Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity

Area of Science:

• Toxicology and Bioinformatics
• Genomics and Biomarker Discovery

Background:

• Existing drug-induced liver injury (DILI) testing models inadequately represent human health risks.
• There is a critical need for improved DILI prediction and biomarker development.

Purpose of the Study:

• To develop novel DILI biomarker profiles using gene groups from publicly accessible hepatotoxicity databases.
• To evaluate the utility of 'omics-based versus text-mining-based databases for identifying hepatotoxicity-associated genes.

Main Methods:

• Explored one human liver 'omics-based and four text-mining-based databases for hepatotoxicity-associated gene lists.
• Performed over-representation analysis using a hepatotoxicant-exposed primary human hepatocytes dataset.
• Visualized identified gene groups in pathway formats for biomolecular interpretation.

Main Results:

• Human liver 'omics-based gene lists demonstrated superior performance compared to text-mining gene lists.
• Text-mining databases showed significant variability in their identified gene lists.
• Both database types yielded gene lists with potential for DILI biomarker development.

Conclusions:

• Leveraging existing, openly accessible databases offers a promising avenue for translational toxicology research.
• Developing pathway-based biomarker profiles can enhance the interpretation of DILI mechanisms.
• This approach supports the advancement of DILI biomarker development and predictive toxicology.