A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening.

Area of Science:

• Toxicology and Pharmacology
• Computational Chemistry
• Drug Metabolism

Background:

• In vitro toxicity testing faces limitations due to the inability to replicate complex metabolic processes.
• False negatives can occur when in vitro assays miss metabolites that are bioactive in vivo.
• A need exists for methods to identify potentially active metabolites from inactive parent compounds.

Purpose of the Study:

• To present a workflow for integrating in silico and in vitro data to identify inactive parent chemicals producing active metabolites.
• To enhance the reliability of high-throughput screening (HTS) by accounting for metabolic activation.
• To improve the accuracy of chemical risk assessment by considering metabolic effects.

Main Methods:

• A workflow combining in vitro, in silico, and quantitative structure-activity relationship (QSAR) models was developed.
• Over 1,400 in vitro inactive chemicals were analyzed for potential active metabolites using in silico software.
• A consensus QSAR model predicted estrogen receptor (ER) binding activity for generated metabolites.

Main Results:

• Approximately 8-10% of generated metabolites across two generations showed predicted ER-binding activity.
• These active metabolites were linked to 259 previously in vitro inactive parent chemicals.
• Metabolites with ER-binding potential were enriched in alcohol, aromatic, and phenol substructures.

Conclusions:

• The presented workflow effectively identifies parent chemicals that may become bioactive through metabolism.
• This approach increases confidence in HTS results by addressing metabolic activation.
• The findings aid in identifying potentially hazardous chemicals missed by standard in vitro assays.