The objective of this project is to develop a testing framework that will evaluate the toxicity of complex mixtures of per- and polyfluoroalkyl substances (PFAS) based on biological effects by connecting macromolecular and suborganismal responses to impacts on whole animals. The framework will use putative molecular initiating events identified computationally, toxicity pathways identified through transcriptomic signatures, and toxic effects integrated within bioenergetic models to predict whole organism responses that can be translatable to population risk. New approach methodologies will be incorporated into standardized tests to efficiently screen chemical mixtures and inform hazard estimations and remediation needs.

Technical Approach

Field Sampling Fish in Clark's Marsh

Standardized tests will be conducted on Daphnia and fathead minnows to evaluate effects of a PFAS mixture found in Clark’s Marsh and perfluorooctanesulfonic acid (PFOA) alone. In addition to standardized tests, toxicity will be evaluated using several new approaches, including transcriptomics and molecular docking. The experiments will link transcriptomic signature responses to specific physiological modes of action (pMoA) relevant for dynamic energy budgets of organisms. Once this linkage is established, the project team will demonstrate how this approach can improve population predictions for ecological risk assessment, and test that the approach is able to discern PFAS toxicity patterns from mixtures in the field using caged fathead minnows.


This research will compile and synthesize a large volume of data collected from several levels of biological organization to assess the toxicity of PFAS mixtures. Suborganismal-level changes will be connected to impacts on individual-level bioenergetic processes as modeled in a Dynamic Energy Budget framework. The team's goal of is to develop a model that can predict the chronic impact of a mixture of PFAS on fish populations using only suborganismal data and to distinguish mixture impacts from natural stressors. The advantage of developing this connection is that it could streamline future toxicity testing such that only short-term exposures with suborganismal-level data could predict long-term, population-relevant effects. This framework will also provide the opportunity for retrospective risk assessment and for a way to guide remediation efforts in impacted areas in natural settings. (Anticipated Project Completion - 2027)