Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database

Area of Science:

• Environmental health sciences
• Toxicology
• Computational biology

Background:

• Modern society involves widespread chemical exposure, contributing significantly to the human exposome.
• Monitoring only parent compounds may not accurately reflect exposure due to biotransformation.
• A comprehensive, risk-based human exposome database is crucial for advancing research.

Purpose of the Study:

• To establish a comprehensive, risk-prioritized human exposome database.
• To incorporate physicochemical properties and risk prediction models.
• To develop a user-friendly graphical user interface (GUI) for database searching.

Main Methods:

• Database construction via text mining and data fusion.
• Chemical prioritization using exposure data (Systematic Empirical Evaluation of Models) and toxicity predictions (Toxicity Estimation Software Tool, Toxicological Priority Index, ToxCast).
• Assessment of biotransformation half-lives and prediction of metabolites using machine learning (BioTransformer).

Main Results:

• Compilation of a human exposome database with over 100,000 chemicals.
• Prioritization of 13,441 chemicals by hazard quotient and 7,770 by risk index.
• Generation of a metabolite database and an interactive GUI for data access.

Conclusions:

• The database facilitates chemical management and scientific understanding.
• Enables rapid prioritization of chemicals for biomonitoring in epidemiological studies.
• Enhances the ability to assess human health risks associated with chemical exposures.