Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn’s Disease Using Transcriptome Imputed from Genotypes

Early Progression Prediction in Korean Crohn's Disease Using a Korean-Specific PrediXcan Model

Crohn's disease (CD) is a chronic inflammatory disorder with potential progression to stricturing (B2) or penetrating (B3) phenotypes, leading to significant complications. Early identification of patients at risk for these complications is critical for personalized management. This study aimed to develop a predictive model using clinical data and a Korean-specific transcriptome-wide association study (TWAS) to forecast early progression in CD patients. A retrospective analysis of 430 Korean CD patients from 15 hospitals was conducted. Genotyping was performed using the Korea Biobank Array, and gene expression predictions were derived from a TWAS model based on terminal ileum data. Logistic regression models incorporating clinical and gene expression data predicted progression to B2 or B3 within 24 months of diagnosis. Among the cohort, 13.9% (60 patients) progressed to B2 and 16.9% (73 patients) to B3. The combined model achieved mean area under the curve (AUC) values of 0.788 for B2 and 0.785 for B3 progression. Key predictive genes for B2 included CCDC154, FAM189A2, and TAS2R19, while PUS7, CCDC146, and MLXIP were linked to B3 progression. This integrative model provides a robust approach for identifying high-risk CD patients, potentially enabling early, targeted interventions to reduce disease progression and associated complications.

Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review

Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials-from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multi-omics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care.

Timing of proper introduction, optimization and maintenance of anti-TNF therapy in IBD: Results from a Delphi consensus

BACKGROUND

Crohn's disease and ulcerative colitis are inflammatory bowel diseases (IBDs) with a rapidly growing worldwide incidence. The last decades presented rapid progress in pharmacological treatment leading in many cases to clinical and endoscopic remission, including biological treatment with anti-TNF agents.

AIM

The exact timing of introduction, optimization and maintenance of anti-TNF therapy in IBDs is not thoroughly covered in current guidelines.

METHODS

We used the Delphi panel methodology to gather the IBD experts' views and achieve consensus for clinical recommendations on introducing and maintaining anti-TNF therapy for patients with IBDs.

RESULTS

Twelve recommendations achieved a high level of consensus in two assessment rounds by 52 (1st round) and 47 (2nd round) IBD experts.

CONCLUSION

In many clinical situations, the early use of anti-TNF therapy is recommended. Nowadays, the cost-efficacy profile of anti-TNF biosimilars makes them the first-line drug in a substantial proportion of patients, thus providing the opportunity to increase access to biological therapy.

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

INTRODUCTION

Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician-patient communication.

AREAS COVERED

This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included.

EXPERT OPINION

Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Inflammatory bowel diseases (IBD) encompass two main entities including ulcerative colitis and Crohn's disease. Although having a common global pathophysiological mechanism, IBD patients are characterized by a significant interindividual heterogeneity and may differ by their disease type, disease locations, disease behaviours, disease manifestations, disease course as well as treatment needs. Indeed, although the therapeutic armamentarium for these diseases has expanded rapidly in recent years, a proportion of patients remains with a suboptimal response to medical treatment due to primary non-response, secondary loss of response or intolerance to currently available drugs. Identifying, prior to treatment initiation, which patients are likely to respond to a specific drug would improve the disease management, avoid unnecessary side effects and reduce the healthcare expenses. Precision medicine classifies individuals into subpopulations according to clinical and molecular characteristics with the objective to tailor preventative and therapeutic interventions to the characteristics of each patient. Interventions would thus be performed only on those who will benefit, sparing side effects and expense for those who will not. This review aims to summarize clinical factors, biomarkers (genetic, transcriptomic, proteomic, metabolic, radiomic or from the microbiota) and tools that could predict disease progression to guide towards a step-up or top-down strategy. Predictive factors of response or non-response to treatment will then be reviewed, followed by a discussion about the optimal dose of drug required for patients. The time at which these treatments should be administered (or rather can be stopped in case of a deep remission or in the aftermath of a surgery) will also be addressed. IBD remain biologically complex, with multifactorial etiopathology, clinical heterogeneity as well as temporal and therapeutic variabilities, which makes precision medicine especially challenging in this area. Although applied for many years in oncology, it remains an unmet medical need in IBD.