Integrative human intervention study for mycotoxin biomarker discovery and toxicokinetic characterization

Area of Science:

• Toxicology
• Biomarker Discovery
• Human Health

Background:

• Mycotoxin exposure poses health risks, but validated human exposure biomarkers are scarce.
• Accurate identification of exposure biomarkers is crucial for risk assessment and public health.

Purpose of the Study:

• To develop an integrated framework for mycotoxin biomarker discovery and toxicokinetic (TK) characterization in humans.
• To identify novel biomarkers using non- and minimally-invasive sampling.
• To establish a machine learning (ML)-driven workflow for data analysis and biomarker validation.

Main Methods:

• Integrated framework combining biomarker discovery and TK analysis.
• Utilized machine learning (ML) for data analysis, classification, and regression modeling.
• Employed Bayesian methods for population-TK modeling to estimate ADME parameters.

Main Results:

• Developed and validated a standardized framework for identifying and characterizing mycotoxin biomarkers.
• Demonstrated the utility of ML in analyzing complex biomarker data.
• Successfully estimated key absorption, distribution, metabolism, and excretion (ADME) properties.

Conclusions:

• The proposed framework enables precise and accurate identification and validation of human mycotoxin biomarkers.
• This approach facilitates a better understanding of mycotoxin toxicokinetics in humans.
• The study focused on IARC group III mycotoxins due to ethical considerations.