Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based...

Area of Science:

• Computational toxicology and cheminformatics
• Adverse outcome pathway (AOP) research
• Pulmonary fibrosis research

Background:

• The adverse outcome pathway (AOP) framework offers an alternative to animal testing for chemical safety assessment.
• In silico methods, specifically molecular initiating event (MIE) modeling, are crucial within the AOP framework.
• Intersecting AOPs (AOP 347) involving peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4) have been linked to pulmonary fibrosis.

Purpose of the Study:

• To develop novel quantitative structure-activity relationship (QSAR) models for two key MIEs within AOP 347.
• To compare the predictive performance of QSAR models against other in silico methods like molecular dynamics and pharmacophore modeling.
• To validate the developed models using in vitro test data for assessing their suitability in pulmonary fibrosis risk assessment.

Main Methods:

• Development of two novel quantitative structure-activity relationship (QSAR) models targeting PPAR-γ and TLR4 MIEs.
• Comparative analysis of prediction performance across different MIE modeling techniques.
• Validation of QSAR models using in vitro experimental data.

Main Results:

• The developed QSAR models demonstrated high accuracy in predicting MIEs associated with AOP 347.
• QSAR modeling proved to be a more accurate and suitable method for MIE modeling compared to molecular dynamics and pharmacophore approaches.
• In vitro validation confirmed the reliability of the QSAR models.

Conclusions:

• The study successfully developed and validated two QSAR models based on AOP 347 for PPAR-γ and TLR4.
• These QSAR models are effective in silico tools for screening chemical mixtures potentially causing pulmonary fibrosis.
• The findings support the use of QSAR within the AOP framework as a viable alternative to animal testing.