# Novel Analytical and Experimental Approaches for Predicting the Biological Effects of Mixtures

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $458,401

## Abstract

Project Summary / Abstract
Assessing the health effects of exposure to complex mixtures is a priority for NIEHS: “It is imperative to
develop methods to assess the health effects associated with complex exposures in order to minimize their
impact on the development of disease.” The vast number of potential mixtures includes environmental
chemicals, pharmaceuticals, dietary and endogenous compounds. Concentration addition/dose addition (CA)
is a predictive method widely used for compounds that act by similar mechanisms and provides a foundation
for risk assessment. However, CA cannot make predictions for mixtures that contain full and partial receptor
agonists at effect levels above that of the least efficacious component. Since partial agonists are common, we
developed Generalized Concentration Addition (GCA) to address this need. GCA has been applied to systems
where ligands compete for a single receptor binding site, successfully predicting experimental data for mixtures
of AhR ligands and of PPARγ ligands. This project focuses on ligand-receptor systems as they are biologically
important, initiate many toxicity pathways, and are amenable to modeling and rapid testing. Our overall
hypothesis is that GCA applies to all receptor systems in which ligands reversibly compete for the same
receptor binding sites. Based on mechanistic information, we use pharmacologically-based mathematical
modeling to estimate the biological effect of mixtures; we test the predictions with empirical data. Here, we
propose to test the ability of GCA to predict the biological effects of more complex receptors and mixture
scenarios. Specific Aim 1 tests the ability of GCA to predict receptor activation by mixtures of ligands for
receptors that homodimerize. The predictions will be tested using reporter cell lines for AR and ERα and a
spectrum of ligands (full agonists, partial agonists, competitive antagonists). Applicability of GCA will be further
examined using Tox21 data for single chemicals and mixtures. Specific Aim 2 tests the ability of GCA to predict
mixture effects for downstream biological endpoints. We hypothesize that GCA predicts a downstream effect if
the effect is a function of receptor activation. This will be tested for proximal and distal effects of mixtures of ER
ligands (in vitro) and PPARγ ligands (in vitro and in vivo). Specific Aim 3 examines how similar mechanisms
must be for GCA to apply. Models for several “similar” mechanisms will be compared with empirical data: 1)
mixtures that contain selective receptor modulators for ERα and PPARγ; 2) heterodimer partners that each
bind ligands (ERα:ERβ, PPARγ:RXR) and 3) mixtures containing an aromatase inhibitor (altering the amount
of natural ligand) plus ERα ligands. This project builds upon the Tox21 recommendations of examining
perturbations of toxicity pathways, increased use of in vitro testing and computational models and will generate
a powerful approach for improving risk assessment of mixtures.

## Key facts

- **NIH application ID:** 10020409
- **Project number:** 5R01ES027813-04
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Thomas F Webster
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $458,401
- **Award type:** 5
- **Project period:** 2017-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10020409

## Citation

> US National Institutes of Health, RePORTER application 10020409, Novel Analytical and Experimental Approaches for Predicting the Biological Effects of Mixtures (5R01ES027813-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10020409. Licensed CC0.

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