The codification of hazard and its impact on the hazard versus risk controversy.

The long running controversy about the relative merits of hazard-based versus risk-based approaches has been investigated. There are three levels of hazard codification: level 1 divides chemicals into dichotomous bands of hazardous and non-hazardous; level 2 divides chemicals into bands of hazard based on severity and/or potency; and level 3 places each chemical on a continuum of hazard based on severity and/or potency. Any system which imposes compartments onto a continuum will give rise to issues at the boundaries, especially with only two compartments. Level 1 schemes are only justifiable if there is no variation in severity, or potency or if there is no threshold. This is the assumption implicit in GHS/EU classification for carcinogenicity, reproductive toxicity and mutagenicity. However, this assumption has been challenged. Codification level 2 hazard assessments offer a range of choices and reduce the built-in conflict inherent in the level 1 process. Level 3 assessments allow a full range of choices between the extremes and reduce the built-in conflict even more. The underlying reason for the controversy between hazard and risk is the use of level 1 hazard codification schemes in situations where there are ranges of severity and potency which require the use of level 2 or level 3 hazard codification. There is not a major difference between level 2 and level 3 codification, and they can both be used to select appropriate risk management options. Existing level 1 codification schemes should be reviewed and developed into level 2 schemes where appropriate.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review.

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

Multi-label classification and label dependence in in silico toxicity prediction.

Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance of 3 multi-label classification (MLC) models, namely Classifier Chains (CC), Label Powersets (LP) and Stacking (SBR), against independent classifiers (Binary Relevance) on Tox21 challenge data. Also, we develop a novel label dependence measure that shows full range of values, even at low prior probabilities, for the purpose of data-driven label partitioning. Using Logistic Regression as the base classifier and random label partitioning (k = 3), CC show statistically significant improvements in model performance using Hamming and multi-label accuracy scores (p<0.05), while SBR show significant improvements in multi-label accuracy scores. The weights in the Logistic Regression and Stacking models are positively associated with label dependencies, suggesting that learning label dependence is a key contributor to improving model performance. An original quantitative measure of label dependency is combined with the Louvain community detection method to learn label partitioning using a data-driven process. The resulting MLCs with learned label partitioning were generally found to be non-inferior to their corresponding random or no label partitioning counterparts. Additionally, using the Random Forest classifier in a 10-fold stratified cross validation Stacking model, we find that the top-performing stacking model out-performs the corresponding base model in 11 out of 12 Tox21 labels. Taken together, these results suggest that MLC models could potentially boost the performance of current single-endpoint predictive models and that label partitioning learning may be used in place of random label partitionings.

Proposal for a neurotoxic classification for chemicals at work.

Many neurotoxic chemicals are used in the workplace but there is currently no database dedicated to neurotoxicity. We aimed to develop a classification method for neurotoxicity based on a weight-of-evidence approach, similar to the IARC classification for carcinogenicity. Human and animal lines of evidence were collected from recent toxicological profiles and a literature search and were combined into six groups from neurotoxic to potentially not neurotoxic. The method was tested on 26 chemicals, mixtures or group of products used in the workplace in France: 31% were considered neurotoxic, 31% probably and 11% possibly neurotoxic, and 27% not classifiable because of insufficient data. This operational method suggests that many chemicals used in the workplace are neurotoxic and that questionnaires used to collect data on occupational chemical exposure should propose items with more targeted compounds that have common chemical or toxic properties to improve risk assessment.

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning.

As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose individuals with ASD merely based on clinical observations. Without additional support of biochemical markers, the difficulty of diagnosis could impact therapeutic decisions and, therefore, lead to delayed treatments. Recently, accumulating evidence have shown that both genetic abnormalities and chemical toxicants play important roles in the onset of ASD. In this work, a new multilabel classification (MLC) model is proposed to identify the autistic risk genes and toxic chemicals on a large-scale data set. We first construct the feature matrices and partially labeled networks for autistic risk genes and toxic chemicals from multiple heterogeneous biological databases. Based on both global and local measure metrics, the simulation experiments demonstrate that the proposed model achieves superior classification performance in comparison with the other state-of-the-art MLC methods. Through manual validation with existing studies, 60% and 50% out of the top-20 predicted risk genes are confirmed to have associations with ASD and autistic disorder, respectively. To the best of our knowledge, this is the first computational tool to identify ASD-related risk genes and toxic chemicals, which could lead to better therapeutic decisions of ASD.

A retrospective study on EU harmonised classifications for carcinogenicity to guide future research.

Under European Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP), chemicals can be classified as carcinogenic if they are considered to induce tumours, increase tumour incidence and/or malignancy, or shorten the time to tumour occurrence. Cancer classifications are divided into different hazard categories: Carc. 1A (known human carcinogen), Carc. 1B (presumed human carcinogen), Carc. 2 (suspected human carcinogen), and chemicals not classified for carcinogenicity. Selecting which classification is appropriate can be challenging, as judgements need to be made both on the existing hazard data and on its relevance to humans. One aspect to be considered in defining human relevance is a chemical's mode of action (MoA); the series of necessary key events that lead from an exposure to the adverse effect (in this case, tumours). This work aims to identify and discuss some of the features that have led ECHA's Committee for Risk Assessment (RAC) to decide upon harmonised cancer classifications for chemicals, and to prioritise future research on MoA and/or human relevance. RAC bases its decisions on cancer classification on both the weight-of-evidence (WoE) and strength-of-evidence (SoE) of this particular activity. Multiple factors contribute, including the species in which tumours are seen, and the relevance of the MoA to human health.

Optimising testing strategies for classification of human health and environmental hazards - A proof-of-concept study.

This paper outlines a new concept to optimise testing strategies for improving the efficiency of chemical testing for hazard-based risk management. While chemical classification based on standard checklists of information triggers risk management measures, the link is not one-to-one. Toxicity testing may be performed with no impact on the safe use of chemicals . Each hazard class and category is not assigned a unique pictogram and for the purpose of this proof-of-concept study, the level of concern for a chemical for the population and the environment is simplistically considered to be reflected by the hazard pictograms. Using active substances in biocides and plant protection products as a dataset, three testing strategies were built with the boundary condition that an optimal approach must indicate a given level of concern while requiring less testing (strategy B), prioritising new approach methodologies (strategy C) or combining the two considerations (strategy D). The implementation of the strategies B and D reduced the number of tests performed by 6.0% and 8.8%, respectively, while strategy C relied the least on in vivo methods. The intentionally simplistic approach to optimised testing strategies presented here could be used beyond the assessment of biocides and plant protection products to gain efficiencies in the safety assessment of other chemical groups, saving animals and making regulatory testing more time- and cost-efficient.

It's Time to Replace the Term "Heavy Metals" with "Potentially Toxic Elements" When Reporting Environmental Research.

Even if the Periodic Table of Chemical Elements is relatively well defined, some controversial terms are still in use. Indeed, the term "heavy metal" is a common term used for decades in the natural sciences, and even more in environmental sciences, particularly in studies of pollution impacts. As the use of the term appears to have increased, we highlight the relevance of the use of the term "Potentially Toxic Element(s)", which needs more explicit endorsement, and we illustrate the chemical elements that need to be considered.

ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses.

The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.

Toxicity classification of e-cigarette flavouring compounds based on European Union regulation: analysis of findings from a recent study.

INTRODUCTION: A recent study raised concerns about e-cigarette liquids toxicity by reporting the presence of 14 flavouring chemicals with toxicity classification. However, the relevant toxicity classification was not estimated according to the measured concentrations. The purpose of this study was to calculate the toxicity classification for different health hazards for all the flavouring chemicals at the maximum concentrations reported. METHODS: The analysis was based on the European Union Classification Labelling and Packaging regulation. The concentration of each flavouring chemical was compared with the minimum concentration needed to classify it as toxic. Additionally, toxicity classification was examined for a theoretical e-cigarette liquid containing all flavouring chemicals at the maximum concentrations reported. RESULTS: There was at least one toxicity classification for all the flavouring chemicals, with the most prevalent classifications related to skin, oral, eye and respiratory toxicities. One chemical (methyl cyclopentenolone) was found at a maximum concentration 150.7% higher than that needed to be classified as toxic. For the rest, the maximum reported concentrations were 71.6 to > 99.9% lower than toxicity concentrations. A liquid containing all flavouring compounds at the maximum concentrations would be classified as toxic for one category only due to the presence of methyl cyclopentenolone; a liquid without methyl cyclopentenolone would have 66.7 to > 99.9% lower concentrations of flavourings than those needed to be classified as toxic. CONCLUSIONS: The vast majority of flavouring compounds in e-cigarette liquids as reported in a recent study were present at levels far lower than needed to classify them as toxic. Since exceptions exist, regulatory monitoring of liquid composition is warranted.

Testing the thresholds of toxicological concern values using a new database for food-related substances.

The Threshold of Toxicological Concern (TTC) concept integrates data on exposure, chemical structure, toxicity and metabolism to identify a safe exposure threshold value for chemicals with insufficient toxicity data for risk assessment. The TTC values were originally derived from a non-cancer dataset of 613 compounds with a potentially small domain of applicability. There is interest to test whether the TTC values are applicable to a broader range of substances, particularly relevant to food safety using EFSA's new OpenFoodTox database. After exclusion of genotoxic compounds, organophosphates or carbamates or those belonging to the TTC exclusion categories, the remaining 329 substances in the EFSA OpenFoodTox database were categorized under the Cramer decision tree, into low (Class I), moderate (II), or high (III) toxicity profile. For Cramer Classes I and III the threshold values were 1000 µg/person per day (90% confidence interval: 187-2190) and 87 µg/person per day (90% confidence interval: 60-153), respectively, compared to the corresponding original threshold values of 1800 and 90 µg/person per day. This confirms the applicability of the TTC values to substances relevant to food safety. Cramer Class II was excluded from our analysis because of containing too few compounds. Comparison with the Globally Harmonized System of classification confirmed that the Cramer classification scheme in the TTC approach is conservative for substances relevant to food safety.

Weight-of-Evidence Approach for Assessing Removal of Metals from the Water Column for Chronic Environmental Hazard Classification.

The United Nations and the European Union have developed guidelines for the assessment of long-term (chronic) chemical environmental hazards. This approach recognizes that these hazards are often related to spillage of chemicals into freshwater environments. The goal of the present study was to examine the concept of metal ion removal from the water column in the context of hazard assessment and classification. We propose a weight-of-evidence approach that assesses several aspects of metals including the intrinsic properties of metals, the rate at which metals bind to particles in the water column and settle, the transformation of metals to nonavailable and nontoxic forms, and the potential for remobilization of metals from sediment. We developed a test method to quantify metal removal in aqueous systems: the extended transformation/dissolution protocol (T/DP-E). The method is based on that of the Organisation for Economic Co-operation and Development (OECD). The key element of the protocol extension is the addition of substrate particles (as found in nature), allowing the removal processes to occur. The present study focused on extending this test to support the assessment of metal removal from aqueous systems, equivalent to the concept of "degradability" for organic chemicals. Although the technical aspects of our proposed method are different from the OECD method for organics, its use for hazard classification is equivalent. Models were developed providing mechanistic insight into processes occurring during the T/DP-E method. Some metals, such as copper, rapidly decreased (within 96 h) under the 70% threshold criterion, whereas others, such as strontium, did not. A variety of method variables were evaluated and optimized to allow for a reproducible, realistic hazard classification method that mimics reasonable worst-case scenarios. We propose that this method be standardized for OECD hazard classification via round robin (ring) testing to ascertain its intra- and interlaboratory variability. Environ Toxicol Chem 2019;38:1839-1849. © 2019 SETAC.

Method Development for Determining the Removal of Metals from the Water Column under Transformation/Dissolution Conditions for Chronic Hazard Classification.

An extension of the transformation/dissolution protocol (T/DP) was developed and evaluated as a tool to measure the removal of metals from the water column for chronic aquatic hazard classification. The T/DP extension (T/DP-E) consists of 2 parts: T/DP-E part 1, to measure metal removal from the water column via binding of metals to a substrate and subsequent settling, and T/DP-E part 2, to assess the potential for remobilization of metals following resuspension. The T/DP-E methodology (672-h [28-d] removal period, 1-h resuspension event, and 96-h resettling period) was tested using Cu, Co, and Sr solutions in the presence of a substrate. The metal removal rates varied from rapid removal for Cu to slower rates of removal for Co and Sr. The resuspension event did not trigger any increase in dissolved Cu, Co, or Sr. Additional 96-h experiments were conducted using dissolved Ni, Pb, Zn, and Ag and supported the conclusion that the T/DP-E is sufficiently robust to distinguish removal rates between metals with a wide range of reactivities. The proposed method provides a means to quantify the rate of metal removal from the water column and evaluate remobilization potential in a standardized and reliable way. Environ Toxicol Chem 2019;38:2032-2042. © 2019 SETAC.

Exposure to environmental toxicants and young children's cognitive and social development.

Background Understanding the role of environmental toxicant exposure on children's development is an important area of inquiry in order to better understand contextual factors that shape development and ultimately school readiness among young children. There is evidence suggesting negative links between exposure to environmental toxicants and negative physical health outcomes (i.e. asthma, allergies) in children. However, research on children's exposure to environmental toxicants and other developmental outcomes (cognitive, socioemotional) is limited. Objectives The goal of the current review was to assess the existing literature on the links between environmental toxicants (excluding heavy metals) and children's cognitive, socioemotional, and behavioral development among young children. Methods This literature review highlights research on environmental toxicants (i.e. pesticide exposure, bisphenol A, polycyclic aromatic hydrocarbons, tobacco smoke, polychlorinated biphenyls, flame retardants, phthalates and gas pollutions) and children's development across multiple domains. Results The results highlight the potential risk of exposure to multiple environmental toxicants for young children's cognitive and socioemotional development. Discussion Discussion will focus on the role of environmental toxicants in the cognitive and socioemotional development of young children, while highlighting gaps in the existing literature.

The hazards and risks of inhaled poorly soluble particles - where do we stand after 30 years of research?

BACKGROUND: In 2006, titanium dioxide and carbon black were classified by IARC as "possibly carcinogenic to humans" and in 2017 the European Chemicals Agency's (ECHA) Committee for Risk Assessment concluded titanium dioxide meets the criteria to be classified as suspected of causing cancer (category 2, through the inhalation route). These classifications were based primarily on the occurrence of lung cancer in rats exposed chronically to high concentrations of these materials, as no such responses have been observed in other animal species similarly exposed. After the EU classification of titanium dioxide, it was suggested that Poorly Soluble particles of Low Toxicity (PSLTs) can be evaluated as a group. MAIN BODY: To better understand the current state of scientific opinion, we sought perspective from several international experts on topics relevant to the classification of carbon black; titanium dioxide; and, the potential future classification of PSLTs. Areas discussed included: grouping of PSLTs; the relevance of rat lung cancer responses to high concentrations of PSLTs; and, clearance overload and implications for interpretation of inhalation toxicology studies. We found there were several areas where a large majority of experts, including ourselves, agreed. These included concerns on the grouping of PSLT and the definition of clearance overload. Regarding the extrapolation of PSLT associated lung cancer in rats there were some strongly held differences, although most experts questioned the relevance when excessive exposures which overwhelm lung clearance were required. SHORT CONCLUSION: Given the ongoing discussion on PSLT classification and safety, we believe it is important to re-activate the public debate including experts and stakeholders. Such an open discussion would serve to formally document where scientific consensus and differences exist. This could form the basis for design of future safety programs and safety assessments.

Target site model: Predicting mode of action and aquatic organism acute toxicity using Abraham parameters and feature-weighted k-nearest neighbors classification.

A database of 1480 chemicals with 47 associated modes of action compiled from the literature encompasses a wide range of chemical classes (alkanes, polycyclic aromatic hydrocarbons, pesticides, and polar compounds) and includes toxicity data for 79 different aquatic genera. The data were split into a calibration group and a validation group (80/20) to apply k-nearest neighbors (k-NN) methodology to predict the toxic mode of action for the compound. Other approaches were tested (support vector machines and linear discriminant analysis) as well as variations in the k-NN technique (distance weighting, feature weighting). Best-prediction results were found with k = 3, in a voting platform with optimized feature weighting. Using the predicted mode of action, the appropriate polyparameter target site model for that mode of action is applied to calculate the 50% lethal concentration (LC50). Predicted LC50s for the validation database resulted in a root-mean squared error (RMSE) of 0.752. This can be compared to an RMSE of 0.655 for the same validation set using the reference mode of action labels. The complete database resulted in an RMSE of 0.793 for reference mode of action labels. This confirms that the classification model has sufficient accuracy for predicting the mode of action and for determining toxicity using the target site model. Environ Toxicol Chem 2019;38:375-386. © 2018 SETAC.

Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach.

The implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: the median lethal dose (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-accuracy predictions based on the evaluations conducted by ICCVAM's ATWG.

Grouping of nanomaterials to read-across hazard endpoints: a review.

The use of non-testing strategies like read-across in the hazard assessment of chemicals and nanomaterials (NMs) is deemed essential to perform the safety assessment of all NMs in due time and at lower costs. The identification of physicochemical (PC) properties affecting the hazard potential of NMs is crucial, as it could enable to predict impacts from similar NMs and outcomes of similar assays, reducing the need for experimental (and in particular animal) testing. This manuscript presents a review of approaches and available case studies on the grouping of NMs to read-across hazard endpoints. We include in this review grouping frameworks aimed at identifying hazard classes depending on PC properties, hazard classification modules in control banding (CB) approaches, and computational methods that can be used for grouping for read-across. The existing frameworks and case studies are systematically reported. Relevant nanospecific PC properties taken into account in the reviewed frameworks to support grouping are shape and surface properties (surface chemistry or reactivity) and hazard classes are identified on the basis of biopersistence, morphology, reactivity, and solubility.

Development of a Screening Method for Health Hazard Ranking and Scoring of Chemicals Using the Mahalanobis⁻Taguchi System.

For efficient management of chemicals, it is necessary to preferentially select hazardous chemicals as being high-priority through a screening method. Over the past 20 years, chemical ranking and scoring (CRS) methods have been applied in many countries; however, these CRS methods have a few limitations. Most of the existing methods only use some of the variables to calculate the hazard of chemicals or use the most conservative score without consideration of the correlation between chemical toxicities. This evaluation could underestimate or overestimate the real health hazard of the chemicals. To overcome the limitations of these methods, we developed a new CRS method using the Mahalanobis⁻Taguchi System (MTS). The MTS, which conducts multivariate analysis, produced chemical rankings that took into accounts the correlation between variables related to chemical health hazards. Also, the proportion of chemicals managed by the Korea Chemicals Control Act that were given a high rating appeared to be higher when the MTS was used, compared to the existing methods. These results indicated that the new method evaluated the health hazards of chemicals more accurately, and we expect that the MTS method could be applied to a greater range of chemicals than the existing CRS methods.