Optimizing vedolizumab therapy in ulcerative colitis: A critical synthesis of trial evidence and the emerging role of artificial intelligence

Alfadl Abdulfattah

PLOS Digital Health, 2026, vol. 5, issue 2, 1-8

Abstract: Background: Vedolizumab, a monoclonal antibody targeting the α4β7 integrin, offers gut-selective immunosuppression and represents a cornerstone biologic therapy for moderate-to-severe ulcerative colitis (UC). While pivotal randomized controlled trials (RCTs) have established its efficacy, a substantial subset of patients experience primary non-response. This variability presents significant clinical challenges, including patient morbidity and healthcare costs associated with cycling through ineffective therapies, underscoring an urgent need for personalized treatment strategies. Objectives: This review aims to critically reappraise the foundational RCT evidence supporting vedolizumab use in UC, examining both strengths and limitations, and providing a comprehensive analysis of how artificial intelligence (AI), particularly machine learning (ML), can be leveraged to optimize vedolizumab treatment selection, predict outcomes, and personalize management. Methods: A systematic literature search was performed across PubMed, Scopus, and Web of Science databases. The review synthesized data from key Phase III trials (GEMINI 1, VARSITY), long-term extension safety studies, relevant meta-analyses summarizing efficacy and safety, and pertinent studies investigating the application of AI and ML techniques within inflammatory bowel disease management. The search included terms such as vedolizumab, UC, AI, and predictive modeling. Findings: Landmark trials confirmed vedolizumab’s superiority over placebo for inducing and maintaining remission, with week 52 clinical remission rates reaching 41.8% in the GEMINI 1 trial. Concurrently, emerging AI/ML models, integrating complex patient data, show considerable promise in predicting biologic response with high accuracy, with some models achieving an area under the curve (AUC) of 0.82 (95% CI 0.78–0.86). Neural networks have demonstrated an accuracy of approximately 79% in specific predictive contexts. Conclusions: The strategic integration of AI-driven predictive analytics with vedolizumab’s clinical and pharmacodynamic data represents a pivotal next step towards achieving true precision medicine in UC.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001208

DOI: 10.1371/journal.pdig.0001208

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