost-effectiveness of proactive TDM versus conventional management during maintenance treatment with anti-TNFα.

Aims & Methods

A search was conducted of MEDLINE, EMBASE, and the Cochrane Library up to January 2022. The primary outcome was the ability to maintain clinical remission at 12 months. The certainty of evidence was determined using the GRADE approach.

Results

Nine studies were identified: one systematic review, six randomised clinical trials, and two cohort studies (for safety evaluation). The analysis of the clinical trials showed no superior efficacy of proactive TDM [relative risk 1.16; 95% confidence interval (CI): 0.98–1.37, n=528; I²=55%]. In contrast, the analysis of the cohort studies and systematic review revealed that proactive TDM could improve the durability of anti-TNFα treatment [odds ratio (OR) 0.12; 95%CI: 0.05–0.27; n=390; I²=45%), prevent acute infusion reactions (OR 0.21; 95%CI: 0.05–0.82; n=390; I²=0%), decrease adverse events (OR 0.38; 95%CI: 0.15–0.98; n=390; I²=14%), and reduce the probability of surgery, at lower economical expenditure.

Conclusion

The analysed evidence did not confirm the superiority of proactive TDM of anti-TNFα agents over conventional management. Therefore, proactive TDM should not be recommended for the management of IBD patients receiving maintenance anti-TNFα treatment.

References

1. Vande Casteele N, et al. Trough concentrations of infliximab guide dosing for patients with inflammatory bowel disease. Gastroenterology. 2015;148(7):1320-1329.
2. D’Haens G, et al. Increasing Infliximab Dose Based on Symptoms, Biomarkers, and Serum Drug Concentrations Does Not Increase Clinical, Endoscopic, and Corticosteroid-Free Remission in Patients With Active Luminal Crohn’s Disease. Gastroenterology. 2018;154(5):1343-1351.
3. Assa A, et al. Proactive Monitoring of Adalimumab Trough Concentration Associated With Increased Clinical Remission in Children With Crohn’s Disease Compared With Reactive Monitoring. Gastroenterology. 2019;157(4):985-996.
4. Syversen SW, et al. Effect of Therapeutic Drug Monitoring vs Standard Therapy During Maintenance Infliximab Therapy on Disease Control in Patients With Immune-Mediated Inflammatory Diseases: A Randomized Clinical Trial. JAMA. 2021;326(23):2375-84.
5. Strik AS, et al. Efficacy of dashboard driven dosing of infliximab in inflammatory bowel disease patients; a randomized controlled trial. Scand J Gastroenterol. 2021;56(2):145-54.
06. D’Haens GR, et al. Higher vs Standard Adalimumab Induction Dosing Regimens and Two Maintenance Strategies: Randomized SERENE CD Trial Results. Gastroenterology. 2022;162(7):1876-90.
07. Papamichael K, et al. Improved Long-term Outcomes of Patients With Inflammatory Bowel Disease Receiving Proactive Compared With Reactive Monitoring of Serum Concentrations of Infliximab. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2017;15(10):1580-1588.e3.

Disclosure

B Novella Arribas, F Rodríguez Salvanés, and P Loeches Belinchón have no conflict of interest. N Manceñido Marcos has served as a speaker and consultant for or received financial support for educational activities from Janssen, Abbvie, Pfizer, Takeda, Ferring, Faes Farma, Dr. Falk Pharma, and Tillots Pharma. G Mora Navarro has served as a speaker and consultant for or has received research and training funding from Sanofi, Lilly, ESTEVE, and AstraZeneca. JP Gisbert has served as a speaker, consultant, and advisory member for or has received research funding from MSD, Abbvie, Hospira, Pfizer, Kern Pharma, Biogen, Takeda, Janssen, Roche, Sandoz, Celgene, Ferring, Faes Farma, Shire Pharmaceuticals, Dr. Falk Pharma, Tillotts Pharma, Chiesi, Casen Fleet, Gebro Pharma, Otsuka Pharmaceutical, and Vifor Pharma.