{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T02:38:00Z","timestamp":1790735880292,"version":"4.1.0"},"reference-count":56,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"crossref","award":["Hartree National Centre for Digital Innovation"],"award-info":[{"award-number":["Hartree National Centre for Digital Innovation"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000360","name":"Scottish Funding Council","doi-asserted-by":"publisher","award":["Precision Medicine Scotland Innovation Centre (PMS-IC)"],"award-info":[{"award-number":["Precision Medicine Scotland Innovation Centre (PMS-IC)"]}],"id":[{"id":"10.13039\/501100000360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn\u2019s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models\u2019 predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in\n                    <jats:italic>ex vivo<\/jats:italic>\n                    patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNF\u03b1 from the fresh IBD tissues in the presence\/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNF\u03b1 levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by\n                    <jats:italic>ex vivo<\/jats:italic>\n                    assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0263248","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T13:38:29Z","timestamp":1645623509000},"page":"e0263248","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":38,"title":["Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9177-4452","authenticated-orcid":true,"given":"Laura-Jayne","family":"Gardiner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Paola","family":"Carrieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Bingham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graeme","family":"Macluskie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9262-1696","authenticated-orcid":true,"given":"David","family":"Bunton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marian","family":"McNeil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward O.","family":"Pyzer-Knapp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"issue":"4","key":"pone.0263248.ref001","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3390\/jpm6040020","article-title":"Precision Health Economics and Outcomes Research to Support Precision Medicine: Big Data Meets Patient Heterogeneity on the Road to Value","volume":"6","author":"Y Chen","year":"2016","journal-title":"J Pers Med"},{"key":"pone.0263248.ref002","doi-asserted-by":"crossref","first-page":"239","DOI":"10.4103\/0975-7406.160040","article-title":"Perspective: Does personalized medicine hold the future for medicine?","volume":"7","author":"AA Agyeman","year":"2015","journal-title":"J Pharm Bioallied Sci"},{"key":"pone.0263248.ref003","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/nrd3078","article-title":"How to improve RD productivity: The pharmaceutical industry\u2019s grand challenge","author":"SM Paul","year":"2010","journal-title":"Nature Reviews Drug Discovery"},{"key":"pone.0263248.ref004","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1038\/nrd3557","article-title":"Quantifying factors for the success of stratified medicine","volume":"10","author":"MR Trusheim","year":"2011","journal-title":"Nat Rev Drug Discov"},{"key":"pone.0263248.ref005","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1038\/nrd4309","article-title":"Lessons learned from the fate of AstraZeneca\u2019s drug pipeline: a five-dimensional framework","volume":"13","author":"D Cook","year":"2014","journal-title":"Nat Rev Drug Discov"},{"issue":"9\u201310","key":"pone.0263248.ref006","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.drudis.2011.12.020","article-title":"Can the flow of medicines be improved? 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