Challenging Additivity: Comparing Predicted and Observed AhR Activity of Polycyclic Aromatic Compound (PAC) Mixtures Containing Active and Inactive Constituents

TL;DR

This study compares predicted and observed toxicity of PAC mixtures using different models, finding that including inactive chemicals overestimates potency.

Contribution

The study introduces a framework for modeling PAC mixtures by prioritizing active components and using BMC-based methods.

Findings

Including inactive chemicals in mixture models leads to overestimation of potency.

Generalized concentration addition (GCA) paired with BMC10 modeling provides the best agreement with observed responses.

Scaling contributions to active components improves predictive accuracy.

Abstract

Class-based cumulative risk assessment approaches have
been applied
to high-priority environmental contaminants such as polycyclic aromatic
compounds (PACs), yet uncertainties remain in their application. In
this study, we evaluated the influence of inactive chemicals on mixture
modeling outcomes and explored strategies for predicting the aryl
hydrocarbon receptor (AhR)-mediated toxicity of PAC mixtures. Using
an in vitro AhR reporter gene assay, we tested seven defined mixtures
composed of six active and seven inactive PACs. Observed concentration–response
curves were compared to predictions from three established mixture
models, concentration addition (CA), independent action (IA), and
generalized concentration addition (GCA), using both effective concentration
eliciting 10% response (EC10) and benchmark concentration (BMC10) approaches. Including inactive chemicals without scaling
led to consistent overestimation of potency, especially in models
assuming equal efficacy. Predictive accuracy improved across all models
when mixtures were limited to active chemicals and contributions were
scaled to 100%, excluding inactives. Among approaches, GCA consistently
produced the best agreement with measured responses, particularly
when paired with BMC modeling. BMC10 values better accommodate partial
agonists. Our findings support a pragmatic, mechanism-based framework
for modeling environmental mixtures, one that prioritizes active components,
scales their contributions, and adopts BMC-based methods to estimate
potency.

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