{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T09:51:00Z","timestamp":1770371460664,"version":"3.49.0"},"reference-count":15,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Vedolizumab, a monoclonal antibody targeting the \u03b14\u03b27 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Landmark trials confirmed vedolizumab\u2019s 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\u20130.86). Neural networks have demonstrated an accuracy of approximately 79% in specific predictive contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec005\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The strategic integration of AI-driven predictive analytics with vedolizumab\u2019s clinical and pharmacodynamic data represents a pivotal next step towards achieving true precision medicine in UC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pdig.0001208","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T18:39:39Z","timestamp":1770316779000},"page":"e0001208","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing vedolizumab therapy in ulcerative colitis: A critical synthesis of trial evidence and the emerging role of artificial intelligence"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2325-5044","authenticated-orcid":true,"given":"Alfadl","family":"Abdulfattah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"issue":"3","key":"pdig.0001208.ref001","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1124\/jpet.109.153973","article-title":"The binding specificity and selective antagonism of vedolizumab, an anti-alpha4beta7 integrin therapeutic antibody in development for inflammatory bowel diseases","volume":"330","author":"D Soler","year":"2009","journal-title":"J Pharmacol Exp Ther"},{"issue":"8","key":"pdig.0001208.ref002","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1056\/NEJMoa1215734","article-title":"Vedolizumab as induction and maintenance therapy for ulcerative colitis","volume":"369","author":"BG Feagan","year":"2013","journal-title":"N Engl J Med"},{"issue":"13","key":"pdig.0001208.ref003","doi-asserted-by":"crossref","DOI":"10.1016\/j.cgh.2020.02.010","article-title":"Development and validation of clinical scoring tool to predict outcomes of treatment with vedolizumab in patients with ulcerative colitis","volume":"18","author":"PS Dulai","year":"2020","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"7","key":"pdig.0001208.ref004","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1111\/apt.14548","article-title":"Early vedolizumab trough levels predict mucosal healing in inflammatory bowel disease: a multicentre prospective observational study","volume":"47","author":"W Yacoub","year":"2018","journal-title":"Aliment Pharmacol Ther"},{"key":"pdig.0001208.ref005","unstructured":"US National Library of Medicine. Identification of predictors of response to vedolizumab therapy in patients with inflammatory bowel disease (PREDICT-VDZ). ClinicalTrials.gov Identifier: NCT04524624. [Internet]. Updated March 15, 2023. [cited 2025 Apr 19]. Available from: https:\/\/clinicaltrials.gov\/study\/NCT04524624"},{"issue":"2","key":"pdig.0001208.ref006","first-page":"223","article-title":"Artificial intelligence in inflammatory bowel disease: how machine learning and deep learning may improve clinical practice","volume":"164","author":"AN Ananthakrishnan","year":"2023","journal-title":"Gastroenterology"},{"issue":"3","key":"pdig.0001208.ref007","doi-asserted-by":"crossref","DOI":"10.1053\/j.gastro.2020.10.024","article-title":"Central reading of ulcerative colitis clinical trial videos using neural networks","volume":"160","author":"K Gottlieb","year":"2021","journal-title":"Gastroenterology"},{"issue":"12","key":"pdig.0001208.ref008","first-page":"2112","article-title":"Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis","volume":"13","author":"L Villanova","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"13","key":"pdig.0001208.ref009","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1056\/NEJMoa1905725","article-title":"Vedolizumab versus adalimumab for moderate-to-severe ulcerative colitis","volume":"381","author":"BE Sands","year":"2019","journal-title":"N Engl J Med"},{"issue":"4","key":"pdig.0001208.ref010","article-title":"Real-world treatment outcomes associated with early versus delayed vedolizumab initiation in patients with ulcerative colitis","volume":"6","author":"N Krugliak Cleveland","year":"2024","journal-title":"Crohns Colitis 360"},{"issue":"1","key":"pdig.0001208.ref011","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/apt.14075","article-title":"Systematic review: the safety of vedolizumab for the treatment of inflammatory bowel disease","volume":"46","author":"WA Bye","year":"2017","journal-title":"Aliment Pharmacol Ther"},{"issue":"10","key":"pdig.0001208.ref012","first-page":"2090","article-title":"Vedolizumab concentration levels and immunogenicity in inflammatory bowel disease","volume":"116","author":"B Bressler","year":"2021","journal-title":"Am J Gastroenterol"},{"issue":"2","key":"pdig.0001208.ref013","first-page":"321","article-title":"Performance of a deep learning model for predicting vedolizumab response in ulcerative colitis","volume":"73","author":"RW Stidham","year":"2024","journal-title":"Gut"},{"issue":"1","key":"pdig.0001208.ref014","first-page":"113","article-title":"Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis","volume":"34","author":"T Ozawa","year":"2022","journal-title":"Dig Endosc"},{"issue":"7758","key":"pdig.0001208.ref015","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/s41586-019-1237-9","article-title":"Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases","volume":"569","author":"J Lloyd-Price","year":"2019","journal-title":"Nature"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001208","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T18:39:42Z","timestamp":1770316782000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001208"}},"subtitle":[],"editor":[{"given":"Jing","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":15,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0001208","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]}}}