Grantee Research Project Results
2023 Progress Report: A tiered hybrid experimental-computational strategy for rapid risk assessment of complex environmental mixtures using novel analytical and toxicological methods
EPA Grant Number: R840450Title: A tiered hybrid experimental-computational strategy for rapid risk assessment of complex environmental mixtures using novel analytical and toxicological methods
Investigators: Rusyn, Ivan , Wright, Fred A. , Zhou, Yihui , Chiu, Weihsueh A
Institution: Texas A & M University , North Carolina State University
EPA Project Officer: Aja, Hayley
Project Period: September 1, 2022 through August 31, 2025
Project Period Covered by this Report: September 1, 2022 through August 31,2023
Project Amount: $750,000
RFA: Development of Innovative Approaches to Assess the Toxicity of Chemical Mixtures Request for Applications (RFA) (2022) RFA Text | Recipients Lists
Research Category: Safer Chemicals , Health Effects , Human Health , Chemical Safety for Sustainability , New Approach Methods (NAMs) , Mixtures , Predictive Toxicology , CSS
Objective:
Evaluation of composition and hazards of chemical mixtures, or complex products classified as UVCBs (unknown variable composition or biological substances), presents a multitude of challenges. These include the presence of unknown constituents, a limited basis for grouping additive and independent components, and the lack of toxicity data on most constituents, and whole mixtures or UVCBs. Recent disasters resulting in redistribution of complex chemical mixtures in the environment have provided our group an opportunity to develop new approaches to rapid composition and hazard characterization to inform rapid decision-making. Our long-term goal is to ensure timely risk-based assessment of mixtures and UVCBs that ensures human health protection from the toxicity of known/unknown components. We will accomplish this through integration of novel toxicological (i.e., human cell-based assays), analytical (i.e., ion mobility spectrometry-mass spectrometry), and modeling (i.e., interaction, mediation and dose reconstruction) methods. We plan to demonstrate the application of these methods in the context of rapid risk assessment/management of sites contaminated with uncharacterized chemical mixtures.
The main outcomes of this project will be a suite of analytical, in vitro, and computational methods and tools that can be applied in a tiered strategy for rapid quantitative characterization of the composition and hazards of complex environmental mixtures and UVCBs. Overall, the project is pursuing the following specific aims.
Aim 1: To determine grouping of chemical mixture components for assessment of hazard(s) through integration of multi-phenotype/multi-tissue bioactivity data from a compendium of human induced pluripotent stem cell (iPSC)-derived cells.
Aim 2: To develop approaches for prioritization of components in whole mixtures or UVCBs that are likely to contribute most to joint toxicity through mediation and interaction methods that integrate multi-dimensional analytical and in vitro bioactivity data.
Aim 3: To evaluate prediction of joint toxicity of whole or defined mixtures and UVCBs through novel probabilistic additivity models of grouped (Aim 1) and prioritized (Aim 2) components.
Aim 4: To demonstrate the integration of the proposed exposure, toxicological and modeling methods into a tiered hybrid experimental-computational strategy for rapid risk assessment of complex environmental mixtures and UVCBs.
Progress Summary:
We have begun our research on the approaches to grouping of chemical mixture components for assessment of hazard(s) through integration of multi-phenotype/multi-tissue bioactivity data from a compendium of human induced pluripotent stem cell (iPSC)-derived cells using several case studies – a diverse library of per- and poly-fluoroalkyl substances (PFAS) and PAH-containing UVCBs (e.g., products of petroleum refining). Overall, we are testing a hypothesis that a targeted battery of human cell-based assays can be used to determine whether there are structure-bioactivity relationships for individual substances and mixtures, and to characterize potential risks by comparing bioactivity (point-of-departure) to exposure estimates. In collaboration with scientists at US EPA-NCCTE, we study PFAS, a class of contaminants of very high concern yet there is little information on their potential health hazards and regulatory agencies need safety data to determine whether exposure limits or restrictions are needed. With thousands of PFAS in commerce, cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards. We tested a diverse set of 56 PFAS from 8 structure-based classes in concentration response using a focused set of 6 human cell types from suggested target organs for PFAS effects. We found that many tested compounds were without effect; however, cell-specific responses were observed for some PFAS, indicating that a compendium of in vitro models is necessary to identify potential hazards. Still, our results show that structure-bioactivity relationships could not be established for tested PFAS and that they may need to be tested in our proposed focused set of several human cell types.
Our research to develop approaches for prioritization of components in whole mixtures or UVCBs that are likely to contribute most to joint toxicity through mediation and interaction methods that integrate multi-dimensional analytical and in vitro bioactivity data focused on facilitating the dialogue between the industry and the decision-makers. For example, one of the challenging topics has been the extent of chemical compositional characterization of products of petroleum refining that may be necessary for substance identification and hazard evaluation. To address this, we published a comprehensive review of the analytical methods and how they can relate to the most recent regulatory guidance (Roman-Hubers et al., 2023). This manuscript contributes to the mutual appreciation of the regulatory guidance and the realities of what information new analytical chemistry methods can deliver. In addition, we have used several novel statistical methods to determine whether relationships exist between chemical constituents and bioactivity. For example, the study of Cordova et al (2023) used this approach to explore the relationships between chemical constituents of UVCBs and their bioactivity.
Our studies to evaluate prediction of joint toxicity of whole or defined mixtures and UVCBs through novel probabilistic additivity models of grouped and prioritized components focused on evaluating the accuracy of additivity models for predicting mixture bioactivity. Specifically, we already piloted the overall approaches to probabilistic additivity modeling using studies of diverse environmental chemicals in a genetically-diverse in vitro model of human lymphoblast cell lines. For example, in a study by Ford et al (2022), we tested defined mixtures and their individual components and determine whether adverse effects of the mixtures were likely to be more variable in a population than those of the individual chemicals. We used the in vitro model comprised 146 human lymphoblastoid cell lines from four diverse subpopulations of European and African descent. This study demonstrated the feasibility of using a set of human lymphoblastoid cell lines as an in vitro model to quantify the extent of inter-individual variability in hazardous properties of both individual chemicals and mixtures. In a follow up publication (Jang et al., 2022), we conducted probabilistic concentration addition analysis of complex mixture exposures in a population-based human in vitro model. Utilizing the data from Ford et al (2022), we applied concentration addition to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio. We found that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple concentration addition models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.
Finally, in an effort to demonstrate the integration of the proposed exposure, toxicological and modeling methods into a tiered hybrid experimental-computational strategy for rapid risk assessment of complex environmental mixtures and UVCBs, we have concentrated our efforts on assessing the impact of bioactivity-based grouping on evaluation of joint toxicity. Specifically, two recent publications demonstrate how the strategy we proposed can be implemented for regulatory considerations of complex substances (in both cases the examples pertain to petroleum UVCBs). First, we demonstrated a tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo (Tsai et al., 2023). Second, we supported the hazard and risk characterization components of the study of petroleum UVCBs (Cordova et al., 2023), which showed that novel analytical and in vitro methods may aid in defining proper groupings for complex petroleum UVCBs. Importantly, this study also demonstrated the challenges of variability among samples and the need for additional in vitro testing of the whole mixtures/UVCBs.
Future Activities:
In Specific Aim 1, we will continue experiments with the in vitro models and both individual chemicals and mixtures. We anticipate receiving the chemical library from the EPA vitro testing of PFAS mixtures (see table below) that were prepared to emulate various environmental conditions and human biomonitoring data. We will then use these data for dose reconstruction modeling and comparisons to the data on the individual PFAS.
In Specific Aim 2, we will combine our novel machine learning prediction models for responses and predictors with standard methods for mediation analysis using the standard Sobel statistic to test for significant mediation. We will initially apply these procedures to the UVCB data analyzed in Tsai et al. (2023), with analytical predictors such as polycyclic aromatic content used as the base causal predictors, gene expression used as potential mediating factors, and iPSC cell assay bioactivity as responses.
In Specific Aim 3, we will continue work on evaluating additivity models from the PFAS mixture experiments conducted in Aim 1. Additionally, once both in vitro testing and IMS-MS characterization of the 1000+ chemical library is complete, we will use those results to design sufficiently similar mixtures as outlined in Aim 3.1.
In Specific Aim 4, we will focus on utilizing the results of PFAS testing in Aim 1 to evaluate the impact of bioactivity-based grouping. Additionally, once the in vitro bioactivity and IMS-MS characterizations of the 1000+ chemical library is complete, we will use those results to identify “contaminants of concern” in whole mixtures and evaluate the relative degree of health protection from “traditional” risk assessment as compared to those based on predicted or measured whole mixture bioactivity. Furthermore, we will work with Aim 2 to compare risk prioritization based on traditional methods with those based on interaction and mediation analyses.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
| Other project views: | All 3 publications | 3 publications in selected types | All 3 journal articles |
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Ford L, Lin H, Zhou Y, Wright F, Gombar V, Sedykh A, Shah R, Chiu W, Rusyn I. Characterizing PFAS hazards and risks: a human population-based in vitro cardiotoxicity assessment strategy. HUMAN GENOMICS 2024;18(1) |
R840450 (2023) |
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Lu E, Ford L, Rusyn I, Chiu W. Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability. RISK ANALYSIS 2024; |
R840450 (2023) R835802 (Final) |
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Supplemental Keywords:
mixtures, risk assessment, analytical, toxicology, NAMs, UVCBsProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.