{ "abstract": "Background: Evodia rutaecarpa (EVR) is a widely used low-toxicity traditional Chinese medicine in clinical practice. Its adverse reactions are often subtle with prominent inter-individual variability, and the associated risks are frequently underestimated. This study conducted multi-omics analysis of the metabolic and microbiological characteristics of its related adverse reactions, and constructed a prediction model to provide a reference for individualized safe medication.\n\nMethods: 86 patients with gastric diseases receiving EVR-containing compound formulas were enrolled and divided into a control group (n = 50) and an adverse reaction group (n = 36) based on the presence or absence of adverse reactions. Serum, urine, and saliva samples were collected for untargeted metabolomics and 16S rRNA sequencing, respectively. Seven machine learning algorithms (logistic regression, random forest, LightGBM, etc. ) were employed to construct predictive models in combination with LOOCV, screen key features, and derive a multi-omics joint prediction formula.\n\nResults: Clinically, adverse events related to EVR predominantly presented as upper-gastrointestinal symptoms. Omics profiling of patient samples identifies tentative correlative trends between EVR-associated adverse events and systemic changes including disturbed amino acid metabolism, shifted neuroactive molecule profiles, altered bile secretion signatures and oral microbiota dysbiosis. Machine learning screening yields multiple model-associated classification features from multi-omics datasets. Serum PE (P-18:1/22:0), hydroxyisocaproic acid and ureidoisobutyric acid show statistical trends linked to lower model prediction outputs for adverse events. Urinary 1, 3-Dihydro-(2H)-indol-2-one and MG (0:0/20:4/0:0) correlate with reduced predictive values, whereas arachidoyl ethanolamide associates with elevated model prediction scores. Higher relative abundances of Granulicatella , Alysiella , Leptotrichia hongkongensis and Streptococcus in saliva correspond to higher predictive probabilities from the classification model. An AUC-weighted multi-omics joint formula is established to calculate individual predictive likelihood of EVR-related adverse reactions. Scores ≥ 0.597 indicate higher model-derived predictive tendency, while scores below 0.597 indicate lower predictive possibility.\n\nConclusion: The ten omics-derived discriminative features and the multi-omics joint prediction formula developed in this work enable quantitative estimation of individual-level risk for EVR-related adverse reactions. They offer preliminary references for safety assessment, risk stratification, and personalized application of low-toxicity traditional Chinese medicines.", "abstract_shown": true, "affiliations": [ "Chinese Medicine Germplasm Resources Innovation and Effective Uses Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University of Traditional Chinese Medicine", "Key Laboratory of Standardization of Chinese Medicine (Chengdu University of Traditional Chinese Medicine), Ministry of Education", "Department of Pharmacy, Chinese PLA General Hospital", "Lung Disease Diagnosis and Treatment Center, National Medical Center, The First Affiliated Hospital of Henan University of Chinese Medicine", "Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R. China / Henan Key Laboratory of Chinese Medicine for Respiratory Diseases, Henan University of Chinese Medicine", "The First Clinical Medical College, Henan University of Chinese Medicine", "College of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine", "Hospital of Chengdu University of Traditional Chinese Medicine, School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine" ], "article_type": "research article", "arxiv": null, "author_total": 11, "authors": [ { "affs": [ 0, 1 ], "name": "Xiyue Tan" }, { "affs": [ 2 ], "name": "Jin Xie" }, { "affs": [ 3, 4, 5 ], "name": "Xiaochuan Guo" }, { "affs": [ 6 ], "name": "Lv Wu" }, { "affs": [ 0, 1 ], "name": "Senpan Shi" }, { "affs": [ 0, 1 ], "name": "Lingping Fu" }, { "affs": [ 0, 1 ], "name": "Wenwen Zhang" }, { "affs": [ 0, 1 ], "name": "Lingshun Zeng" }, { "affs": [ 0, 1 ], "name": "Xiao Ma" }, { "affs": [ 7 ], "name": "Jinhao Zeng" }, { "affs": [ 2 ], "name": "Yanling Zhao" } ], "bibtex": "https://research.draconitic.com/paper/3qgc810eamzqg.bib", "cited_by": 0, "doi": "10.3389/fphar.2026.1901889", "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13724504/?tool=EBI", "id": "3qgc810eamzqg", "issns": [ "1663-9812" ], "landing_url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1901889/full", "language": "eng", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Original Research" ], [ "openalex", "2707|Traditional Chinese Medicine Analysis" ] ], "openalex": "W7222918181", "pdf_url": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1901889/pdf", "pmcid": "PMC13724504", "pmid": "42973801", "published": "2026-10-19", "publisher": "Frontiers Media SA", "ris": "https://research.draconitic.com/paper/3qgc810eamzqg.ris", "sources": [ { "first_seen": "2026-10-30T12:20:06Z", "native_id": "10.3389/fphar.2026.1901889", "source": "crossref", "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1901889/full" }, { "first_seen": "2026-11-03T15:20:51Z", "native_id": "PMC:PMC13724504", "source": "europepmc", "url": "https://europepmc.org/article/PMC/PMC13724504" }, { "first_seen": "2026-11-04T12:23:04Z", "native_id": "PMC13724504", "source": "pmc", "url": "https://doi.org/10.3389/fphar.2026.1901889" }, { "first_seen": "2026-11-04T15:20:48Z", "native_id": "MED:42973801", "source": "europepmc", "url": "https://europepmc.org/article/MED/42973801" } ], "subjects": [ { "field": "medicine", "origin": "issn:1663-9812", "subfield": "pharmacology-toxicology" } ], "title": "Multi-omics and machine learning based on clinical samples: revealing the toxicity characteristics in adverse reactions to Evodia rutaecarpa", "title_text": "Multi-omics and machine learning based on clinical samples: revealing the toxicity characteristics in adverse reactions to Evodia rutaecarpa", "type": "peer-reviewed", "updated_at": "2026-11-06T12:20:57Z", "url": "https://research.draconitic.com/paper/3qgc810eamzqg", "venue": "Frontiers in Pharmacology", "versions": [] }