Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure-Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR's use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR's work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers' widespread appraisal and adaptation of the agency's methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

BACKGROUND: Plastic debris pervades our environment. Some breaks down into microplastics (MPs) that can enter and distribute in living organisms causing effects in multiple target organs. MPs have been demonstrated to harm animals through environmental exposure. Laboratory animal studies are still insufficient to evaluate human impact. And while MPs have been found in human tissues, the health effects at environmental exposure levels are unclear. AIM: We reviewed and summarized existing evidence on health effects from occupational exposure to MPs. Additionally, the diverse effects documented for workers were organized by MP type and associated co-contaminants. Evidence of the unique effects of polyvinyl chloride (PVC) on liver was then highlighted. METHODS: We conducted two stepwise online literature reviews of publications focused on the health risks associated with occupational MP exposures. This information was supplemented with findings from animal studies. RESULTS: Our analysis focused on 34 published studies on occupational health effects from MP exposure with half involving exposure to PVC and the other half a variety of other MPs to compare. Liver effects following PVC exposure were reported for workers. While PVC exposure causes liver toxicity and increases the risk of liver cancers, including angiosarcomas and hepatocellular carcinomas, the carcinogenic effects of work-related exposure to other MPs, such as polystyrene and polyethylene, are not well understood. CONCLUSION: The data supporting liver toxicity are strongest for PVC exposure. Overall, the evidence of liver toxicity from occupational exposure to MPs other than PVC is lacking. The PVC worker data summarized here can be useful in assisting clinicians evaluating exposure histories from PVC exposure and designing future cell, animal, and population exposure-effect research studies.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity. © 2022 Elsevier B.V.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Cyanides are highly toxic chemicals found indoors and outdoors, in air, water, and soil. Environmental exposures often are to mixtures of cyanides with other environmental pollutants. Interactive toxicology is the study of the toxicity of a chemical when it occurs with other chemicals or stressors. Such interactions can modify the joint toxicity of a given mixture. Several binary mixtures of cyanides have been studied in humans and animals to develop antidotes, and their mechanism of action is well understood. We used this limited binary weight of evidence to evaluate the toxicity of untested mixtures, extended it, and applied it to complex environmental mixtures to advance methods for joint toxicity assessment. Federal agencies and local entities provide guidance to evaluate such exposures in the absence of specific data. The objective of this paper is to illustrate use and applicability of ATSDR's framework for evaluation of environmental mixtures, specifically the use of weight of evidence in Tier III, using cyanide mixtures as examples. The results show, for certain cyanide mixtures for which data are available, interactions can be evaluated with a high degree of confidence. For complex mixtures that contain unidentified components, such as found in fires, similarity-based grouping risk assessment is proposed.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner. 2022 Elsevier B.V.

Federal statutes authorize several agencies to protect human populations from chemical emergencies and provide guidance to evacuate, clean, and re-occupy affected areas. Each of the authorized federal agencies have developed programs to provide managers, public health officials, and regulators, with a rapid assessment of potential hazards and risks associated with chemical emergencies. Emergency responses vary based on exposure scenarios, routes, temporal considerations, and the substance(s) present. Traditional chemical assessments and derivation of toxicity values are time-intensive, typically requiring large amounts of human epidemiological and experimental animal data. When a rapid assessment of health effects is needed, an integrated computational approach of augmenting extant toxicity data with in vitro (new alternative toxicity testing methods) data can provide a quick, evidence-based solution. In so doing, multiple streams of data can be used, including literature searches, hazard, dose-response, physicochemical, and environmental fate and transport property data, in vitro cell bioactivity testing and toxicogenomics. The field of toxicology is moving, ever so slowly, towards increased use of this approach as it transforms from observational to predictive science. The challenge is to objectively and transparently derive toxicity values using this approach to protect human health and the environment. Presented here are examples and efforts toward rapid risk assessment that demonstrate unified, parallel, and complementary work to provide timely protection in times of chemical emergency.

The 2018 ATSDR mixture framework recommends three approaches including the hazard index (HI) for environmental mixture toxicity assessment. Per- and polyfluoroalkyls (PFAS) are found in our environment and general populations. Recent experimental mixture toxicity studies of perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) and an assessment of 17 PFAS indicate the use of additivity for their joint toxicity assessment. The aim of this investigation was to detail the stepwise procedures and examine the extent and use of the HI approach for PFAS mixture assessment. Using estimated general public lifetime exposures (high, medium, and low), binary mixtures of PFOS and PFOA yielded, respectively, hazard indices (HIs) of 30.67, 8.33, and 3.63 for developmental toxicity; 10.67, 5.04, and 2.34 for immunological toxicity; 3.57, 1.68, and 0.78 for endocrine toxicity; 4.51, 1.73, and 0.79 for hepatic toxicity; and 15.08, 2.29, and 0.88 for reproductive toxicity. A heterogeneous mixture of PFOA, PFAS, dioxin (CDD), and polybrominated compounds (PBDE) for high exposure scenario yielded HIs of 30.99 for developmental, 10.77 for immunological, 3.64 for endocrine, 4.61 for hepatic, and 17.36 for reproductive effects. The HI values are used as a screening tool; the potential concern for exposures rises as HI values increase. For HI values >1, a follow-up including further analysis of specific exposures, use of internal dosimetry, and uncertainty factors is conducted before recommending appropriate actions. The HI approach appears suitable to address present-day PFAS public health concerns for initial assessment of multiple health effects, until further insights are gained into their mechanistic toxicology.The findings and conclusions in this article are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry.

Mixtures risk assessment needs an efficient integration of in vivo, in vitro and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry PBPK toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, xylenes (BTEX). The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied TEX-gene- disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short-term and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short-term and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.

The Agency for Toxic Substances and Disease Registry (ATSDR) lists cadmium as one of its priority hazardous substances. The agency conducted a comprehensive literature review of cadmium and used the information to develop a toxicological profile that identified the full range of health effects associated with exposure to cadmium. It included an assessment that identified screening levels, termed health guidance values or minimal risk levels (MRLs), below which adverse health effects are not expected. In this paper, we describe how MRLs for cadmium are derived. For the acute inhalation MRL, the traditional no observed adverse effect level or lowest observed adverse effect level (NOAEL/LOAEL) approach is used; for the oral intermediate MRL, the benchmark dose (BMD) approach is used. MRLs were developed for the most sensitive route-specific end points, other than mortality and cancer that were sufficiently supported and justified by the data. These included an acute duration (1-14 day exposure) inhalation MRL of 0.03 µg Cd/m(3) for alveolar histiocytic infiltration and focal inflammation in alveolar septa and an intermediate duration (15-365 day exposure) oral MRL of 0.5 µg Cd/kg/day for decreased bone mineral density.

Lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are among the top 10 pollutants of global health concern. Studies have shown that exposures to these metals produce severe adverse effects. However, the mechanisms underlying these effects, particularly joint toxicities, are poorly understood in humans. The objective of this investigation was to identify and characterize prevalent combinations of these metals and their species in the U.S. NHANES population to provide background data for future studies of potential metal interactions. Exposure was defined as urine or blood levels ≥ medians of the NHANES 2007-2012 participants ≥6 years (n = 7408). Adjusted-odds ratios (adj-OR) and 95% confidence intervals were determined for covariates (age, gender, and race/ethnicity, cotinine and body mass index). Species-specific analysis was also conducted for As and Hg including iAs (urinary arsenous acid and/or arsenic acid), met-iAs (urinary monomethylarsonic acid and/or dimethylarsinic acid), and oHg (blood methyl-mercury and/or ethyl-mercury). For combinations of As and Hg species, age- and gender-specific prevalence was determined among NHANES 2011-2012 participants (n = 2342). Data showed that approximately 49.3% of the population contained a combination of three or more metals. The most prevalent unique specific combinations were Pb/Cd/Hg/As, Pb/Cd/Hg, and Pb/Cd. Age was consistently associated with these combinations: adj-ORs ranged from 10.9 (Pb/Cd) to 11.2 (Pb/Cd/Hg/As). Race/ethnicity was significant for Pb/Cd/Hg/As. Among women of reproductive age, frequency of oHg/iAs/met-iAS and oHg/met-iAs was 22.9 and 40.3%, respectively. These findings may help prioritize efforts to assess joint toxicities and their impact on public health.

Chlorinated dibenzo-p-dioxins (CDDs) are a series of mono- to octa-chlorinated homologous chemicals commonly referred to as polychlorinated dioxins. One of the most potent, well-known, and persistent member of this family is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As part of translational research to make computerized models accessible to health risk assessors, we present a Berkeley Madonna recoded version of the human physiologically based pharmacokinetic (PBPK) model used by the U.S. Environmental Protection Agency (EPA) in the recent dioxin assessment. This model incorporates CYP1A2 induction, which is an important metabolic vector that drives dioxin distribution in the human body, and it uses a variable elimination half-life that is body burden dependent. To evaluate the model accuracy, the recoded model predictions were compared with those of the original published model. The simulations performed with the recoded model matched well with those of the original model. The recoded model was then applied to available data sets of real life exposure studies. The recoded model can describe acute and chronic exposures and can be useful for interpreting human biomonitoring data as part of an overall dioxin and/or dioxin-like compounds risk assessment.

BACKGROUND: A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published. OBJECTIVES: To assess the underlying mechanisms of POPs that have been associated with metabolic diseases, 3 well-known POPs (2,3,7,8-Tetrachlorodibenzodioxin (TCDD), 2,2',4,4',5,5'-Hexachlorobiphenyl (PCB 153), and p,p'-Dichlorodiphenyldichloroethylene (p,p'-DDE)) were studied. We used advanced database search tools to delineate testable hypotheses and guide laboratory-based research studies into underlying mechanisms by which this POPs mixture could produce or exacerbate metabolic diseases. METHODS: For these studies searches, a proprietary systems biology software MetaCoreTM/MetaDrugTM was used to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within 2 or less interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimental well-documented causal associations with type 2 diabetes. CONCLUSIONS: Our systems biology approach allowed identification of converging pathways leading to activation of common targets downstream. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of 3 common POPs, using a systems biology approach, which may link POPs exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of POPs' effects, which could translate into disease prevention and more effective clinical treatment strategies.

Public health protection from air pollution can be achieved more effectively by shifting from a single-pollutant approach to a multi-pollutant approach. To develop such multi-pollutant approaches, identifying which air pollutants are present most frequently is essential. This study aims to determine the frequently found carcinogenic air toxics or hazardous air pollutants (HAPs) combinations across the United States as well as to analyze the health impacts of developing cancer due to exposure to these HAPs. To identify the most commonly found carcinogenic air toxics combinations, we first identified HAPs with cancer risk greater than one in a million in more than 5% of the census tracts across the United States, based on the National-Scale Air Toxics Assessment (NATA) by the U.S. EPA for year 2005. We then calculated the frequencies of their two-component (binary), and three-component (ternary) combinations. To quantify the cancer-related health impacts, we focused on the 10 most frequently found HAPs with national average cancer risk greater than one in a million. Their cancer-related health impacts were calculated by converting lifetime cancer risk reported in NATA 2005 to years of healthy life lost or Disability-Adjusted Life Years (DALYs). We found that the most frequently found air toxics with cancer risk greater than one in a million are formaldehyde, carbon tetrachloride, acetaldehyde, and benzene. The most frequently occurring binary pairs and ternary mixtures are the various combinations of these four air toxics. Analysis of urban and rural HAPs did not reveal significant differences in the top combinations of these chemicals. The cumulative annual cancer-related health impacts of inhaling the top 10 carcinogenic air toxics included was about 1,600 DALYs in the United States or 0.6 DALYs per 100,000 people. Formaldehyde and benzene together contribute nearly 60 percent of the total cancer-related health impacts. Our study shows that although there are many carcinogenic air toxics, only a few of them affect public health significantly at the national level in the United States, based on the frequency of occurrence of air toxics mixtures and cancer-related public health impacts. Future research is needed on their joint toxicity and cumulative health impacts.

Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERalpha) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERalpha. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals.

Occupational exposure limits have traditionally focused on preventing morbidity and mortality arising from inhalation exposures to individual chemical stressors in the workplace. While central to occupational risk assessment, occupational exposure limits have limited application as a refined disease prevention tool because they do not account for all of the complexities of the work and non-occupational environments and are based on varying health endpoints. To be of greater utility, occupational exposure limits and other risk management tools could integrate broader consideration of risks from multiple exposure pathways and routes (aggregate risk) as well as the combined risk from exposure to both chemical and non-chemical stressors, within and beyond the workplace, including the possibility that such exposures may cause interactions or modify the toxic effects observed (cumulative risk). Although still at a rudimentary stage in many cases, a variety of methods and tools have been developed or are being used in allied risk assessment fields to incorporate such considerations in the risk assessment process. These approaches, which are collectively referred to as cumulative risk assessment, have potential to be adapted or modified for occupational scenarios and provide a tangible path forward for occupational risk assessment. Accounting for complex exposures in the workplace and the broader risks faced by the individual also requires a more complete consideration of the composite effects of occupational and non-occupational risk factors to fully assess and manage worker health problems. Barriers to integrating these different factors remain, but new and ongoing community-based and worker health-related initiatives may provide mechanisms for identifying and integrating risk from aggregate exposures and cumulative risks from all relevant sources, be they occupational or non-occupational.

Polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, and mono- and non-ortho polychlorinated biphenyls (dioxin-like PCBs) are identified as a family or group of organic compounds known as 'dioxins' or dioxin-like chemicals (DLCs). The most toxic member of this group is 2,3,7,8-tetrachlorodibenzo-(p)-dioxin (TCDD). Historically, DLCs have caused a variety of negative human health effects, but a disfiguring skin condition known as chloracne is the only health effect reported consistently. As part of translational research to make computerized models accessible to health risk assessors, the Concentration- and Age-Dependent Model (CADM) for TCDD was recoded in the Berkeley Madonna simulation language. The US Agency for Toxic Substances and Disease Registry's computational toxicology laboratory used the recoded model to predict TCDD tissue concentrations at different exposure levels. The model simulations successfully reproduced the National Health and Nutrition Examination Survey (NHANES) 2001-2002 TCDD data in age groups from 6 to 60 years and older, as well as in other human datasets. The model also enabled the estimation of lipid-normalized serum TCDD concentrations in breastfed infants. The model performed best for low background exposures over time compared with a high acute poisoning case that could due to the large dose and associated liver toxicity. Hence, this model may be useful for interpreting human biomonitoring data as a part of an overall DLC risk assessment.

Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures.

Most of the experimental toxicity testing data for chemicals are generated through the use of laboratory animals, namely, rodents such as rats and mice or other species. Interspecies extrapolation is needed to nullify the differences between species so as to use such data for human health/risk assessment. Thus, understanding of interspecies differences is important in extrapolating the laboratory results to humans and conducting human risk assessments based on current credible scientific knowledge. Major causes of interspecies differences in anatomy and physiology, toxicokinetics, injury repair, molecular receptors, and signal transduction pathways responsible for variations in responses to toxic chemicals are outlined. In the risk assessment process, uncertainty associated with data gaps in our knowledge is reflected by application of uncertainty factors for interspecies differences. Refinement of the risk assessment methods is the ultimate goal as we strive to realistically evaluate the impact of toxic chemicals on human populations. Using specific examples from current risk assessment practice, this chapter illustrates the integration of interspecies differences in evaluation of individual chemicals and chemical mixtures.

Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50) for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

Exposure to chemicals occurs often as mixtures. Presented in this paper is information on alkoxyethanols and the impact they might have on human health in combination with some commonly found aliphatic and aromatic compounds. Our studies to evaluate the joint toxicity of these chemicals among themselves and in combination with other chemicals reveal a variety of possible outcomes depending on the exposure scenario. The interactions are predominantly based on metabolic pathways and are common among several solvents and organic compounds. Quantitative structure activity relationship (QSAR) analysis can be used with high confidence to identify chemicals that will interact to influence overall joint toxicity. Potential human exposure to a combination of alkoxyethanol, toluene and substituted benzenes may increase reproductive and developmental disease conditions. Inheritable gene alterations result in changes in the enzyme function in different subpopulations causing variations in quantity and/or quality of particular isoenzymes. These changes are responsible for differential metabolism of chemicals in species, genders, and life stages and are often the basis of a population's susceptibility. Unique genotypes introduced as a function of migration can alter the genetic makeup of any given population. Hence special consideration should be given to susceptible populations while conducting chemical health risk assessments.

Post-exposure risk assessment of chemical and environmental stressors is a public health challenge. Linking exposure to health outcomes is a 4-step process: exposure assessment, hazard identification, dose response assessment, and risk characterization. This process is increasingly adopting "in silico" tools such as physiologically based pharmacokinetic (PBPK) models to fine-tune exposure assessments and determine internal doses in target organs/tissues. Many excellent PBPK models have been developed. But most, because of their scientific sophistication, have found limited field application-health assessors rarely use them. Over the years, government agencies, stakeholders/partners, and the scientific community have attempted to use these models or their underlying principles in combination with other practical procedures. During the past two decades, through cooperative agreements and contracts at several research and higher education institutions, ATSDR funded translational research has encouraged the use of various types of models. Such collaborative efforts have led to the development and use of transparent and user-friendly models. The "human PBPK model toolkit" is one such project. While not necessarily state of the art, this toolkit is sufficiently accurate for screening purposes. Highlighted in this paper are some selected examples of environmental and occupational exposure assessments of chemicals and their mixtures.

Toxicity and exposure evaluations remain the two of the key components of human health assessment. While improvement in exposure assessment relies on a better understanding of human behavior patterns, toxicity assessment still relies to a great extent on animal toxicity testing and human epidemiological studies. Recent advances in computer modeling of the dose-response relationship and distribution of xenobiotics in humans to important target tissues have advanced our abilities to assess toxicity. In particular, physiologically based pharmacokinetic (PBPK) models are among the tools than can enhance toxicity assessment accuracy. Many PBPK models are available to the health assessor, but most are so difficult to use that health assessors rarely use them. To encourage their use these models need to have transparent and user-friendly formats. To this end the Agency for Toxic Substances and Disease Registry (ATSDR) is using translational research to increase PBPK model accessibility, understandability, and use in the site-specific health assessment arena. The agency has initiated development of a human PBPK tool-kit for certain high priority pollutants. The tool kit comprises a series of suitable models. The models are recoded in a single computer simulation language and evaluated for use by health assessors. While not necessarily being state-of-the-art code for each chemical, the models will be sufficiently accurate to use for screening purposes. This article presents a generic, seven-compartment PBPK model for six priority volatile organic compounds (VOCs): benzene (BEN), carbon tetrachloride (CCl(4)), dichloromethane (DCM), perchloroethylene (PCE), trichloroethylene (TCE), and vinyl chloride (VC). Limited comparisons of the generic and original model predictions to published kinetic data were conducted. A goodness of fit was determined by calculating the means of the sum of the squared differences (MSSDs) for simulation vs. experimental kinetic data using the generic and original models. Using simplified solvent exposure assumptions for oral ingestion and inhalation, steady-state blood concentrations of each solvent were simulated for exposures equivalent to the ATSDR Minimal Risk Levels (MRLs). The predicted blood levels were then compared to those reported in the National Health and Nutrition ExaminationSurvey (NHANES). With the notable exception of BEN, simulations of combined oral and inhalation MRLs using our generic VOC model yielded blood concentrations well above those reported for the 95th percentile blood concentrations for the U.S. populations, suggesting no health concerns. When the PBPK tool kit is fully developed, risk assessors will have a readily accessible tool for evaluating human exposure to a variety of environmental pollutants.

There is increasing interest in the use of tiered approaches in risk assessment of mixtures or co-exposures to chemicals for prioritization. One possible screening-level risk assessment approach is the threshold of toxicological concern (TTC). To date, default assumptions of dose or response additivity have been used to characterize the toxicity of chemical mixtures. Before a screening-level approach could be used, it is essential to know whether synergistic interactions can occur at low, environmentally relevant exposure levels. Studies demonstrating synergism in mammalian test systems were identified from the literature, with emphasis on studies performed at doses close to the points of departure (PODs) for individual chemicals. This search identified 90 studies on mixtures. Few included quantitative estimates of low-dose synergy; calculations of the magnitude of interaction were included in only 11 papers. Quantitative methodology varied across studies in terms of the null hypothesis, response measured, POD used to test for synergy, and consideration of the slope of the dose-response curve. It was concluded that consistent approaches should be applied for quantification of synergy, including that synergy be defined in terms of departure from dose additivity; uniform procedures be developed for assessing synergy at low exposures; and the method for determining the POD for calculating synergy be standardized. After evaluation of the six studies that provided useful quantitative estimates of synergy, the magnitude of synergy at low doses did not exceed the levels predicted by additive models by more than a factor of 4.

For communities generally and for persons living in the vicinity of waste sites specifically, potential exposures to chemical mixtures are genuine concerns. Such concerns often arise from perceptions of a site's higher than anticipated toxicity due to synergistic interactions among chemicals. This chapter outlines some historical approaches to mixtures risk assessment. It also outlines ATSDR's current approach to toxicity risk assessment. The ATSDR's joint toxicity assessment guidance for chemical mixtures addresses interactions among components of chemical mixtures. The guidance recommends a series of steps that include simple calculations for a systematic analysis of data leading to conclusions regarding any hazards chemical mixtures might pose. These conclusions can, in turn, lead to recommendations such as targeted research to fill data gaps, development of new methods using current science, and health education to raise awareness of residents and health care providers. The chapter also provides examples of future trends in chemical mixtures assessment.

The Agency for Toxic Substances and Disease Registry (ATSDR) is mandated by the US Congress to identify significant human exposure levels, develop methods to determine such exposures, and design strategies to mitigate them. Physiologically based pharmacokinetic (PBPK) models are increasingly being used to evaluate toxicity of environmental pollutants through multiple exposure pathways. As part of its translational research project, ATSDR is developing a human 'PBPK model tool kit' that consists of a series of published models re-coded in a common simulation language. The tool kit currently consists of models, at various stages of development, for priority environmental contaminants including solvents and persistent organic pollutants. Presented here are results of translational activities of re-coding models for cadmium, mercury, and arsenic. As part of this work, following re-coding each new model was evaluated for fidelity followed by sensitivity analysis. Good agreement was generally obtained for all three models when predictions of original and re-coded model simulations were compared. Also presented is an application of the cadmium toxicokinetic model to interpret biomonitoring data from the National Health and Nutrition Examination Survey (NHANES). The PBPK tool kit will enable ATSDR scientists to perform simulations of exposures from contaminated environmental media at sites of concern and to better interpret site-specific biomonitoring data.