Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease.

Autor: Gardiner LJ; IBM Research Europe-Daresbury, The Hartree Centre, Warrington, United Kingdom., Carrieri AP; IBM Research Europe-Daresbury, The Hartree Centre, Warrington, United Kingdom., Bingham K; REPROCELL Europe Ltd, Glasgow, Scotland, United Kingdom., Macluskie G; REPROCELL Europe Ltd, Glasgow, Scotland, United Kingdom., Bunton D; REPROCELL Europe Ltd, Glasgow, Scotland, United Kingdom., McNeil M; Precision Medicine Scotland Innovation Centre, Teaching and Learning Building, Queen Elizabeth University Hospital, Glasgow, Scotland, United Kingdom., Pyzer-Knapp EO; IBM Research Europe-Daresbury, The Hartree Centre, Warrington, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Feb 23; Vol. 17 (2), pp. e0263248. Date of Electronic Publication: 2022 Feb 23 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0263248
Abstrakt: Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's 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' predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo 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 TNFα 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 TNFα 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 ex vivo 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.
Competing Interests: The authors APC, LJG, EPK, MM declare that they have no competing interests. REPROCELL Europe Ltd is a commercial provider of laboratory-based tests for preclinical research. GM, KB and DCB are all paid employees of REPROCELL Europe. These commercial affiliations do not alter adherence of the authors to the journal policies on sharing data and materials.
Databáze: MEDLINE
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