Expanding the chemical exposome using in-silico transformation analysis - an example using insecticides.

Area of Science:

• Environmental Chemistry
• Computational Toxicology
• Bioinformatics

Background:

• The human exposome includes numerous chemicals from consumer and environmental sources.
• Chemicals can transform within cells, altering their toxicity and health impacts.
• Predicting these transformations is crucial for understanding chemical exposure effects.

Purpose of the Study:

• To develop an integrated computational workflow for predicting chemical transformation products within the exposome.
• To leverage machine learning and chemical data science for in-silico transformation prediction.
• To expand the understanding of chemical metabolism and biomonitoring strategies.

Main Methods:

• An integrated workflow combining RXNMmapper, Rxn-INSIGHT, and RDChiral was developed.
• A reaction template library was generated from over 90,000 PubChem reactions.
• The workflow was applied to 181 insecticide structures to predict transformation products.

Main Results:

• The workflow successfully predicted 21,284 unique transformation products for insecticides.
• Many predicted products were identified in PubChem but lacked linkage to parent compounds.
• The approach demonstrated the potential to uncover novel metabolites of environmental chemicals.

Conclusions:

• The developed workflow provides a powerful tool for predicting chemical transformations in the exposome.
• This method can enhance our understanding of chemical metabolism and guide biomonitoring efforts.
• It facilitates the characterization of the human exposome using existing chemical databases.