Paradigm shift in toxicity testing and modeling.

Area of Science:

• Toxicology
• Computational Biology
• Biotechnology

Background:

• Traditional toxicity testing methods using animal models are costly, slow, ethically problematic, and lack human relevance.
• There is a critical need for alternative strategies in chemical risk assessment that are more efficient and reliable.
• In vitro human cell-based assays offer a promising alternative for identifying toxicity pathways and predicting in vivo responses.

Purpose of the Study:

• To review the benefits and impact of quantitative high-throughput screening (qHTS) in chemical risk assessment.
• To highlight the role of qHTS in advancing computational toxicology and predictive modeling.
• To compare quantitative structure-activity relationship (QSAR) models based on traditional and qHTS data.

Main Methods:

• Utilizing in vitro human cell-based assays to identify toxicity pathways and molecular mechanisms.
• Employing quantitative high-throughput screening (qHTS) technology to analyze toxicological endpoints.
• Developing and comparing computational toxicology models, including QSAR, using both in vivo and qHTS data.

Main Results:

• qHTS efficiently decomposes complex toxicological endpoints into specific organ pathways.
• In vitro assays coupled with qHTS improve machine learning effectiveness for mechanism of action identification.
• qHTS enhances data quality and quantity for predictive toxicology model construction, as demonstrated in the US Tox21 program.

Conclusions:

• qHTS-based in silico toxicity models demonstrate high reliability and robustness.
• The increasing availability of qHTS data enriches the pool for predictive toxicology.
• These advanced models are poised to become valuable tools for chemical risk assessment and drug discovery.