Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology.

Area of Science:

• Environmental Toxicology
• Computational Biology
• Risk Assessment

Background:

• Quantitative adverse outcome pathways (qAOPs) integrate biological models to link molecular events to population-level impacts.
• qAOPs offer dose-response and time-course predictions crucial for regulatory science.
• Current methods require robust frameworks for qAOP development and application.

Purpose of the Study:

• To describe the motivation, technical considerations, confidence evaluation, and applications of qAOPs.
• To illustrate qAOP development using aromatase inhibition in fathead minnow (Pimephales promelas).
• To demonstrate the utility of toxic equivalence (TEQ) calculations for untested chemicals.

Main Methods:

• Development of a qAOP linking cytochrome P450 19A aromatase inhibition (MIE) to population effects in fathead minnow.
• Modeling the hypothalamic-pituitary-gonadal axis, vitellogenin synthesis, fecundity, and population dynamics.
• Application of toxic equivalence (TEQ) calculations to predict effects of other aromatase inhibitors.

Main Results:

• A qAOP was constructed, modeling the pathway from aromatase inhibition to population-level decreases in fathead minnow.
• The model successfully predicted population impacts based on molecular initiating events.
• Toxic equivalence calculations enabled prediction of effects for untested chemicals, like iprodione.

Conclusions:

• qAOPs provide valuable quantitative predictions for regulatory decision-making.
• Despite resource intensity, qAOPs offer significant benefits for chemical risk assessment.
• The developed qAOP framework supports the assessment of multiple chemicals through TEQ calculations.