Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization
- Author
- Lia Visintin (UGent) , Eugenio Alladio, María García Nicolás, Sarah De Saeger (UGent) , En-Hsuan Lu, Weihsueh A. Chiu, Tess Goessens (UGent) and Marthe De Boevre (UGent)
- Organization
- Project
- Abstract
- Mycotoxin exposure contributes to adverse human health outcomes, however, data regarding validated human biomarkers of exposure are lacking. This study presents an integrated framework for the biomarker discovery and toxicokinetic characterization of mycotoxin in humans. The aim of the study is to identify new biomarkers, determine their toxicokinetic (TK) properties, and build an integrated data analysis workflow using machine learning (ML), whilst focusing on non- and minimally-invasive sampling strategies. Following sample collection and chemical analysis, obtained datasets are used for the computation of ML models. Probability-based techniques are employed to calculate specific boundaries in the multidimensional space and, in parallel, ML classification methodologies are evaluated to scrutinize controls from intervened volunteers. Furthermore, multivariate regression models are computed to study the correlation of potential biomarkers with mycotoxin dosages. Once biomarkers have been identified, data are fit using Bayesian methods to a population-TK model to estimate key parameters related to absorption, distribution, metabolism, and excretion. This standardized framework allows the scientific community to identify and validate new mycotoxin biomarkers and related ADME-properties in both a precise and accurate manner. Although we developed the proposed trial for various different mycotoxins, due to ethical considerations, focus was set towards IARC group III-classified mycotoxins.
- Keywords
- INTERINDIVIDUAL VARIABILITY, EXPOSURE, OPLS, DEOXYNIVALENOL, CALIBRATION, REGRESSION, TOXICOLOGY, SELECTION, PLASMA, URINE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K9Q5PWYCJAJWN838A6WH2DGS
- MLA
- Visintin, Lia, et al. “Integrative Human Intervention Study for Mycotoxin Biomarker Discovery and Toxicokinetic Characterization.” SCIENTIFIC REPORTS, vol. 15, no. 1, 2025, doi:10.1038/s41598-025-27106-6.
- APA
- Visintin, L., Alladio, E., Nicolás, M. G., De Saeger, S., Lu, E.-H., Chiu, W. A., … De Boevre, M. (2025). Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization. SCIENTIFIC REPORTS, 15(1). https://doi.org/10.1038/s41598-025-27106-6
- Chicago author-date
- Visintin, Lia, Eugenio Alladio, María García Nicolás, Sarah De Saeger, En-Hsuan Lu, Weihsueh A. Chiu, Tess Goessens, and Marthe De Boevre. 2025. “Integrative Human Intervention Study for Mycotoxin Biomarker Discovery and Toxicokinetic Characterization.” SCIENTIFIC REPORTS 15 (1). https://doi.org/10.1038/s41598-025-27106-6.
- Chicago author-date (all authors)
- Visintin, Lia, Eugenio Alladio, María García Nicolás, Sarah De Saeger, En-Hsuan Lu, Weihsueh A. Chiu, Tess Goessens, and Marthe De Boevre. 2025. “Integrative Human Intervention Study for Mycotoxin Biomarker Discovery and Toxicokinetic Characterization.” SCIENTIFIC REPORTS 15 (1). doi:10.1038/s41598-025-27106-6.
- Vancouver
- 1.Visintin L, Alladio E, Nicolás MG, De Saeger S, Lu E-H, Chiu WA, et al. Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization. SCIENTIFIC REPORTS. 2025;15(1).
- IEEE
- [1]L. Visintin et al., “Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization,” SCIENTIFIC REPORTS, vol. 15, no. 1, 2025.
@article{01K9Q5PWYCJAJWN838A6WH2DGS,
abstract = {{Mycotoxin exposure contributes to adverse human health outcomes, however, data regarding validated human biomarkers of exposure are lacking. This study presents an integrated framework for the biomarker discovery and toxicokinetic characterization of mycotoxin in humans. The aim of the study is to identify new biomarkers, determine their toxicokinetic (TK) properties, and build an integrated data analysis workflow using machine learning (ML), whilst focusing on non- and minimally-invasive sampling strategies. Following sample collection and chemical analysis, obtained datasets are used for the computation of ML models. Probability-based techniques are employed to calculate specific boundaries in the multidimensional space and, in parallel, ML classification methodologies are evaluated to scrutinize controls from intervened volunteers. Furthermore, multivariate regression models are computed to study the correlation of potential biomarkers with mycotoxin dosages. Once biomarkers have been identified, data are fit using Bayesian methods to a population-TK model to estimate key parameters related to absorption, distribution, metabolism, and excretion. This standardized framework allows the scientific community to identify and validate new mycotoxin biomarkers and related ADME-properties in both a precise and accurate manner. Although we developed the proposed trial for various different mycotoxins, due to ethical considerations, focus was set towards IARC group III-classified mycotoxins.}},
articleno = {{39096}},
author = {{Visintin, Lia and Alladio, Eugenio and Nicolás, María García and De Saeger, Sarah and Lu, En-Hsuan and Chiu, Weihsueh A. and Goessens, Tess and De Boevre, Marthe}},
issn = {{2045-2322}},
journal = {{SCIENTIFIC REPORTS}},
keywords = {{INTERINDIVIDUAL VARIABILITY,EXPOSURE,OPLS,DEOXYNIVALENOL,CALIBRATION,REGRESSION,TOXICOLOGY,SELECTION,PLASMA,URINE}},
language = {{eng}},
number = {{1}},
pages = {{16}},
title = {{Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization}},
url = {{http://doi.org/10.1038/s41598-025-27106-6}},
volume = {{15}},
year = {{2025}},
}
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