A framework for integrating evidence to assess hazards and risk.

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference.

Tackling the toxics in plastics packaging.

The widespread use of plastic packaging for storing, transporting, and conveniently preparing or serving foodstuffs is significantly contributing to the global plastic pollution crisis. This has led to many efforts directed toward amending plastic packaging's end of life, such as recycling, or alternative material approaches, like increasingly using paper for food packaging. But these approaches often neglect the critical issue of chemical migration: When contacting foodstuffs, chemicals that are present in packaging transfer into food and thus unwittingly become part of the human diet. Hazardous chemicals, such as endocrine disrupters, carcinogens, or substances that bioaccumulate, are collectively referred to as "chemicals of concern." They can transfer from plastic packaging into food, together with other unknown or toxicologically uncharacterized chemicals. This chemical transfer is scientifically undisputed and makes plastic packaging a known, and avoidable, source of human exposure to synthetic, hazardous, and untested chemicals. Here, I discuss this issue and highlight aspects in need of improvement, namely the way that chemicals present in food packaging are assessed for toxicity. Further, I provide an outlook on how chemical contamination from food packaging could be addressed in the future. Robust innovations must attempt systemic change and tackle the issue of plastic pollution and chemical migration in a way that integrates all existing knowledge.

Highly efficient molecular film for inhibiting volatilization of hazardous nitric acid.

Nitric acid, an important basic chemical raw material, plays an important role in promoting the development of national economy. However, such liquid hazardous chemicals are easy to cause accidental leakage during production, transportation, storage and use. The high concentration and corrosive toxic gas generated from decomposition shows tremendous harm to the surrounding environment and human life safety. Therefore, how to inhibit the volatilization of nitric acid and effectively control and block the generation of the toxic gas in the first time are the key to deal with the nitric acid leakage accident. Herein, a new method of molecular film obstruction is proposed to inhibit the nitric acid volatilization. The molecular film inhibitor spontaneously spread and form an insoluble molecular film on the gas-liquid interface, changing the state of nitric acid liquid surface and inhibiting the volatilization on the molecular scale. The inhibition rate up to 96% can be achieved below 45 °C within 400 min. Cluster structure simulation and energy barrier calculation is performed to elucidate the inhibition mechanism. Theoretical analysis of energy barrier shows that the specific resistance of the inhibitor significantly increased to 460 s·cm-1 at 45 °C, and the generated energy barrier is about 17,000 kJ·mol-1, which is much higher than the maximum energy required for nitric acid volatilization of 107.97 kJ·mol-1. The molecular film obstruction strategy can effectively inhibit the volatilization of nitric acid. This strategy paves the way for preventing the volatilization of liquid hazardous chemicals in accidental leakage treatment.

Review of dose setting for the extended one-generation reproductive toxicity studies (OECD TG 443): Considerations on ECHA's dose level selection recommendations.

During 2020, The European Chemicals Agency (ECHA) began evaluating the OECD Test Guideline 443: Extended One Generation Reproductive Toxicity Study (EOGRTS) to analyze specific aspects related to study design, conduct and toxicological findings. A significant outcome of this ECHA evaluation focused on adequate dose level selection. Subsequently, ECHA published recommendations for DART studies, however, these recommendations seemingly do not align with the principles of the 3Rs, animal welfare or human safety goals, specifically, regarding three aspects. First, the requirement to segregate testing for sexual function and fertility from the ability to produce normally developing offspring increases the risk of inadequate identification of postnatal hazards for development and sexual function and fertility, therefore failing human health protection goals. Second, the current ECHA high-dose level setting recommendations for EOGRTS exceed the MTD (Maximum Tolerated Dose), and therefore compromise the interpretation of the biological response relative to the intrinsic effect of the chemical under evaluation. Third, the combination of these aspects will result in an increase in the number of animals tested, increasing animal welfare concerns. This paper reflects the consensus of subject matter experts, professional, and scientific societies who have authored and signed on to this statement. The signatories encourage ECHA to adopt a revised science-driven approach to the dose selection criteria that strikes a balance between regulatory vigilance and scientific pragmatism.

Can "Hazard-Cost-Effectiveness Analysis" improve the risk management of chemicals under REACH?

Cost-Effectiveness Analysis (CEA) is a decision-making framework to prioritize policy decisions for chemicals. Differences in hazard profiles among chemicals are not integrated in CEA under the EU REACH Regulation, which could limit its relevance. Another concern is that two different economic decision support methods (CEA for chemicals considered as PBTs or vPvBs from a regulatory perspective and Cost Benefit Analysis (CBA) for others) are used under REACH. To address this situation, we define "Hazard" CEA by integrating a hazard score, based on persistence, bioaccumulation and (eco)toxicity, in the effect indicator of CEA. We test different designs and parameterizations of Hazard-CEA on a set of past socio-economic assessments under REACH for PBT and non-PBT chemicals. Weighing and thresholds in hazard scores do not have a significant impact on the outcome of Hazard-CEA but the design of the hazard scoring method does. We suggest using an integrated and unweighted scoring method with a multiplicative formulation based on the notion of risk. Hazard-CEA could be used for both PBT and non-PBT chemicals, to use a single method in REACH and therefore improve consistency in policy decisions. Our work also suggests that using Hazard-CEA could help make decision easier.

Predicting Endocrine Disruption Using Conformal Prediction - A Prioritization Strategy to Identify Hazardous Chemicals with Confidence.

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

Advancing Computational Toxicology by Interpretable Machine Learning.

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Physico-chemical and biological remediation techniques for the elimination of endocrine-disrupting hazardous chemicals.

Due to their widespread occurrence and detrimental effects on human health and the environment, endocrine-disrupting hazardous chemicals (EDHCs) have become a significant concern. Therefore, numerous physicochemical and biological remediation techniques have been developed to eliminate EDHCs from various environmental matrices. This review paper aims to provide a comprehensive overview of the state-of-the-art remediation techniques for eliminating EDHCs. The physicochemical methods include adsorption, membrane filtration, photocatalysis, and advanced oxidation processes. The biological methods include biodegradation, phytoremediation, and microbial fuel cells. Each technique's effectiveness, advantages, limitations, and factors affecting their performance are discussed. The review also highlights recent developments and future perspectives in EDHCs remediation. This review provides valuable insights into selecting and optimizing remediation techniques for EDHCs in different environmental matrices.

Advancing the science on chemical classes.

BACKGROUND: Hazard identification, risk assessment, regulatory, and policy activity are usually conducted on a chemical-by-chemical basis. Grouping chemicals into categories or classes is an underutilized approach that could make risk assessment and management of chemicals more efficient for regulators. OBJECTIVE AND METHODS: While there are some available methods and regulatory frameworks that include the grouping of chemicals (e.g.,same molecular mechanism or similar chemical structure) there has not been a comprehensive evaluation of these different approaches nor a recommended course of action to better consider chemical classes in decision-making. This manuscript: 1) reviews current national and international approaches to grouping; 2) describes how groups could be defined based on the decision context (e.g., hazard/risk assessment, restrictions, prioritization, product development) and scientific considerations (e.g., intrinsic physical-chemical properties); 3) discusses advantages of developing a decision tree approach for grouping; 4) uses ortho-phthalates as a case study to identify and organize frameworks that could be used across agencies; and 5) discusses opportunities to advance the class concept within various regulatory decision-making scenarios. RESULTS: Structural similarity was the most common grouping approach for risk assessment among regulatory agencies (national and state level) and non-regulatory organizations, albeit with some variations in its definition. Toxicity to the same target organ or to the same biological function was also used in a few cases. The phthalates case study showed that a decision tree approach for grouping should include questions about uses regulated by other agencies to encourage more efficient, coherent, and protective chemical risk management. DISCUSSION AND CONCLUSION: Our evaluation of how classes of chemicals are defined and used identified commonalities and differences based on regulatory frameworks, risk assessments, and business strategies. We also identified that using a class-based approach could result in a more efficient process to reduce exposures to multiple hazardous chemicals and, ultimately, reduce health risks. We concluded that, in the absence of a prescribed method, a decision tree approach could facilitate the selection of chemicals belonging to a pre-defined class (e.g., chemicals with endocrine-disrupting activity; organohalogen flame retardants [OFR]) based on the decision-making context (e.g., regulatory risk management).

Genetic association between the APOE ε4 allele, toxicant exposures and Gulf war illness diagnosis.

INTRODUCTION: Exposure to nerve agents, pyridostigmine bromide (PB), pesticides, and oil-well fires during the 1991 Gulf War (GW) are major contributors to the etiology of Gulf War Illness (GWI). Since the apolipoprotein E (APOE) ε4 allele is associated with the risk of cognitive decline with age, particularly in the presence of environmental exposures, and cognitive impairment is one of the most common symptoms experienced by veterans with GWI, we examined whether the ε4 allele was associated with GWI. METHODS: Using a case-control design, we obtained data on APOE genotypes, demographics, and self-reported GW exposures and symptoms that were deposited in the Boston Biorepository and Integrative Network (BBRAIN) for veterans diagnosed with GWI (n = 220) and healthy GW control veterans (n = 131). Diagnosis of GWI was performed using the Kansas and/or Center for Disease Control (CDC) criteria. RESULTS: Age- and sex-adjusted analyses showed a significantly higher odds ratio for meeting the GWI case criteria in the presence of the ε4 allele (Odds ratio [OR] = 1.84, 95% confidence interval [CI = 1.07-3.15], p ≤ 0.05) and with two copies of the ε4 allele (OR = 1.99, 95% CI [1.23-3.21], p ≤ 0.01). Combined exposure to pesticides and PB pills (OR = 4.10 [2.12-7.91], p ≤ 0.05) as well as chemical alarms and PB pills (OR = 3.30 [1.56-6.97] p ≤ 0.05) during the war were also associated with a higher odds ratio for meeting GWI case criteria. There was also an interaction between the ε4 allele and exposure to oil well fires (OR = 2.46, 95% CI [1.07-5.62], p ≤ 0.05) among those who met the GWI case criteria. CONCLUSION: These findings suggest that the presence of the ε4 allele was associated with meeting the GWI case criteria. Gulf War veterans who reported exposure to oil well fires and have an ε4 allele were more likely to meet GWI case criteria. Long-term surveillance of veterans with GWI, particularly those with oil well fire exposure, is required to better assess the future risk of cognitive decline among this vulnerable population.

Does REACH provide sufficient information to regulate substances toxic to reproduction?

Hazard classification and risk assessment of substances, is essential to protect workers and consumers from hazardous substances including reproductive toxicants. The ability to classify substances for reproductive toxicity under the current REACH information requirements has been assessed. For low tonnage substances (<10 ton per annum (tpa)) information for classification is insufficient. When only a reproductive screening study is available (10-100 tpa), substances are mostly not classified in Category 1B as developmental and non-potent fertility effects may be missed. The information requirements could be improved by automatic triggering of follow-up studies in case of a Category 2 classification based on a screening study. Additionally, a study could be added to the information requirements for substances produced at 1-10 tpa. Performing a risk assessment is often problematic due to the limited study requirements at low tonnage levels. Only for substances produced at more than 100 tpa, there is a high likelihood to detect reproductive effects and perform accurate risk assessment provided that the extended-one-generation-reproductive-toxicity-study and/or extra cohorts are triggered where required. Regardless of the tonnage level, no specific studies on lactation are required. With this paper we intend to contribute to the discussion on the information requirements for reproductive toxicity in view of the REACH revision.

Identification of molecular initiating events (MIE) using chemical database analysis and nuclear receptor activity assays for screening potential inhalation toxicants.

An adverse outcome pathway (AOP) framework can facilitate the use of alternative assays in chemical regulations by providing scientific evidence. Previously, an AOP, peroxisome proliferative-activating receptor gamma (PPARγ) antagonism that leads to pulmonary fibrosis, was developed. Based on a literature search, PPARγ inactivation has been proposed as a molecular initiating event (MIE). In addition, a list of candidate chemicals that could be used in the experimental validation was proposed using toxicity database and deep learning models. In this study, the screening of environmental chemicals for MIE was conducted using in silico and in vitro tests to maximize the applicability of this AOP for screening inhalation toxicants. Initially, potential inhalation exposure chemicals that are active in three or more key events were selected, and in silico molecular docking was performed. Among the chemicals with low binding energy to PPARγ, nine chemicals were selected for validation of the AOP using in vitro PPARγ activity assay. As a result, rotenone, triorthocresyl phosphate, and castor oil were proposed as PPARγ antagonists and stressor chemicals of the AOP. Overall, the proposed tiered approach of the database-in silico-in vitro can help identify the regulatory applicability and assist in the development and experimental validation of AOP.

Safe handling of hazardous drugs.

Background: This evidence-based practice guideline was developed to update and address new issues in the handling of hazardous drugs including being compliant with NAPRA (National Association of Pharmacy Regulatory Authorities) and USP 800 (United States Pharmacopeia) standards, the use of personal protective equipment and treatment in diverse settings including in the home setting. Methods: This guideline was developed from an adaptation and endorsement of existing guidelines and from three systematic reviews. Prior to publication, this guideline underwent a series of peer, patient, methodological and external reviews to gather feedback. All comments were addressed and the guideline was amended when required. This guideline applies to and is intended for all health care workers who may come into contact with hazardous drugs at any point in the medication circuit. Results: The recommendations represent a reasonable and practical set of procedures that the intended users of this guideline should implement to minimize the opportunity for accidental exposure. These recommendations are not limited to just the point of care, but cover the entire chain of handling of cytotoxics from the time they enter the institution until they leave in the patient or as waste. Conclusions: Decreasing the likelihood of accidental exposure to cytotoxic agents within the medication circuit is the main objective of this evidenced-based guideline. The recommendations differ slightly from previous guidelines due to new evidence.

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

Exposure to Hazardous Substances and Their Health Effects Among Residents Living Near Three Industrial Waste Incinerators in Korea.

BACKGROUND: This study aimed to evaluate exposure to various hazardous substances emitted by incineration facilities and their likely effect on the health for residents of Bugi-myeon, Cheongju, Korea, which has three incineration facilities. METHODS: Heavy metals, polycyclic aromatic hydrocarbons (PAHs), and dioxin concentrations in the air and soil of exposed and control areas were measured. Moreover, the exposure levels to harmful substances and its effects on health were investigated in 1,124 exposed and 232 control adults. RESULTS: PAHs and dioxin concentrations in the air in the exposed area were significantly higher than in the control area. Urinary cadmium and PAHs metabolite concentrations were significantly higher in the exposed group than in the control group. The exposure group also had a higher prevalence of depression and self-reported allergic symptoms than the control group. CONCLUSION: The possibility of residents in Bugi-myeon being exposed to hazardous substances at incineration facilities cannot be ruled out. To prevent them from further exposure to hazardous substances, it is necessary to prohibit the expansion of additional incineration facilities in this area and to implement continuous monitoring projects for residents.

The application of acute oral toxicity computational models in dangerous goods classification.

Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.

Analysis of dermal exposure assessment in the US Environmental Protection Agency Toxic Substances Control Act risk evaluations of chemical manufacturing.

The United States Environmental Protection Agency (EPA) regulates chemical manufacture, import, processing, distribution, use, and disposal under the 2016 amended Toxic Substances Control Act (TSCA) for the purposes of protecting the public and sensitive populations-including workers-from chemical exposure risk. The publication of several TSCA risk evaluations provided a unique opportunity to evaluate the evolving regulatory approach for assessing the dermal exposure pathway in occupational settings. In this analysis, the occupational dermal exposure assessment methods employed in several TSCA risk evaluations were assessed. Specifically, a methodology review was conducted for the occupational dermal scenarios of manufacturing and feedstock use in the risk evaluations of three chlorinated organic chemicals: trichloroethylene, carbon tetrachloride, and perchloroethylene. Additionally, alternative exposure estimates were generated using the exposure model IH SkinPermTM. The review and alternative exposure analyses indicate that the current TSCA modeling approach may generate total dermal absorbed doses for chlorinated chemical manufacturing and feedstock use scenarios that are 2- to 20-fold higher than those generated by IH SkinPerm. Best-practice recommendations developed in the methodology review support a tiered, integrated approach to dermal exposure assessment that emphasizes collecting qualitative data; employing validated, peer-reviewed models that align with current industrial practices; and gathering empirical sampling data where needed. Collaboration among industry, EPA, and other stakeholders to share information and develop a standard approach to exposure assessment under TSCA would improve the methodological rigor of, and increase confidence in, the risk evaluation results.

Toxic Tire Wear Compounds (6PPD-Q and 4-ADPA) Detected in Airborne Particulate Matter Along a Highway in Mississippi, USA.

Tire wear particles (TWPs) are a major category of microplastic pollution produced by friction between tires and road surfaces. This non-exhaust particulate matter (PM) containing leachable toxic compounds is transported through the air and with stormwater runoff, leading to environmental pollution and human health concerns. In the present study, we collected airborne PM at varying distances (5, 15 and 30 m) along US Highway 278 in Oxford, Mississippi, USA, for ten consecutive days using Sigma-2 passive samplers. Particles (~ 1-80 µm) were passively collected directly into small (60 mL) wide-mouth separatory funnels placed inside the samplers. Particles were subsequently subjected to solvent extraction, and extracts were analyzed for TWP compounds by high resolution orbitrap mass spectrometry. This pilot study was focused solely on qualitative analyses to determine whether TWP compounds were present in this fraction of airborne PM. The abundance of airborne TWPs increased with proximity to the road with deposition rates (TWPs cm-2 day-1) of 23, 47, and 63 at 30 m, 15 m, and 5 m from the highway, respectively. Two common TWP compounds (6PPD-Q and 4-ADPA) were detected in all samples, except the field blank, at levels above their limits of detection, estimated at 2.90 and 1.14 ng L-1, respectively. Overall, this work suggests airborne TWPs may be a potential inhalation hazard, particularly for individuals and wildlife who spend extended periods outdoors along busy roadways. Research on the bioavailability of TWP compounds from inhaled TWPs is needed to address exposure risk.

Population susceptibility: A vital consideration in chemical risk evaluation under the Lautenberg Toxic Substances Control Act.

The 2016 Frank Lautenberg Chemical Safety for the 21st Century Act (Lautenberg TSCA) amended the 1976 Toxic Substances Control Act (TSCA) to mandate protection of susceptible and highly exposed populations. Program implementation entails a myriad of choices that can lead to different degrees of public health protections. Well-documented exposures to multiple industrial chemicals occur from air, soil, water, food, and products in our workplaces, schools, and homes. Many hazardous chemicals are associated with or known to cause health risks; for other industrial chemicals, no data exist to confirm their safety because of flaws in 1976 TSCA. Under the 2016 Lautenberg amendments, the United States Environmental Protection Agency (EPA) must evaluate chemicals against risk-based safety standards under enforceable deadlines, with an explicit mandate to identify and assess risks to susceptible and highly exposed populations. Effective public health protection requires EPA to implement the Lautenberg TSCA requirements by incorporating intrinsic and extrinsic factors that affect susceptibility, adequately assessing exposure among vulnerable groups, and accurately identifying highly exposed groups. We recommend key scientific and risk assessment principles to inform health-protective chemical policy such as consideration of aggregate exposures from all pathways and, when data are lacking, the use of health-protective defaults.