Current practice and recommendations for advancing how human variability and susceptibility are considered in chemical risk assessment

Ethical principles for regulatory risk decision-making

Risk assessors, managers, and decision-makers are responsible for evaluating diverse human, environmental, and animal health risks. Although the critical elements of risk assessment and management are well-described in national and international documents, the ethical issues involved in risk decision-making have received comparatively little attention to date. To address this aspect, this article elaborates fundamental ethical principles designed to support fair, balanced, and equitable risk-based decision-making practices. Experts and global thinkers in risk, health, regulatory, and animal sciences were convened to share their lived experiences in relation to the intersection between risk science and analysis, regulatory science, and public health. Through a participatory and knowledge translation approach, an integrated risk decision-making model, with ethical principles and considerations, was developed and applied using diverse, contemporary risk decision-making and regulatory contexts. The ten principles - autonomy, minimize harm, maintain respect and trust, adaptability, reduce disparities, holistic, fair and just, open and transparent, stakeholder engagement, and One Health lens - demonstrate how public sector values and moral norms (i.e., ethics) are relevant to risk decision-making. We also hope these principles and considerations stimulate further discussion, debate, and an increased awareness of the application of ethics in identifying, assessing, and managing health risks.

Models to evaluate the pulmonary toxicity of desert dust and what we have learned from them so far: a mini-review

Millions of people worldwide are exposed to aerosolised desert dust and are at risk of the adverse respiratory health effects it causes. This mini-review gives an overview of the study types that can be used to assess the respiratory toxicity of desert dust and the insights gained from these studies. We highlight the main advantages and disadvantages of epidemiological, in vivo, and in vitro studies. Regarding in vitro studies, we discuss models of increasing complexity, i.e., traditional submerged cell cultures, air-liquid interface cultures, organ-on-a-chip models, organoids, and precision-cut lung slices. Epidemiological studies have shown increased short-term mortality and exacerbated acute and chronic respiratory diseases after desert dust events. In contrast, a connection to the onset of chronic diseases is more difficult to prove. In vivo and in vitro studies have particularly addressed the cellular and molecular effects of desert dust. It was found that desert dust activates immune cells and induces the expression of inflammatory cytokines and oxidative stress markers. The specific effects and their extent vary between dust samples from different sources. The investigation of the role of the composition is still immature and needs further effort including more extensive screenings. The advancement of easy-to-handle and realistic pulmonary in vitro models is required to automate screenings, support mechanistic insights, and enable the assessment of long-term exposure scenarios. In agreement with striving to develop new approach methodologies, such advancements can reduce and replace animal experiments and strongly benefit the translatability of research outcomes to human health protection.

Environmental concentrations of TCEP and TDCIPP induce dysbiosis of gut microbiotal and metabolism in the honeybee (Apis mellifera L.)

Organophosphorus flame retardants (OPFRs) have emerged as significant global pollutants, yet their harmful effects on pollinating insects remain largely unexplored. This study explored the toxicological effects of tri(2-chloroethyl) phosphate (TCEP) and tri(1,3-dichloro-2-propyl) phosphate (TDCIPP) on the gut microbiota and metabolic pathways of honeybees (Apis mellifera L.). Exposure to TCEP led to a 35 % reduction in intestinal wall thickness and significantly suppressed the expression of pyrimidine metabolism-associated enzymes, including CAD, DHODH, and ODCase (p < 0.05). In contrast, TDCIPP exposure increased the relative abundance of Snodgrassella and Lactobacillus by 40 % and 25 %, respectively, while exerting more extensive toxicity by disrupting nucleotide metabolism, oxidative stress responses, and microbial diversity. Histological assessments revealed that both chemicals compromised intestinal wall integrity and induced crypt loss in the midgut epithelium. Multi-omics analyses underscored distinct toxicity mechanisms: TCEP primarily inhibited pyrimidine biosynthesis, impairing nucleotide synthesis and DNA repair processes, whereas TDCIPP caused broader metabolic disturbances, likely attributed to its greater hydrophobicity. Notably, the enhanced prevalence of certain microbial taxa suggests potential microbial adaptations to TDCIPP-induced stress. This comparative analysis highlighted the detrimental effects of TCEP and TDCIPP on gut health and metabolism, critical factors for honeybee survival and ecological function. These findings underscored the urgent need for further investigation into the ecological hazards posed by OPFRs and provided a basis for developing mitigation strategies to address the impacts of persistent organic pollutants (POPs) on pollinators.

Development of the Navigation Guide Evidence-to-Decision Framework for Environmental Health: Version 1.0

Environmental exposures, including widespread industrial pollution, impact human health and are amplified in more highly exposed communities. Policy and regulatory frameworks for making decisions and recommendations on interventions to mitigate or prevent exposures tend to narrowly focus on exposure and some health-related data related to risks. Typically, such frameworks do not consider other factors, including essentiality, health equity, and distribution of benefits and costs. Further, decisions and recommendations lack transparency regarding how they were developed. We developed the Navigation Guide Evidence-to-Decision Framework for Environmental Health (E2DFEH) to provide a structured and transparent framework incorporating a range of scientific information and factors for decision-making. We reviewed current evidence-to-decision frameworks and engaged in an iterative consensus-based process involving 30 experts from 25 organizations in the academic, government, and nonprofit sectors. The E2DFEH framework includes three Foundations that are structural factors considered as part of recommendation development: 1) Essentiality, 2) Human Rights, and 3) Quality of the Evidence. It also includes three core Criteria that guide the development of a specific recommendation, informed by an evaluation of relevant evidence: 1) Environmental Justice, 2) Maximizing Benefits and Reducing Harm, and 3) Sociocultural Acceptability and Feasibility. The framework's goal is to make the decision process transparent and comprehensive through explicit consideration of core factors important for decisions, leading to more equitable and health-protective interventions.

Identifying studies evaluating susceptibility factors for chemical health assessments: A case study focused on methylmercury developmental neurotoxicity

Identifying susceptibility factors for adverse health effects from chemical exposures is an important aspect of characterizing human health impacts. However, to date, an efficient approach for identifying these factors has not been established. To address this limitation, two approaches were utilized to find studies that contained susceptibility information using methylmercury (MeHg) developmental neurotoxicity (DNT) as a case study. Both approaches start with a comprehensive literature search of 5 databases on MeHg followed by keyword filtering for potential epidemiology studies; however, the approaches diverged for the subsequent steps. Approach 1 initially included screening of all 7,531 studies captured by the human filter, but was modified when it was determined that 96% of studies found to include susceptibility information were captured by a dose-response filter. Approach 2 developed a susceptibility filter to limit the screening needed. Approach 1 resulted in the identification of 172 studies with information on MeHg DNT susceptibility. Approach 2 reduced screening by 52%, but only captured 74% of PECO-relevant studies when applied to the final study set. Although Approach 2 reduced screening by 12% compared with the use of the dose-response filter in Approach 1, the decreased detection of relevant studies precludes its use in most cases. Expected technological advances that allow refinement of a susceptibility filter to improve performance would be advantageous because of the potential further reduction in screening burden. However, at this time, Approach 1, involving the application of a dose-response filter, is currently recommended for identifying epidemiology papers with information on susceptibility factors.

Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective

Trace metal pollution is primarily driven by industrial, agricultural, and mining activities and presents complex environmental challenges with significant implications for ecological and human health. Traditional methods of environmental risk assessment (ERA) often fall short in addressing the intricate dynamics of trace metals, necessitating the adoption of advanced statistical techniques. This review focuses on integrating contemporary statistical methods, such as Bayesian modeling, machine learning, and geostatistics, into ERA frameworks to improve risk assessment precision, reliability, and interpretability. Using these innovative approaches, either alone or preferably in combination, provides a better understanding of the mechanisms of trace metal transport, bioavailability, and their ecological impacts can be achieved while also predicting future contamination patterns. The use of spatial and temporal analysis, coupled with uncertainty quantification, enhances the assessment of contamination hotspots and their associated risks. Integrating statistical models with ecotoxicology further strengthens the ability to evaluate ecological and human health risks, providing a broad framework for managing trace metal pollution. As new contaminants emerge and existing pollutants evolve in their behavior, the need for adaptable, data-driven ERA methodologies becomes ever more pressing. The advancement of statistical tools and interdisciplinary collaboration will be essential for developing more effective environmental management strategies and informing policy decisions. Ultimately, the future of ERA lies in integrating diverse data sources, advanced analytical techniques, and stakeholder engagement, ensuring a more resilient approach to mitigating trace metal pollution and protecting environmental and public health.

Next Generation Risk Assessment to Address Disease-Related Vulnerability-A Proof of Concept for the Sunscreen Octocrylene

Risk assessment for cosmetics in the European Union (EU) are triggered by a ban on animal testing and concerns of endocrine disruption (ED). The risk assessment focuses on healthy populations and, for potential ED, includes specific developmental stages as vulnerable due to specific concerns on endocrine effects. However, the assessment focuses on healthy individuals and does not consider that some pathologies may increase dermal absorption and even vulnerability to endocrine disruptors. Data from the EU risk assessment, human pharmacokinetic studies and ToxCast bioactivity were combined in a hypothesis-driven Next-Generation Risk Assessment to identify possible risk drivers for vulnerable populations including oncological patients and atopic dermatitis. In vitro effects are observed at concentration in the order of measured plasmatic levels under normal use patterns. The induction of hepatic enzymes is the most relevant bioactivity endpoint, in line with animal findings. The information on endocrine potential is inconclusive, and the possibility for skin effects and endocrine mechanism linked to tumor induction require further elucidation. The information on octocrylene (CAS number: 6197-30-4) bioactivity is limited, lacking information on the metabolites and the immunotoxicity potential, particularly relevant for oncological patients.

Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!

Regulatory dose-response assessments traditionally rely on in vivo data and default assumptions. New Approach Methods (NAMs) present considerable opportunities to both augment traditional dose-response assessments and accelerate the evaluation of new/data-poor chemicals. This review aimed to determine the potential utilization of NAMs through a unified conceptual framework that compartmentalizes derivation of toxicity values into five sequential Key Dose-response Modules (KDMs): (1) point-of-departure (POD) determination, (2) test system-to-human (e.g. inter-species) toxicokinetics and (3) toxicodynamics, (4) human population (intra-species) variability in toxicodynamics, and (5) toxicokinetics. After using several "traditional" dose-response assessments to illustrate this framework, a review is presented where existing NAMs, including in silico, in vitro, and in vivo approaches, might be applied across KDMs. Further, the false dichotomy between "traditional" and NAMs-derived data sources is broken down by organizing dose-response assessments into a matrix where each KDM has Tiers of increasing precision and confidence: Tier 0: Default/generic values, Tier 1: Computational predictions, Tier 2: Surrogate measurements, and Tier 3: Direct measurements. These findings demonstrated that although many publications promote the use of NAMs in KDMs (1) for POD determination and (5) for human population toxicokinetics, the proposed matrix of KDMs and Tiers reveals additional immediate opportunities for NAMs to be integrated across other KDMs. Further, critical needs were identified for developing NAMs to improve in vitro dosimetry and quantify test system and human population toxicodynamics. Overall, broadening the integration of NAMs across the steps of dose-response assessment promises to yield higher throughput, less animal-dependent, and more science-based toxicity values for protecting human health.

Quantifying the effect of human interindividual kinetic differences on the relative potency value for riddelliine N-oxide at low dose levels by a new approach methodology

Pyrrolizidine alkaloid N-oxides (PA-N-oxides) are predominant in plants and herbal foods, and are converted to pyrrolizidine alkaloids (PAs) upon consumption, leading to toxicity. The effect of interindividual kinetic differences on the relative potency values of PA-N-oxides compared to their PAs (REPPANO to PA) was studied, with riddelliine N-oxide (RIDO) and riddelliine (RID) as model compounds. In vitro kinetic data measured in incubations with 30 fecal and 25 liver S9 donor samples showed high variation across individuals, where the interindividual variability was captured with Bayesian multilevel regression. The distributions of influential PBK model parameters were used as input for physiologically based kinetic (PBK) modeling combined with Monte Carlo (MC) simulations to calculate the probability distribution of REPRIDO to RID values. At low dose levels, interindividual differences were shown to be a factor that influences the REPRIDO to RID value while neither dose nor endpoint used plays a role. The distribution of the REPRIDO to RID value ranged from 0.71 to 0.97 (95th percentile) with a mean value of 0.87. The approach described enables determination of interindividual REPPANO to PA values at low dose levels, which are not accessible in in vivo experiments quantifying the REPPANO to PAvalue.

Maternal pesticide exposure and risk of birth defects: a population-based cross-sectional study in China

Objective

This study aimed to examine the association between maternal pesticide exposure during the periconceptional period and birth defects in their offspring.

Methods

A survey was conducted among 29,204 women with infants born between 2010 and 2013 in Shaanxi Province, Northwest China. All cases of birth defects were diagnosed using the International Classification of Diseases, Tenth Revision (ICD-10). Given the multistage sampling design, the generalized estimating equation (GEE) binomial regression models with log link and exchangeable correlation structures were used to analyze the association between maternal pesticide exposures and birth defects.

Results

Among the 29,204 subjects, 562 mothers had children with birth defects, resulting in an incidence rate of 192.44 per 10,000 live births. The incidence of birth defects was higher in the pesticide-exposed group compared to the control group (737.46/10,000 vs. 186.04/10,000). After adjusting for baseline demographic characteristics, fertility status, nutritional factors, and environmental factors in the GEE model, the results indicated that the risk of birth defects and cardiovascular system defects in mothers exposed to pesticides during the periconceptional period was 2.39 times (95% CI: 1.84-3.10) and 3.14 times (95% CI: 1.73-5.71) higher, respectively, compared to the control group.

Conclusion

This study demonstrated that maternal exposure to pesticides during the periconceptional period was associated with an increased risk of birth defects, particularly cardiovascular system defects in offspring. Consequently, it would be beneficial to avoid pesticide exposure from three months before pregnancy through the first trimester to lower birth defects in infants.

Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Elucidation of endogenous and exogenous chemicals in maternal serum using high-resolution mass spectrometry

The increasing exposure to environmental chemicals calls for comprehensive non-targeted analysis to detect unrecognized substances in human samples. We examined human serum samples to classify compounds as endogenous or exogenous using public databases and to explore the relationships between exposure markers and metabolic patterns. Serum samples from 84 pregnant women at 32 weeks gestation were analyzed using LC-QToFMS. Using the PubChemLite for Exposomics database, we annotated and classified 106 compounds (51 endogenous, 55 exogenous). The compound patterns were analyzed using three dimensional reduction methods: Principal Component Analysis (PCA), regularized Generalized Canonical Correlation Analysis (rGCCA), and Uniform Manifold Approximation and Projection (UMAP). OPTICS clustering applied to these methods revealed two distinct clusters, with 89 % of significant compounds overlapping between clusters. The detected exogenous compounds included dietary substances, phthalates, nitrogenous compounds, and parabens. Pathway enrichment analysis showed that chemical exposure was linked to changes in amino acid metabolism, protein and mineral transport, and energy metabolism. While we found associations between exposure and metabolite changes, we could not establish causality. Our approach of analyzing both exogenous and endogenous chemicals from the same dataset using PubChemLite database presents a new method for exposome research, despite limitations in sample size and peak annotation accuracy. These findings contribute to understanding multiple chemical exposures and their metabolic effects in human biomonitoring.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

BACKGROUND

Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

OBJECTIVES

We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

METHODS

We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there.

RESULTS

We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states.

DISCUSSION

Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

The regulation of endocrine-disrupting chemicals to minimize their impact on health

Endocrine-disrupting chemicals (EDCs) are substances generated by human industrial activities that are detrimental to human health through their effects on the endocrine system. The global societal and economic burden posed by EDCs is substantial. Poorly defined or unenforced policies can increase human exposure to EDCs, thereby contributing to human disease, disability and economic damage. Researchers have shown that policies and interventions implemented at both individual and government levels have the potential to reduce exposure to EDCs. This Review describes a set of evidence-based policy actions to manage, minimize or even eliminate the widespread use of these chemicals and better protect human health and society. A number of specific challenges exist: defining, identifying and prioritizing EDCs; considering the non-linear or non-monotonic properties of EDCs; accounting for EDC exposure effects that are latent and do not appear until later in life; and updating testing paradigms to reflect 'real-world' mixtures of chemicals and cumulative exposure. A sound strategy also requires partnering with health-care providers to integrate strategies to prevent EDC exposure in clinical care. Critical next steps include addressing EDCs within global policy frameworks by integrating EDC exposure prevention into emerging climate policy.

Characterizing metals in particulate pollution in communities at the fenceline of heavy industry: combining mobile monitoring and size-resolved filter measurements

Exposures to metals from industrial emissions can pose important health risks. The Chester-Trainer-Marcus Hook area of southeastern Pennsylvania is home to multiple petrochemical plants, a refinery, and a waste incinerator, most abutting socio-economically disadvantaged residential communities. Existing information on fenceline community exposures is based on monitoring data with low temporal and spatial resolution and EPA models that incorporate industry self-reporting. During a 3 week sampling campaign in September 2021, size-resolved particulate matter (PM) metals concentrations were obtained at a fixed site in Chester and on-line mobile aerosol measurements were conducted around Chester-Trainer-Marcus Hook. Fixed-site arsenic, lead, antimony, cobalt, and manganese concentrations in total PM were higher (p < 0.001) than EPA model estimates, and arsenic, lead, and cadmium were predominantly observed in fine PM (<2.5 μm), the PM fraction which can penetrate deeply into the lungs. Hazard index analysis suggests adverse effects are not expected from exposures at the observed levels; however, additional chemical exposures, PM size fraction, and non-chemical stressors should be considered in future studies for accurate assessment of risk. Fixed-site MOUDI and nearby mobile aerosol measurements were moderately correlated (r ≥ 0.5) for aluminum, potassium and selenium. Source apportionment analyses suggested the presence of four major emissions sources (sea salt, mineral dust, general combustion, and non-exhaust vehicle emissions) in the study area. Elevated levels of combustion-related elements of health concern (e.g., arsenic, cadmium, antimony, and vanadium) were observed near the waste incinerator and other industrial facilities by mobile monitoring, as well as in residential-zoned areas in Chester. These results suggest potential co-exposures to harmful atmospheric metal/metalloids in communities surrounding the Chester-Trainer-Marcus Hook industrial area at levels that may exceed previous estimates from EPA modeling.

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.