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.