Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

Abstract

Background:

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop “Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects.” NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

Objectives:

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

Results:

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

Discussion:

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340

Introduction

Biomonitoring programs^1–3 indicate that people experience diverse chemical exposures from their use of personal care and household products, diet, lifestyle choices, occupational exposures, and incidental contact with environmental contaminants. People are also exposed to numerous nonchemical stressors,⁴ which include biological (e.g., viruses) and physical (e.g., noise, radiation) factors and psychosocial stressors (e.g., fear of violence, lack of social networks). Exposure patterns can vary widely with life stage (e.g., infancy, pregnancy, senectitude) and occupation. Racial/ethnic disparities in chemical and psychosocial stressor burdens can contribute to increased vulnerability in black and Hispanic communities.⁵ Despite this complex landscape, risk evaluations have traditionally focused on single chemicals and have addressed mixtures in more limited contexts (e.g., cumulative assessment of pesticides⁶ within mechanism-based classes, co-exposures at Superfund sites⁷).

Mixtures research—the study of the combined effects of chemicals and nonchemical stressors—is needed to provide the scientific basis for regulatory frameworks that fully account for real world exposures. This was highlighted in recent US Environmental Protection Agency (EPA) efforts to update practices associated with the Toxic Substances Control Act (TSCA),⁸ update the guidelines for cumulative risk assessment,⁹ and develop research priorities for addressing cumulative impacts.¹⁰ The overarching goal of mixtures research is to understand the contribution of complex environmental exposures to human disease. Component-based (i.e., bottom-up) research reduces the complexity of real-world exposures by focusing on known components (i.e., mixture components with available exposure and toxicity data) and evaluating defined mixtures with quantified constituents (Figure 1). Component-based approaches, typical in toxicology, gather data on individual chemicals and combinations thereof to evaluate hypotheses about the joint action of chemicals.¹¹ Alternatively, top-down research starts with complex exposures (e.g., the totality of exposures over a lifetime or “exposome,” exposures measured in epidemiological cohorts, whole mixtures with some unidentified fraction) and attempts to deconvolute the connections between those complex exposures and observed responses and identify toxicity drivers (Figure 1). Top-down research approaches can range from epidemiological studies that include real-world exposures¹² to toxicology studies that evaluate complex mixtures.¹³

Figure 1.

Approaches to mixtures research. Mixtures research can be “bottom-up” by reducing real-world mixtures to components (i.e., individual chemicals) and using additivity models to predict mixture effects or “top down” wherein complex whole mixtures related to the real-world exposure are evaluated. Whole mixtures can be variable, and approaches that compare across related mixtures are needed to ensure that data generated on one whole mixture can be used to estimate the risk associated with sufficiently similar data-poor mixtures. Both bottom-up and top-down approaches aim to inform intervention and exposure mitigation strategies to improve public health.

The National Institute of Environmental Health Sciences (NIEHS) has supported diverse mixtures research through both intramural and extramural programs.^14–18 In 2010, NIEHS created the Cross-Divisional Combined Exposures and Mixtures (CEM) Focus Group to consolidate information, provide strategic guidance, and foster collaboration between its intramural [Division of Intramural Research (DIR) and the Division of Translational Toxicology (DTT)—formerly the Division of the National Toxicology Program] and extramural [Division of Extramural Research and Training (DERT)] divisions. To craft an NIEHS mixtures research strategy, the group organized a workshop in 2011 titled “Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects.”¹⁶ Experts in exposure science, toxicology, epidemiology, statistics, and risk analysis representing all sectors (i.e., government, industry, academia, nongovernmental organizations) gathered to discuss critical areas for advancing the field. Cross-cutting themes that emerged from the workshop have influenced NIEHS mixtures research over the past decade (Table 1). Since 2012, the NIEHS Strategic Plan^33,34 has prioritized combined exposures and mixtures, recently highlighting them among five cross-divisional research focus areas targeted for programmatic development along with environmental health disparities, metabolomics, microbiome, and neuroscience.

Table 1.

Mixtures research needs identified in the 2011 NIEHS workshop “Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects” and related activities over the past decade.

Theme	NIEHS activities
Prioritization of chemicals/mixtures (e.g., use of NHANES, EWAS, maximum cumulative ratio)	Exposure and exposome grants,19,20 intramural and extramural biostatistics projects21,22,23
Translating mixtures research from in vitro to in vivo end points	Polycyclic Aromatic Compound Mixtures Assessment Program (PAC-MAP)24
Cross-disciplinary efforts (e.g., coordination between epidemiology and toxicology)	PRIME,12 intramural epidemiology work,25–27 Superfund Research Program (SRP)17
Development/refinement of both component-based and whole mixtures approaches	Component-based research efforts11 (e.g., PAC-MAP, PFAS); whole mixtures research28 (e.g., botanicals, SRP)
Sufficient similarity as a key approach	Botanicals29,30
Systems-based approaches for studying mixtures	Atherosclerosis workshop, Converging on Cancer workshop, chemical and nonchemical stressor projects31
Data collection and management	Botanical Safety Consortium32

Note: EWAS, environment-wide association studies; NHANES, National Health and Nutrition Examination Survey; NIEHS, National Institute of Environmental Health Sciences; PFAS, per- and polyfluoroalkyl substances.

In addition to NIEHS’s strong support for intramural mixtures research, funding for extramural work on mixtures has increased. The Superfund Research Program (SRP), mandated under the Superfund Amendments and Reauthorization Act of 1986,³⁵ addresses complex issues relevant to hazardous substances³⁶ and is particularly well-suited for supporting mixtures research. SRP Centers at Duke University, Harvard School of Public Health, Oregon State University, and Texas A&M have an explicit focus on mixtures. Beyond SRP funding, NIEHS also funds a significant body of investigator-initiated research on mixtures. A recent cursory portfolio analysis using the National Institutes of Health (NIH) RePORTER database (https://reporter.nih.gov/) resulted in almost 600 mixtures-focused projects funded and administered by NIEHS from fiscal year (FY) 2011 to FY 2021, compared to just over 350 projects from FY 2000 to FY 2010 when the word “mixtures” was used as a search term. Although, this portfolio has almost doubled in the past 10 years, grant recipients and reviewers have communicated to the authors that they have experienced challenges in the review process due to the complexity of mixtures research and the small number of experts focused on this research area (e.g., limited mixtures experts on grant review panels). The analysis does not account for the recent growth in the exposome grant portfolio³⁷ and the increased focus on nonchemical stressors (e.g., noise, temperature, social factors, lack of health care, socioeconomic stressors, and circadian rhythm disruption) combined with environmental chemical stressors within the NIEHS health disparities and environmental justice portfolio of grants.³⁸

In this commentary, we detail progress from the last decade and highlight persistent challenges requiring dedicated efforts. We also identify emerging issues that could shape mixtures research in the coming decade. Throughout, we highlight cross-disciplinary efforts, collaborative science, and policy implications.

Prioritization of Chemicals and Mixtures

Due to the staggering number of possible chemical combinations, the authors propose that approaches are required to prioritize mixtures that pose the greatest risk to public health. A first step is characterizing exposure patterns within diverse populations. The exposome concept^39,40 offers the most comprehensive starting point. The exposome, a term first coined by Wild³⁹ to encompass the totality of environmental exposures a person experiences, was later expanded to include all external and internal exposures throughout a person’s lifetime.⁴⁰ While the exposome represents the ultimate real-world mixture, we distinguish it from the term “mixture,” which we are using to describe a snapshot of combined exposures in time that does not typically include endogenous responses to external stimuli. NIEHS supports considerable research in the areas of exposure science and the exposome.^19,37 For example, the NIEHS Human Health Exposure Analysis Resource (HHEAR) program was developed to better understand how environmental exposures lead to disease and to promote the characterization of the exposome.⁴¹ Specifically, it enables NIH-funded researchers to measure environmental exposures and integrate their data with other datasets by providing access to laboratory, statistical, and data science analysis services.⁴¹ HHEAR-related projects have assessed exposures including phthalates,⁴² phenols,⁴² metals,⁵ per- and polyfluoroalkyl substances (PFAS),⁴³ and flame retardants⁴⁴ and associated biological responses including oxidative stress,⁴⁵ DNA damage,⁴⁶ and epigenomics.⁴⁷ Diseases of interest have included asthma,⁴⁸ diabetes,⁴⁹ autism,⁵⁰ and pregnancy outcomes.⁵¹ A list of all HHEAR publications can be found on the website.⁵²

The proliferation of exposome research has galvanized advances in nontargeted analysis (e.g., metabolomics), which uses high-resolution mass spectrometry (HRMS), chemical databases, and computational tools to detect and identify unknown chemicals.⁵³ Indeed, NIEHS supports intramural and extramural core laboratories (e.g., NIEHS Metabolomics Core, HHEAR, NIEHS Exposome Program) that conduct nontargeted analyses. These efforts have greatly enhanced our understanding of joint exposures and have identified contaminants of emerging concern.⁵⁴

Important obstacles remain in fully realizing the potential of the exposome concept.^55,56 Challenges include detecting the entire range of chemicals present in limited biological samples (e.g., blood), dealing with complex matrices and potential loss of unknown analytes during clean up steps, detecting very-low-level chemicals, lacking analytical standards for confirmation and quantification, identifying chemicals based on features detected in high-resolution mass spectrometry data, and accounting for changes over time.⁵⁵ A 2021 NIEHS workshop discussed the importance of geospatial resolution of exposure data.⁵⁷ The cross-disciplinary workshop discussion identified research areas that utilize geospatially resolved exposure data to inform epidemiological studies, including reduction of measurement errors and uncertainty in exposure estimates, as well as challenges in integration of data across multiple scales and sources.⁵⁷ Despite these challenges, we contend that information gathered for the purpose of establishing the exposome (e.g., human biomonitoring, environmental measurements using personal monitoring devices, stationary area monitoring, and nontargeted analysis) provide an excellent starting place for mixture prioritization efforts.

The potential of biomonitoring and exposure data for prioritizing mixtures can be seen in large cohort studies such as NIEHS’ PEGS (Personalized Environment and Genes Study).²¹ Recently, Lowe et al.²² examined the association of chemical mixtures to psoriasis and eczema. They found that a multipollutant model accounting for cumulative exposures elucidated contributions that would not have been identified using single-chemical analyses.²²

Two methods for prioritizing mixtures for study are the maximum cumulative ratio and environment-wide association studies (EWAS).¹⁶ The maximum cumulative ratio, proposed by Price and Han,⁵⁸ represents the hazard index divided by the highest individual chemical risk quotient and is used to identify whether a single chemical is driving hazard vs. multiple chemicals contributing, thereby warranting a cumulative risk assessment. Recent examples of its application include identifying priority mixtures in European populations based on biomonitoring data³ and in a US watershed to identify priority areas for targeted ecological risk assessment activities.⁵⁹ The maximum cumulative ratio also has clear policy implications based on its inclusion in the European Food Safety Authority’s (EFSA’s) guidance on harmonized methodologies for human health, animal health, and ecological risk assessment of combined exposure to multiple chemicals.⁶⁰

Environment-wide association studies (EWAS) were introduced in 2010 for prioritizing mixtures based on agnostic and nontargeted investigation of chemical associations with a particular disease.⁶¹ EWAS borrowed statistical approaches developed for genome-wide association studies to identify environmental exposures that could be linked to biomarkers of disease—both measured in biomonitoring efforts (e.g., the National Health and Nutrition Examination Survey). NIEHS scientists leveraged the PEGS cohort to investigate associations between environmental factors and cardiovascular disease.²³ While numerous EWAS have been conducted in the past decade, a recent review of EWAS identified challenges including standardization of study designs to allow for data integration and refining statistical testing methods.⁶² The authors concluded that EWAS offers a promising framework for generating hypotheses but requires further development to ensure replicability and reproducibility.⁶²

In addition, in vitro high-throughput screening of individual chemicals and combinations thereof was recommended for prioritization of chemical mixtures, which could be followed by targeted in vitro or in vivo studies to confirm findings or test specific mixture hypotheses. Large-scale high-throughput screening programs such as Tox21, a collaboration between the NIEHS, the EPA, the National Center for Advancing Translational Sciences, and the Food and Drug Administration (FDA),⁶³ have evaluated thousands of individual chemicals as well as several targeted mixtures in over 70 in vitro assays. NIEHS Superfund Center grantees have used a combination of exposure and in vitro screening data to prioritize mixtures for further study.⁶⁴ A cross-disciplinary team used silicone wrist bands as personal monitoring devices to measure over 1,500 chemicals and identified 14 frequently detected chemicals. They then used in vitro [i.e., primary normal human bronchial epithelial (NHBE) cells] and in vivo (i.e., developmental zebrafish) models to evaluate both the individual chemicals and their mixtures.⁶⁴ This approach minimizes the use of mammals, can determine the potential health effects in a faster timeframe, and can screen large numbers of chemicals at a time. We assert that these types of studies allow for the evaluation of interactions of chemicals and may assist in the prioritization of chemical mixtures for further study.

Translating Mixtures Research from in Vitro to in Vivo Endpoints

New Approach Methodologies (NAMs) do not have a harmonized definition⁶⁵ but generally refer to in silico, in vitro, and nonmammalian whole animal (e.g., developmental zebrafish, C. elegans) approaches for evaluating hazard and risk, with intact animals excluded from some definitions.⁶⁶ Regulatory agencies are keen on using predictive NAMs for evaluating risk and decreasing traditional animal testing (e.g., rodents, dogs, primates).^66,67 Technological advances such as three-dimensional (3D) culture models⁶⁸ and enriched databases for in silico evaluation like integrated chemical environment (ICE) developed by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM)⁶⁹ have increased the credibility of NAMs. The concept of adverse outcome pathways (AOPs) has provided a framework for linking early molecular and cellular events to apical end points that provide the basis for risk evaluation.⁷⁰ The use of NAMs in mixtures research is complicated by the potential for unanticipated interactions among mixture components as well as the compounding of the typical issues associated with NAMs (e.g., artifacts due to physicochemical properties like lipophilicity and volatility, matrix interference). NIEHS is dedicating significant intramural and extramural effort to evaluating and refining NAMs for mixture testing.

NIEHS DTT aims to incorporate in vitro and in silico evaluations into existing in vivo testing programs to compare results across methodologies, explore mechanisms of joint action, build confidence in NAM application to mixtures, and better define the domain of applicability for priority mixtures. These efforts reflect the broader shift from a hazard identification focus to a more translational mission within the DTT. For example, in collaboration with EPA scientists, mixtures of polycyclic aromatic compounds (PACs) are being evaluated in 28-d immunotoxicity studies in mice and an in vitro assay measuring aryl hydrocarbon receptor activation as part of the PAC Mixtures Assessment Program (PAC-MAP). In addition, in silico methods have been used to better define the PAC class and add context to the data currently being generated.²⁴ Similarly, PFAS mixtures tested in vivo will also be evaluated in human in vitro liver models. NIEHS grantees are using in vitro systems (i.e., human receptors transfected into COS cells) to evaluate the joint effects of PFAS mixtures at the peroxisome proliferator activated receptor $\mathrm{\alpha }$ ($\text{PPAR}\mathrm{\alpha }$) level by comparing observed mixture responses to those predicted using a generalized concentration addition model that accommodates full and partial agonists.⁷¹ We submit that case studies comparing mixtures data from NAMs and traditional in vivo studies are needed to understand the domains of applicability of different NAMs and build confidence in using resulting data for public health decision-making.

Cross-Disciplinary Efforts

The complexity of mixtures research requires multidisciplinary collaboration. There are fundamental differences in the way various disciplines approach mixtures research (Figure 1). Toxicology tends to reduce mixtures to controlled and manageable components, while epidemiology starts with the complexity of real-world exposures.⁷² We contend that effective public health decision-making requires cross-disciplinary collaboration and data integration. Several NIEHS-driven efforts have been aimed at developing multidisciplinary collaborations and programs to address key questions in mixtures research.

In 2015, the NIEHS organized a workshop on “Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology” that brought together experts in epidemiology, toxicology, and statistics.⁷³ The workshop aimed to explore novel statistical approaches designed to analyze complex chemical mixtures data in simulated and real-world epidemiological data sets. Key takeaways from the workshop were that the statistical method should be selected based upon consideration of the specific hypothesis or scientific question. Participants also stressed the need for sharing more simulated and real-world mixtures datasets among the research community to refine statistical methodologies.⁷³

Following the workshop, NIEHS created the Powering Research Through Innovative Methods in Epidemiology (PRIME) Request for Applications (RFA) to support projects on developing innovative statistical methods to address the methodological challenges of mixtures analysis while considering toxicology in modeling strategies and providing statistical approach resources to the broader research community.⁷⁴ Joubert et al.¹² reviewed progress on the PRIME program. Thus far, the PRIME Program has supported six projects focused on statistical approaches involving response surface methods,⁷⁵ Bayesian methods with variable selection,⁷⁶ principal component pursuit methods,⁷⁷ factor analysis for interactions,⁷⁸ lagged weighted quantile sum regression,⁷⁹ and explained variation methods,⁸⁰ among others. The workshop and subsequent PRIME funding mechanism have had a significant impact, helping to stimulate a notable increase in epidemiological studies using novel statistical methods to examine multiple exposures.

Discussions from the NIEHS Cross-Divisional CEM Focus Group led to a 2022 NIEHS and NCI workshop on “Complex Exposures in Breast Cancer: Unraveling the Role of Environmental Mixtures,” which brought together epidemiologists, cancer biologists, toxicologists, and statisticians. The workshop highlighted recent NIEHS intramural^81,82 and extramural^83,84 research on breast cancer and mixtures. Other cross-disciplinary collaborations emerging from the focus group include work to evaluate the chemical and toxicological variability of botanical products in the marketplace⁸⁵ and a pilot study measuring the bioactivity in samples collected from the Sister Study cohort, which are also undergoing nontargeted chemical analysis.

In addition to NIEHS collaborations involving intramural staff, the NIEHS supports a model of cross-disciplinary effort in mixtures research through the SRP, which includes research centers and other grant mechanisms for the past 35 years focused on biomedical and environmental science and engineering.³⁶ Specific SRP mixtures-focused programs and projects are discussed in more detail throughout.

Development/Refinement of Both Component-Based and Whole-Mixtures Approaches

The 2000 EPA Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures⁸⁶ presented three risk assessment paths (Figure 2). Based on data availability, these paths focus on the mixture in question, a similar mixture, or the known components of a mixture. Each path involves challenges and uncertainties that require research attention. NIEHS intramural and extramural research programs are aimed at informing both whole-mixture approaches that rely on either data from the mixture of interest or a related mixture(s) and component-based mixture approaches.

Figure 2.

Cumulative risk assessment options adapted from the 2000 US Environmental Protection Agency Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures.⁸⁶ Based on data availability, risk assessments can be conducted using data from the whole mixture of interest, data from a sufficiently similar mixture(s), or data from known mixture constituents.

Component-Based Mixtures Work

Component-based approaches reduce complex mixtures to a defined set of constituents for which adequate data are available. A recent EPA white paper describes advances in the component-based approach of dose addition commonly applied to chemical mixtures.⁸⁷ Although pragmatic, component-based approaches rely on several assumptions about the joint action of the chemicals. Polycyclic aromatic hydrocarbons (PAHs) provide an example of the component-based relative potency factor approach for estimating carcinogenic risk from exposure to mixtures.^88,89 There are many uncertainties and limitations of the component-based approach, as noted with PAHs—and relevant to other chemical classes.⁹⁰ PAHs represent a large class of structurally related compounds that are ubiquitous contaminants. They are created through incomplete combustion of organic matter and always exist as complex and dynamic mixtures. Although it is difficult to estimate the total number of PAHs and related compounds (i.e., heterocyclic and substituted PAHs), only a small fraction of PAHs has sufficient toxicity data available for use in quantitative cumulative risk evaluation. Furthermore, there are limited mixture data demonstrating that PAHs act through a common mechanism and display dose additivity (no interactions).⁹⁰ Recommendations for strengthening the scientific basis for cumulative risk assessment include expanding the number of PAHs that are monitored and leveraging short-term and NAM data to develop relative potencies for a wider swath of PAHs.

Current efforts within the NIEHS DTT are aimed at addressing some of the important issues identified in supporting component-based risk assessments of PAHs.^89,90 One of the objectives of the DTT’s CEM Program is to generate empirical evidence on component-based approaches and compare results to alternative whole-mixture approaches for assessing hazard. PACs, including parent PAHs as well as heterocyclic and substituted PAHs, are being used as a case study to rigorously evaluate select assumptions involved in the relative potency factor approach, refining statistical tools used in component-based mixture evaluations, expanding the scope to include structurally diverse PACs and noncarcinogenic effects of PACs (i.e., immunotoxicity), and exploring the utility of NAMs in PAC evaluation.⁹¹ Additionally, the DTT is collaborating with NIEHS grantees at Texas A&M University to evaluate global gene expression changes in liver tissues following exposure to individual PACs and PAC mixtures. Future component-based mixture evaluations will focus on environmentally relevant PFAS mixtures.

The SRP’s center grant mechanism (i.e., P42) has a long history of promoting component-based mixtures work. The Colorado State University SRP center focused on physiologically based-pharmacokinetic/pharmacodynamic (PB-PK/PD) modeling for mixtures, effects of a seven-chemical mixture on tumor promotion in a rodent hepatocarcinogenesis model, and ecosystem responses to a mixture of heavy metals.^92,93 The Boston University SRP Center focused on refining component-based models for dose additivity in the generalized additivity model^94,95 and evaluating exposure to nonchemical stressors (e.g., socioeconomic factors, social adversity), in combination with early life chemical exposures, that may alter adolescent and adult behavior.^96,97

The Oregon State University SRP Center studies include how humans metabolize carcinogenic PAHs and developmental toxicities of PAH mixtures using a high-throughput zebrafish model.⁹⁸ They have also deployed passive sampling devices to assess PAHs at Superfund sites to determine the effectiveness of remediation strategies as well as exposure assessment with the use of wearable wristbands.⁹⁹ Finally, they have employed new analytical approaches to assessing chemical changes in PAHs at Superfund sites and have collaborated with Native American communities to measure personal exposure to PAH mixtures.^100–103

Other SRP Centers are well-poised to support future mixtures research {e.g., Northeastern SRP Center (e.g., phthalate mixtures); University of Louisville SRP Center [e.g., volatile organic compound (VOC)] mixtures}, and Louisiana State University SRP Center [e.g., environmentally persistent free radicals (EPFRs)]. The SRP also maintains an R01 grant portfolio which focuses on remediation and prevention/intervention approaches to hazardous substances. For example, SRP funded investigator-initiated work to understand the effects of metals and their mixtures on the physical, chemical, and ecological environment in a contaminated stream system at the North Fork Clear Creek (NFCC) Superfund Site in central Colorado.^104,105

Whole-Mixtures Work

Whole-mixture approaches are frequently limited by the paucity of whole-mixture toxicity data, uncertainty about toxicity-drivers within complex mixtures, and a lack of established methods for evaluating sufficient similarity of related mixtures. In this section, we will focus on efforts to generate data on whole mixtures and to identify toxic constituents.

Natural disasters, such as hurricanes, can result in significant contamination events. Resulting mixtures of concern can be complex and dynamic, challenging cleanup efforts. SRP grantees are working to better understand the complex mixtures resulting from natural disasters. Work by the Texas A&M University SRP Center has been focused on mitigating the human health consequences of exposure to hazardous mixtures during environmental emergency-related contamination events focusing on sites in the Galveston Bay/Houston Ship Channel area.^106–109 Their other goal is to establish predictive in vitro methods for quantitative evaluation of the complex mixture-perturbed adverse outcome pathways and intra- and interindividual variability in toxicity.¹¹⁰

Mixtures resulting from biomass burning (e.g., wildfires, cookstove emissions) have important public health implications. In a 2014 NIEHS-organized symposium on “Assessing Exposures and Health Effects Related to Indoor Biomass Fuel Burning,” experts discussed best practices for measuring exposure to complex biomass burning emissions and research to improve cookstove technologies and interventions. Collaborative research stemming from the symposium engaged NIEHS, EPA, and RTI International researchers in evaluating the stability of cookstove emission samples generated under laboratory and simulated field conditions.¹¹¹ Current NIEHS efforts focus on preparing a monograph for the Report on Carcinogens on wood smoke.¹¹² Complementary to this, NIEHS-funded work has been aimed at understanding which constituents in wildfire smoke drive observed toxicity.¹¹³

The DTT has previously evaluated botanical dietary supplements such as black cohosh extract,^114–116 ephedra,¹¹⁷ and Ginkgo biloba extract.¹¹⁸ Botanical ingredients are complex mixtures that typically contain hundreds to thousands of constituents, which can vary in composition depending on multiple environmental and processing factors.¹¹⁹ Additionally, botanicals are an important public health concern due to widespread exposure at relatively high doses, variable quality across the marketplace, and examples of adverse events in consumers.¹¹⁹ As such, botanical ingredients are ideal for developing methods to improve testing and aid in data interpretation of complex mixtures to better understand potential health effects. Current DTT work is focused on generating toxicological data on botanicals and developing tools to interpret complex mixture data with projects on evaluating polypharmacokinetics (using metabolomics to follow the pharmacokinetics of complex mixtures)¹²⁰ and using bioassay guided fractionation.¹²¹ Finally, the DTT is playing a critical role in the Botanical Safety Consortium, a public–private partnership established through a memorandum of understanding between NIEHS, the FDA, and the Health and Environmental Sciences Institute (HESI), to provide scientific support for the use of in silico and in vitro approaches in the safety evaluation of botanicals.³²

Future work will extend tools and approaches developed using botanicals to other environmentally relevant complex mixtures. High-priority intramural research on complex mixtures includes evaluating different formulations of aqueous film forming foam (AFFF) using in vitro liver models (building on individual PFAS work, e.g., Rowan-Carroll et al.¹²²) assessing glyphosate formulations for genetic toxicity,¹²³ and exploring the associations of personal care products with disease in epidemiological studies.^124–128

Sufficient Similarity as a Key Approach

It is crucial to develop an evidence-based approach to determine when two related mixtures are sufficiently similar for data extrapolation.²⁸ Sufficient similarity determinations typically involve chemical analysis of complex mixtures, bioactivity profiling, and multivariate statistical methods. They have been applied to various complex mixtures including drinking water disinfection byproducts,¹²⁹ pyrethroid pesticides,¹³⁰ and petroleum substances.^131,132 We propose that continued method development and real-world case studies are necessary for regulatory application. Signaling the importance of these methods in mixtures risk assessment, the US EPA has developed a Mixtures Similarity Tool (MiST) based on the sufficient similarity approach described in Marshall et al.,¹³⁰ to aid in the evaluation of polychlorinated biphenyls.¹³³

The DTT has developed several case studies on determining sufficient similarity with black cohosh,²⁹ Echinacea,²⁹ and Ginkgo biloba³⁰ extract that incorporate nontargeted chemical analysis and in vitro bioassays. An ongoing cross-divisional project between DTT and DIR scientists aims to use nuclear magnetic resonance (NMR), mass spectrometry, and bioactivity profiling to evaluate sufficient similarity of blue cohosh, yohimbe, and goldenseal samples.⁸⁵ While the current focus of DTT’s complex mixtures research centers on botanicals, future plans include applying the methods to other complex mixtures (e.g., PAC-containing environmental samples).

Systems-Based Approaches for Studying Mixtures

We think that developing models that reflect the complexity of biological systems is important in mixtures research due to the potential for mixture constituents to interact with different cellular and molecular pathways that converge on common adverse outcomes. To address the goal of applying a systems biology approach to mixtures research, the NIEHS focused on atherosclerosis and carcinogenesis. The first workshop in 2018 cosponsored by NIEHS with the National Heart, Lung, and Blood Institute (NHLBI) was titled “Understanding the Combined Effects of Environmental Chemical and Non-Chemical Stressors: Atherosclerosis as a Model.” Workshop participants applied an AOP framework to elucidate the role of environmental chemicals and nonchemical stressors in atherosclerosis and discussed evidence for the impact and mechanisms of nonchemical stressors (e.g., depression/anxiety, poor sleep) on atherosclerosis. Participants recognized the complexity of studying the health effects posed by the combination of chemical and nonchemical stressors (personal communication). During the workshop, we noted that using the AOP framework to organize knowledge was useful for hypothesis generation, even though specific interaction pathway(s) were not definitively identified.

A second workshop organized by NIEHS in collaboration with EPA and University of California Berkeley (UC Berkeley) was held in 2019 and titled “Converging on Cancer.” Informed by the Halifax Project on exploring low-dose mixtures contribution to cancer,^134,135 this workshop focused on using our knowledge of complex cancer biology and characteristics of carcinogens to inform research on how combinations of chemicals could contribute jointly to cancer development. A subsequent publication captured recommendations for designing mixtures studies.³¹

NIEHS prioritizes developing research projects to increase our understanding of how chemical and nonchemical stressors can contribute cumulatively to disease development. This complements efforts at the US EPA¹³⁶ and by EuroMix researchers¹³⁷ that use AOP Networks to develop and test hypotheses about the joint action of chemicals with divergent mechanisms of action. Initial project exploration has focused on design considerations in building a mixtures program to evaluate combinations of chemicals that target cancer-relevant targets.³¹ Future research in this area promises to shift the paradigm of cumulative risk assessment from a focus on narrowly defined and similarly acting chemicals (e.g., organophosphates) to a more comprehensive evaluation of chemical and nonchemical stressors that target common diseases.

Data Collection and Management

Literature searches for mixtures research often encounter a significant number of studies on single chemicals. Despite discussion on developing a federal database to collect and curate data from mixtures studies, we could not identify meaningful progress in this endeavor. The Interaction Profiles for Toxic Substances, maintained by the Agency for Toxic Substances and Disease Registry (ATSDR) provide the only dedicated source for mixture-specific interaction information.¹³⁸ Software tools such as the Health Assessment Workplace Collaborative (HAWC) developed by DTT Integrative Health Assessments branch and the International Agency for Research on Carcinogens and Table Builder¹³⁹ can be leveraged to deconvolute large literature pools. Advances in literature extraction methods have driven the development of approaches to predict causal relationships (e.g., $\text{environmental\hspace{0.17em}factor}\to \text{disease}$) from existing literature using statistical machine learning or deep learning.¹⁴⁰ The NIH 2023 Data Sharing policy¹⁴¹ promises to significantly increase data sharing among mixtures researchers. Moving forward, it will be important to avoid identified pitfalls, such as legal and regulatory considerations, technical aspects, validation of data, misinterpretation of data, and difficulty with large data transfers.¹⁴² The NIEHS SRP, which supports cross-disciplinary focused centers, has also been a pioneer in the realm of data-sharing.¹⁴³

Large-scale efforts to combine and harmonize data from multiple collection efforts will provide computational toxicologists with rich datasets for mixtures evaluation. Examples of this include a collaborative effort to screen chemicals for developmental neurotoxicity,¹⁴⁴ the Consortium Linking Academic and Regulatory Insights on Bisphenol A Toxicity (CLARITY-BPA) project,¹⁴⁵ and the Botanical Safety Consortium.³²

Knowledge Gaps and Future Directions

Despite a significant amount of research effort dedicated to mixtures at NIEHS, there is still a long journey ahead with important knowledge gaps in understanding their health effects. While the mixtures research priorities at NIEHS reflect those of the wider mixtures research community, there are some important areas of research in which we are not currently engaged. For example, there are significant efforts in Europe to establish effect-based trigger values for mixtures assessments¹⁴⁶ and to derive mixtures assessment (or allocation) factors for use in risk assessment.¹⁴⁷

As discussed above, we are entering the age of data science and integration across large and diverse datasets, which brings both opportunities (e.g., building more predictive models for identifying and characterizing mixtures health risks) and significant challenges. Implementation of the NIH Data Sharing Policy in January 2023¹⁴¹ moves toward generating data that abides by the FAIR (findable, accessible, interoperable, and reproducible) principles.¹⁴⁸ Study design, access to data, organization of data, machine learning, artificial intelligence, and novel statistical approaches will all be key in optimizing use of mixtures-relevant datasets. The increased availability of complex analysis approaches offers promise for increasing the utility of large datasets and generating new hypotheses. However, we also recognize the need for careful consideration in matching the appropriate model to the question at hand and avoiding the pitfalls of “overfitting” data (i.e., finding patterns in background noise).^149,150 In addition, training the next generation of computational toxicologists will be critical. Development of datasets and training materials with mixtures-related examples, such as the InTelligence and Machine Learning (TAME) toolkit, will be particularly useful.¹⁵¹

There is growing interest from researchers, communities, and regulatory agencies for capturing a more comprehensive picture of risk from exposure to chemical and nonchemical stressors. The EPA recently published recommendations for cumulative impacts research¹⁰ in response to Executive Orders 13985 (FR Doc. 2021-01753) and 14008 (FR Doc. 2021-02177) aimed at advancing racial equity and supporting underserved communities and to address the climate crisis, respectively. Cumulative impact research involves evaluation of nonchemical stressors, which present challenges in epidemiological (e.g., biases in questionnaire data, the need to incorporate perception of stress) and toxicological (e.g., difficulty in developing animal models that accurately reflect human stressors) contexts. Furthermore, development of biomarkers that reflect cumulative stress (e.g., cortisol in hair and nails¹⁵²) requires additional research attention. Community engagement is required to determine mixtures exposures of concern and priority diseases with links to environmental exposures. Finally, we contend that better use of biomonitoring and geospatially resolved data is needed to move forward.¹⁵³

In our opinion, progress in exposomic research should be leveraged to inform prioritization of mixtures for study and to develop hypotheses that can be tested through mixtures research. Researchers at the UC Berkeley SRP Center have been actively pursuing the exposome,^154–156 especially early life exposures, effects of chemical mixtures and their impacts on remediation efforts, and the complex transformation of chemicals to reactive intermediates. Additional work is needed to harness exposomic data for evaluating the health impacts of mixtures. Furthermore, there has been little progress on better understanding how the timing of combined exposures can impact health outcomes.

Finally, progress on addressing these critical areas will require cross-disciplinary and collaborative research. We recommend the development of an international community of practice mixtures group comprised of researchers and end-users (risk analysists) that could serve as a resource for vetting mixtures research plans and evaluating the success of ongoing research.

Conclusions

Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges like environmental justice and climate change. NIEHS’s strategic planning for 2023–2028 has identified multiple areas related to mixtures as high priorities for research, including climate change and joint effects of chemical and nonchemical stressors. Epidemiological, toxicological, and statistical cross-disciplinary mixtures work will continue within the intramural divisions of NIEHS. Extramurally, multiple SRP Centers have increased work on mixtures issues, and we anticipate continued increases in investigator-initiated mixtures research.

Conclusions and opinions are those of the individual authors and do not necessarily reflect the policies or views of EHP Publishing or the National Institute of Environmental Health Sciences.