Comparative assessment of different alternatives to animal models for developmental toxicity prediction using physiologically based toxicokinetic modelling approach: A case study of hexaconazole, an azole fungicide

The 21st-century risk assessment is gradually moving from animal-based toxicity studies to in vitro alternative assays that are sustainable and ethically acceptable. Alternative assays, such as rat whole embryo culture test (WEC), mouse embryonic stem cell test (EST), zebrafish embryotoxicity test (ZET), and ToxCast assays, are widely used for screening the chemicals for developmental toxicity. However, for use in risk assessment, these assays require integration with the predictive approaches, such as physiologically based toxicokinetic (PBTK) model. Using PBTK-facilitated reverse dosimetry approach, we translated the in vitro assay concentration to human equivalent doses (HEDs) using hexaconazole (HEX, a widely used fungicide) as the model compound. For this, a rat PBTK model was developed and verified using in-house generated toxicokinetic data. Based on the rat model, human PBTK model was developed to translate the in vitro concentrations of various alternative assays into HEDs (0.16-7850 mg/kg/day). These HEDs were compared with the HED derived using the traditional approach based on rat toxicity data. The HEDs derived from the alternative assays (WEC, EST and ZET) showed poor correlation with the HED derived from the traditional approach. However, most of the HEDs derived from the ToxCast assays were close to the traditional HED. This indicated that the PBTK model-facilitated reverse dosimetry approach could derive the HEDs directly from in vitro assays when sufficient animal data is lacking.

The potential toxic effects of estrogen exposure on neural and vascular development in zebrafish

Estrogens and estrogenic chemicals are endocrine-disrupting chemicals (EDCs). The potential toxicity of EDCs to humans and aquatic organisms has become increasingly concerning. However, at present, the potential toxic mechanisms of EDCs on neural and vascular development are still being fully investigated. During the study, we utilized zebrafish to assess the developmental neural and vascular toxicity of different estrogens. The results indicated that zebrafish treated with different estrogens, especially E2, exhibit developmental malformations, including increased mortality, decreased body length, decreased heart rate, aberrant swimming behavior, and increased developmental malformations, including spinal curvature (SC), yolk edema (YE) and pericaidial edema (PE), in a dose-dependent manner with 72 h-treated. Further morphological evaluation revealed that E2 exposure significantly induced motor neural abnormalities in zebrafish embryos. In addition, treated with these three estrogens also impaired the vascular development in the early stage of zebrafish embryos. Mechanistically, the identification of downstream factors revealed that several key neural and vascular development-related genes, including syn2a, gfap, gap43, shha, kdr, flt1 and flt4, were transcriptionally downregulated after estrogen exposure in zebrafish, suggesting that estrogen exposure might cause neural and vascular toxicity by interfering the mRNA levels of genes relevant to neural and vascular development.

Perspectives on chick embryo models in developmental and reproductive toxicity screening

Teratology, the study of congenital anomalies and their causative factors intersects with developmental and reproductive toxicology, employing innovative methodologies. Evaluating the potential impacts of teratogens on fetal development and assessing human risk is an essential prerequisite in preclinical research. The chicken embryo model has emerged as a powerful tool for understanding human embryonic development due to its remarkable resemblance to humans. This model offers a unique platform for investigating the effects of substances on developing embryos, employing techniques such as ex ovo and in ovo assays, chorioallantoic membrane assays, and embryonic culture techniques. The advantages of chicken embryonic models include their accessibility, cost-effectiveness, and biological relevance to vertebrate development, enabling efficient screening of developmental toxicity. However, these models have limitations, such as the absence of a placenta and maternal metabolism, impacting the study of nutrient exchange and hormone regulation. Despite these limitations, understanding and mitigating the challenges posed by the absence of a placenta and maternal metabolism are critical for maximizing the utility of the chick embryo model in developmental toxicity testing. Indeed, the insights gained from utilizing these assays and their constraints can significantly contribute to our understanding of the developmental impacts of various agents. This review underscores the utilization of chicken embryonic models in developmental toxicity testing, highlighting their advantages and disadvantages by addressing the challenges posed by their physiological differences from mammalian systems.

Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides

Open access new approach methods (NAM) in the US EPA ToxCast program and NTP Integrated Chemical Environment (ICE) were used to investigate activities of four neurotoxic pesticides: endosulfan, fipronil, propyzamide and carbaryl. Concordance of in vivo regulatory points of departure (POD) adjusted for interspecies extrapolation (AdjPOD) to modelled human Administered Equivalent Dose (AEDHuman) was assessed using 3-compartment or Adult/Fetal PBTK in vitro to in vivo extrapolation. Model inputs were from Tier 1 (High throughput transcriptomics: HTTr, high throughput phenotypic profiling: HTPP) and Tier 2 (single target: ToxCast) assays. HTTr identified gene expression signatures associated with potential neurotoxicity for endosulfan, propyzamide and carbaryl in non-neuronal MCF-7 and HepaRG cells. The HTPP assay in U-2 OS cells detected potent effects on DNA endpoints for endosulfan and carbaryl, and mitochondria with fipronil (propyzamide was inactive). The most potent ToxCast assays were concordant with specific components of each chemical mode of action (MOA). Predictive adult IVIVE models produced fold differences (FD) < 10 between the AEDHuman and the measured in vivo AdjPOD. The 3-compartment model was concordant (i.e., smallest FD) for endosulfan, fipronil and carbaryl, and PBTK was concordant for propyzamide. The most potent AEDHuman predictions for each chemical showed HTTr, HTPP and ToxCast were mainly concordant with in vivo AdjPODs but assays were less concordant with MOAs. This was likely due to the cell types used for testing and/or lack of metabolic capabilities and pathways available in vivo. The Fetal PBTK model had larger FDs than adult models and was less predictive overall.

The Future of the Teratogenicity Testing

The purpose of this review is to examine the importance, possible advantages and disadvantages of teratogenicity tests, and their future. For this purpose, numerous sources have been scanned in the field of teratogenicity. Although there are many methods related to teratogenic studies and very important studies have been made in this field, there are still serious deficiencies. There are advantages and disadvantages of in vitro and in vivo classical tests that have been used so far. The current status of in vivo tests is a matter of debate, especially due to the use of experimental animals. However, in vitro tests that do not perform the distribution and metabolism of chemicals also raise doubts in determination of teratogenicity. Despite the modern approaches of molecular biology and genetics and the best diagnostic techniques, the real cause of more than half of congenital diseases is still not understood. In this sense, the importance and necessity of teratogenic tests are understood once again. It is necessary to develop faster, reliable, and inexpensive techniques to replace traditional in vivo tests. It is important to disseminate harmless and reliable imaging techniques such as micro-CT. The use of European Center for the Validation of Alternative Methods (ECVAM) scientifically validated and approved in vitro tests such as embryonic stem cell test (EST), micro mass test (MM), and whole embryo culture (WEC) tests in routine screening can provide a solution in a shorter time than the classical tests. Improving these tests and developing new tests can help to solve the problem permanently.

Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing

Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

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

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

Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data

Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers.

Evaluation of in vitro toxicity information for zebrafish as a promising alternative for chemical hazard and risk assessment

In vitro assays are widely proposed as a test alternative to traditional in vivo standard acute and chronic toxicity tests. However, whether toxicity information derived from in vitro assays instead of in vivo tests could provide sufficient protection (e.g., 95 % of protection) for chemical risks remain evaluated. To investigate the feasibility of zebrafish (Danio rerio) cell-based in vitro test method as a test alternative, we comprehensively compared sensitivity differences among endpoints, among test methods (in vitro, FET and in vivo), and between zebrafish and rat (Rattus norvegicus), respectively using chemical toxicity distribution (CTD) approach. For each test method involved, sublethal endpoints were more sensitive than lethal endpoints for both zebrafish and rat, respectively. Biochemistry (zebrafish in vitro), development (zebrafish in vivo and FET), physiology (rat in vitro) and development (rat in vivo) were the most sensitive endpoints for each test method. Nonetheless, zebrafish FET test was the least sensitive one compared to its in vivo and in vitro tests for either lethal or sublethal responses. Comparatively, rat in vitro tests considering cell viability and physiology endpoints were more sensitive than rat in vivo test. Zebrafish was found to be more sensitive than rat regardless of in vivo or in vitro tests for each pairwise endpoint of concern. Those findings indicate that zebrafish in vitro test is a feasible test alternative to zebrafish in vivo and FET test and traditional mammalian test. It is suggesting that zebrafish in vitro test can be optimized by choosing more sensitive endpoints, such as biochemistry to provide sufficient protection for zebrafish in vivo test and to establish applications of zebrafish in vitro test in future risk assessment. Our findings are vital for evaluating and further application of in vitro toxicity toxicity information as an alternative for chemical hazard and risk assessment.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods

The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making.

Review of ecologically relevant in vitro bioassays to supplement current in vivo tests for whole effluent toxicity testing - Part 1: Apical endpoints

Whole effluent toxicity (WET) testing is commonly used to ensure that wastewater discharges do not pose an unacceptable risk to receiving environments. Traditional WET testing involves exposing animals to (waste)water samples to assess four major ecologically relevant apical endpoints: mortality, growth, development, and reproduction. Recently, with the widespread implementation of the 3Rs to replace, reduce and refine the use of animals in research and testing, there has been a global shift away from in vivo testing towards in vitro alternatives. However, prior to the inclusion of in vitro bioassays in regulatory frameworks, it is critical to establish their ecological relevance and technical suitability. This is part 1 of a two-part review that aims to identify in vitro bioassays that can be used in WET testing and relate them to ecologically relevant endpoints through toxicity pathways, providing the reader with a high-level overview of current capabilities. Part 1 of this review focuses on four apical endpoints currently included in WET testing: mortality, growth, development, and reproduction. For each endpoint, the link between responses at the molecular or cellular level, that can be measured in vitro, and the adverse outcome at the organism level were established through simplified toxicity pathways. Additionally, literature from 2015 to 2020 on the use of in vitro bioassays for water quality assessments was reviewed to identify a list of suitable bioassays for each endpoint. This review will enable the prioritization of relevant endpoints and bioassays for incorporation into WET testing.

High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges

The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.

Machine learning models for rat multigeneration reproductive toxicity prediction

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling

All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures.

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals

BACKGROUND

Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.

OBJECTIVES

We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.

METHODS

Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.

RESULTS

The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.

DISCUSSION

Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779.

Clearly weighing the evidence in read-across can improve assessments of data-poor chemicals

This paper provides a systematic weight-of-evidence method for read-across analyses of data-poor chemicals. The read-across technique extrapolates toxicity from analogous chemicals for which suitable test data are available to a target chemical. To determine that a candidate analogue is the 'best' and is sufficiently similar, the evidence for similarity of each candidate analogue to the target is weighed. We present a systematic weight of evidence method that provides transparency and imposes a consistent and rigorous inferential process. The method assembles relevant information concerning structure, physicochemical attributes, toxicokinetics, and toxicodynamics of the target and analogues. The information is then organized by evidence types and subtypes and weighted in terms of properties: relevance, strength, and reliability into weight levels, expressed as symbols. After evidence types are weighted, the bodies of evidence are weighted for collective properties: number, diversity, and coherence. Finally, the weights for the types and bodies of evidence are weighed for each analogue, and, if the overall weight of evidence is sufficient for one or more analogues, the analogue with the greatest weight is used to estimate the endpoint effect. We illustrate this WoE approach with a read-across analysis for screening the organochlorine contaminant, p,p'-dichlorodiphenyldichloroethane (DDD), for noncancer oral toxicity.

Emerging concepts and opportunities for endocrine disruptor screening of the non-EATS modalities

Endocrine disrupting chemicals (EDCs) are ubiquitous in the environment and involve diverse chemical-receptor interactions that can perturb hormone signaling. The Organization for Economic Co-operation and Development has validated several EDC-receptor bioassays to detect endocrine active chemicals and has established guidelines for regulatory testing of EDCs. Focus on testing over the past decade has been initially directed to EATS modalities (estrogen, androgen, thyroid, and steroidogenesis) and validated tests for chemicals that exert effects through non-EATS modalities are less established. Due to recognition that EDCs are vast in their mechanisms of action, novel bioassays are needed to capture the full scope of activity. Here, we highlight the need for validated assays that detect non-EATS modalities and discuss major international efforts underway to develop such tools for regulatory purposes, focusing on non-EATS modalities of high concern (i.e., retinoic acid, aryl hydrocarbon receptor, peroxisome proliferator-activated receptor, and glucocorticoid signaling). Two case studies are presented with strong evidence amongst animals and human studies for non-EATS disruption and associations with wildlife and human disease. This includes metabolic syndrome and insulin signaling (case study 1) and chemicals that impact the cardiovascular system (case study 2). This is relevant as obesity and cardiovascular disease represent two of the most significant health-related crises of our time. Lastly, emerging topics related to EDCs are discussed, including recognition of crosstalk between the EATS and non-EATS axis, complex mixtures containing a variety of EDCs, adverse outcome pathways for chemicals acting through non-EATS mechanisms, and novel models for testing chemicals. Recommendations and considerations for evaluating non-EATS modalities are proposed. Moving forward, improved understanding of the non-EATS modalities will lead to integrated testing strategies that can be used in regulatory bodies to protect environmental, animal, and human health from harmful environmental chemicals.

Specificity of time- and dose-dependent morphological endpoints in the fish embryo acute toxicity (FET) test for substances with diverse modes of action: the search for a "fingerprint"

The fish embryo acute toxicity (FET) test with the zebrafish (Danio rerio) embryo according to OECD TG 236 was originally developed as an alternative test method for acute fish toxicity testing according to, e.g., OECD TG 203. Given the versatility of the protocol, however, the FET test has found application beyond acute toxicity testing as a common tool in environmental hazard and risk assessment. Whereas the standard OECD guideline is restricted to four core endpoints (coagulation as well as lack of somite formation, heartbeat, and tail detachment) for simple, rapid assessment of acute toxicity, further endpoints can easily be integrated into the FET test protocol. This has led to the hypothesis that an extended FET test might allow for the identification of different classes of toxicants via a "fingerprint" of morphological observations. To test this hypothesis, the present study investigated a set of 18 compounds with highly diverse modes of action with respect to acute and sublethal endpoints. Especially at higher concentrations, most observations proved toxicant-unspecific. With decreasing concentrations, however, observations declined in number, but gained in specificity. Specific observations may at best be made at test concentrations ≤ EC10. The existence of a "fingerprint" based on morphological observations in the FET is, therefore, highly unlikely in the range of acute toxicity, but cannot be excluded for experiments at sublethal concentrations.

What to Expect When Expecting in Lab: A Review of Unique Risks and Resources for Pregnant Researchers in the Chemical Laboratory

Pregnancy presents a unique risk to chemical researchers due to their occupational exposures to chemical, equipment, and physical hazards in chemical research laboratories across science, engineering, and technology disciplines. Understanding “risk” as a function of hazard, exposure, and vulnerability, this review aims to critically examine the state of the science for the risks and associated recommendations (or lack thereof) for pregnant researchers in chemical laboratories (labs). Commonly encountered hazards for pregnant lab workers include chemical hazards (organic solvents, heavy metals, engineered nanomaterials, and endocrine disruptors), radiation hazards (ionizing radiation producing equipment and materials and nonionizing radiation producing equipment), and other hazards related to the lab environment (excessive noise, excessive heat, psychosocial stress, strenuous physical work, and/or abnormal working hours). Lab relevant doses and routes of exposure in the chemical lab environment along with literature and governmental recommendations or resources for exposure mitigation are critically assessed. The specific windows of vulnerability based on stage of pregnancy are described for each hazard, if available. Finally, policy gaps for further scientific research are detailed to enhance future guidance to protect pregnant lab workers.

Exploration of the DARTable Genome- a Resource Enabling Data-Driven NAMs for Developmental and Reproductive Toxicity Prediction

The identification of developmental and reproductive toxicity (DART) is a critical component of toxicological evaluations of chemical safety. Adverse Outcome Pathways (AOPs) provide a framework to describe biological processes leading to a toxic effect and can provide insights in understanding the mechanisms underlying toxicological endpoints and aid the development of new approach methods (NAMs). Integrated approaches to testing and assessment (IATA) can be developed based on AOP knowledge and can serve as pragmatic approaches to chemical hazard characterization using NAMs. However, DART effects remain difficult to predict given the diversity of biological mechanisms operating during ontogenesis and consequently, the considerable number of potential molecular initiating events (MIEs) that might trigger a DART Adverse Outcome (DART AO). Consequently, two challenges that need to be overcome to create an AOP-based DART IATA are having sufficient knowledge of relevant biology and using this knowledge to determine the appropriate selection of cell systems that provide sufficient coverage of that biology. The wealth of modern biological and bioinformatics data can be used to provide this knowledge. Here we demonstrate the utility of bioinformatics analyses to address these questions. We integrated known DART MIEs with gene-developmental phenotype information to curate the hypothetical human DARTable genome (HDG, ∼5 k genes) which represents the comprehensive set of biomarkers for DART. Using network analysis of the human interactome, we show that HDG genes have distinct connectivity compared to other genes. HDG genes have higher node degree with lower neighborhood connectivity, betweenness centralities and average shortest path length. Therefore, HDG is highly connected to itself and to the wider network and not only to their local community. Also, by comparison with the Druggable Genome we show how the HDG can be prioritized to identify potential MIEs based on potential to interact with small molecules. We demonstrate how the HDG in combination with gene expression data can be used to select a panel of relevant cell lines (RD-1, OVCAR-3) for inclusion in an IATA and conclude that bioinformatic analyses can provide the necessary insights and serve as a resource for the development of a screening panel for a DART IATA.

Developmental toxicity testing of unsubstituted and methylated 4- and 5-ring polycyclic aromatic hydrocarbons using the zebrafish embryotoxicity test

The present study evaluates the in vitro developmental toxicity of 4- and 5-ring PAHs including benz[a]anthracene and benzo[a]pyrene and six of their monomethylated congeners, and dibenz[a,h]anthracene using the zebrafish embryotoxicity test (ZET). In general, the tested PAHs induced various developmental effects in the zebrafish embryos including unhatched embryos, no movement and circulation, yolk sac and pericardial edemas, deformed body shape, and cumulative mortality at 96 h post fertilization (hpf). The alkyl substituent on different positions of the aromatic ring of the PAHs appeared to change their in vitro developmental toxicity. Comparison to a previously reported molecular docking study showed that the methyl substituents may affect the interaction of the PAHs with the aryl hydrocarbon receptor (AhR) which is known to play a role in the developmental toxicity of some PAHs. Taken together, our results show that methylation can either increase or decrease the developmental toxicity of PAHs and suggest this may relate to effects on the molecular dimensions and resulting consequences for interactions with the AhR.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Constructing a developmental and reproductive toxicity database of chemicals (DART NIHS DB) for integrated approaches to testing and assessment

Developmental and reproductive toxicity (DART) is an important endpoint, and databases (DBs) are essential for evaluating the risk of untested substances using alternative methods. We have constructed a reliable and transparent DART DB, which we named DART NIHS DB, using the publicly available datasets of DART studies of industrial chemicals conducted by Japanese government ministries in accordance with the corresponding OECD test guidelines (OECD TG421 and TG422). This DB is unique because its dataset chemicals have little overlap with those of ToxRefDB, which compiles large-scale DART data, and it is reliable because the included datasets were created after reviewing the individual study reports. In DART NIHS DB, 171 of 404 substances exhibited signs of DART, which occurred during fertility and early embryonic development (49 substances), organogenesis (59 substances), and the perinatal period (161 substances). When the lowest-observed-adverse-effect level (LOAEL) of DART was compared with that of repeated-dose toxicity (RDT), 15 substances (12%) had a lower LOAEL for DART than for RDT. Of these, five substances displayed significant DART at doses of ≤ 50 mg/kg bw/day. The chemical and toxicity information in this DB will be useful for the development of stage-specific adverse outcome pathways (AOPs) via integration with mechanistic information. The whole datasets of the DB can be implemented in read-across support tools such as the OECD QSAR Toolbox, which will further lead to future integrated approaches to testing and assessment based on AOPs.

Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data

Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

Assessment of the in vitro developmental toxicity of diethylstilbestrol and estradiol in the zebrafish embryotoxicity test

The present study investigated the developmental toxicity of diethylstilbestrol (DES) in the zebrafish embryotoxicity test (ZET). This was done to investigate whether the ZET would better capture the developmental toxicity of DES than the embryonic stem cells test (EST) that was previously shown to underpredict the DES-induced developmental toxicity as compared to in vivo data, potentially because the EST does not capture late events in the developmental process. The ZET results showed DES-induced growth retardation, cumulative mortality and dysmorphisms (i.e. induction of pericardial edema) in zebrafish embryos while the endogenous ERα agonist 17β-estradiol (E2) showed only growth retardation and cumulative mortality with lower potency compared to DES. Furthermore, the DES-induced pericardial edema formation in zebrafish embryos could be counteracted by co-exposure with ERα antagonist fulvestrant, indicating that the ZET captures the role of ERα in the mode of action underlying the developmental toxicity of DES. Altogether, it is concluded that the ZET differentiates DES from E2 with respect to their developmental toxicity effects, while confirming the role of ERα in mediating the developmental toxicity of DES. Furthermore, comparison to in vivo data revealed that, like the EST, in a quantitative way also the ZET did not capture the relatively high in vivo potency of DES as a developmental toxicant.

Retinoid signaling in skeletal development: Scoping the system for predictive toxicology

All-trans retinoic acid (ATRA), the biologically active form of vitamin A, is instrumental in regulating the patterning and specification of the vertebrate embryo. Various animal models demonstrate adverse developmental phenotypes following experimental retinoid depletion or excess during pregnancy. Windows of vulnerability for altered skeletal patterning coincide with early specification of the body plan (gastrulation) and regional specification of precursor cell populations forming the facial skeleton (cranial neural crest), vertebral column (somites), and limbs (lateral plate mesoderm) during organogenesis. A common theme in physiological roles of ATRA signaling is mutual antagonism with FGF signaling. Consequences of genetic errors or environmental disruption of retinoid signaling include stage- and region-specific homeotic transformations to severe deficiencies for various skeletal elements. This review derives from an annex in Detailed Review Paper (DRP) of the OECD Test Guidelines Programme (Project 4.97) to support recommendations regarding assay development for the retinoid system and the use of resulting data in a regulatory context for developmental and reproductive toxicity (DART) testing.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Children's Environmental Health: A Systems Approach for Anticipating Impacts from Chemicals

Increasing numbers of chemicals are on the market and present in consumer products. Emerging evidence on the relationship between environmental contributions and prevalent diseases suggests associations between early-life exposure to manufactured chemicals and a wide range of children’s health outcomes. Using current assessment methodologies, public health and chemical management decisionmakers face challenges in evaluating and anticipating the potential impacts of exposure to chemicals on children’s health in the broader context of their physical (built and natural) and social environments. Here, we consider a systems approach to address the complexity of children’s environmental health and the role of exposure to chemicals during early life, in the context of nonchemical stressors, on health outcomes. By advancing the tools for integrating this more complex information, the scope of considerations that support chemical management decisions can be extended to include holistic impacts on children’s health.

An ontology for developmental processes and toxicities of neural tube closure

In recent years, the development and implementation of animal-free approaches to chemical and pharmaceutical hazard and risk assessment has taken off. Alternative approaches are being developed starting from the perspective of human biology and physiology. Neural tube closure is a vital step that occurs early in human development. Correct closure of the neural tube depends on a complex interplay between proteins along a number of protein concentration gradients. The sensitivity of neural tube closure to chemical disturbance of signalling pathways such as the retinoid pathway, is well known. To map the pathways underlying neural tube closure, literature data on the molecular regulation of neural tube closure were collected. As the process of neural tube closure is highly conserved in vertebrates, the extensive literature available for the mouse was used whilst considering its relevance for humans. Thus, important cell compartments, regulatory pathways, and protein interactions essential for neural tube closure under physiological circumstances were identified and mapped. An understanding of aberrant processes leading to neural tube defects (NTDs) requires detailed maps of neural tube embryology, including the complex genetic signals and responses underlying critical cellular dynamical and biomechanical processes. The retinoid signaling pathway serves as a case study for this ontology because of well-defined crosstalk with the genetic control of neural tube patterning and morphogenesis. It is a known target for mechanistically-diverse chemical structures that disrupt neural tube closure The data presented in this manuscript will set the stage for constructing mathematical models and computer simulation of neural tube closure for human-relevant AOPs and predictive toxicology.

Use of computational toxicology (CompTox) tools to predict in vivo toxicity for risk assessment

Computational Toxicology tools were used to predict toxicity for three pesticides: propyzamide (PZ), carbaryl (CB) and chlorpyrifos (CPF). The tools used included: a) ToxCast/Tox21 assays (AC₅₀ s μM: concentration 50% maximum activity); b) in vitro-to-in vivo extrapolation (IVIVE) using ToxCast/Tox21 AC₅₀s to predict administered equivalent doses (AED: mg/kg/d) to compare to known in vivo Lowest-Observed-Effect-Level (LOEL)/Benchmark Dose (BMD); c) high throughput toxicokinetics population based (HTTK-Pop) using AC₅₀s for endpoints associated with the mode of action (MOA) to predict age-adjusted AED for comparison with in vivo LOEL/BMDs. ToxCast/Tox21 active-hit-calls for each chemical were predictive of targets associated with each MOA, however, assays directly relevant to the MOAs for each chemical were limited. IVIVE AEDs were predictive of in vivo LOEL/BMD₁₀s for all three pesticides. HTTK-Pop was predictive of in vivo LOEL/BMD₁₀s for PZ and CPF but not for CB after human age adjustments 11-15 (PZ) and 6-10 (CB) or 6-10 and 11-20 (CPF) corresponding to treated rat ages (in vivo endpoints). The predictions of computational tools are useful for risk assessment to identify targets in chemical MOAs and to support in vivo endpoints. Data can also aid is decisions about the need for further studies.

Computational approach for collection and prediction of molecular initiating events in developmental toxicity

Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.

Predicting the safety of medicines in pregnancy: A workshop report

The framework for developmental toxicity testing has remained largely unchanged for over 50 years and although it remains invaluable in assessing potential risks in pregnancy, knowledge gaps exist, and some outcomes do not necessarily correlate with clinical experience. Advances in omics, in silico approaches and alternative assays are providing opportunities to enhance our understanding of embryo-fetal development and the prediction of potential risks associated with the use of medicines in pregnancy. A workshop organised by the Medicines and Healthcare products Regulatory Agency (MHRA), "Predicting the Safety of Medicines in Pregnancy - a New Era?", was attended by delegates representing regulatory authorities, academia, industry, patients, funding bodies and software developers to consider how to improve the quality of and access to nonclinical developmental toxicity data and how to use this data to better predict the safety of medicines in human pregnancy. The workshop delegates concluded that based on comparative data to date alternative methodologies are currently no more predictive than conventional methods and not qualified for use in regulatory submissions. To advance the development and qualification of alternative methodologies, there is a requirement for better coordinated multidisciplinary cross-sector interactions coupled with data sharing. Furthermore, a better understanding of human developmental biology and the incorporation of this knowledge into the development of alternative methodologies is essential to enhance the prediction of adverse outcomes for human development. The output of the workshop was a series of recommendations aimed at supporting multidisciplinary efforts to develop and validate these alternative methodologies.

A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health

The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.

Translational Toxicology in Zebrafish

A major goal of translational toxicology is to identify adverse chemical effects and determine whether they are conserved or divergent across experimental systems. Translational toxicology encompasses assessment of chemical toxicity across multiple life stages, determination of toxic mode-of-action, computational prediction modeling, and identification of interventions that protect or restore health following toxic chemical exposures. The zebrafish is increasingly used in translational toxicology because it combines the genetic and physiological advantages of mammalian models with the higher-throughput capabilities and genetic manipulability of invertebrate models. Here, we review recent literature demonstrating the power of the zebrafish as a model for addressing all four activities of translational toxicology. Important data gaps and challenges associated with using zebrafish for translational toxicology are also discussed.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

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.

Characterizing cleft palate toxicants using ToxCast data, chemical structure, and the biomedical literature

Cleft palate has been linked to both genetic and environmental factors that perturb key events during palatal morphogenesis. As a developmental outcome, it presents a challenging, mechanistically complex endpoint for predictive modeling. A data set of 500 chemicals evaluated for their ability to induce cleft palate in animal prenatal developmental studies was compiled from Toxicity Reference Database and the biomedical literature, which included 63 cleft palate active and 437 inactive chemicals. To characterize the potential molecular targets for chemical-induced cleft palate, we mined the ToxCast high-throughput screening database for patterns and linkages in bioactivity profiles and chemical structural descriptors. ToxCast assay results were filtered for cytotoxicity and grouped by target gene activity to produce a "gene score." Following unsuccessful attempts to derive a global prediction model using structural and gene score descriptors, hierarchical clustering was applied to the set of 63 cleft palate positives to extract local structure-bioactivity clusters for follow-up study. Patterns of enrichment were confirmed on the complete data set, that is, including cleft palate inactives, and putative molecular initiating events identified. The clusters corresponded to ToxCast assays for cytochrome P450s, G-protein coupled receptors, retinoic acid receptors, the glucocorticoid receptor, and tyrosine kinases/phosphatases. These patterns and linkages were organized into preliminary decision trees and the resulting inferences were mapped to a putative adverse outcome pathway framework for cleft palate supported by literature evidence of current mechanistic understanding. This general data-driven approach offers a promising avenue for mining chemical-bioassay drivers of complex developmental endpoints where data are often limited.

Assembling systems biology, embryo development and teratogenesis: What do we know so far and where to go next?

The recognition of molecular mechanisms of a teratogen can provide insights to understand its embryopathy, and later to plan strategies for the prevention of new exposures. In this context, experimental research is the most invested approach. Despite its relevance, these assays require financial and time investment. Hence, the evaluation of such mechanisms through systems biology rise as an alternative for this conventional methodology. Systems biology is an integrative field that connects experimental and computational analyses, assembling interaction networks between genes, proteins, and even teratogens. It is a valid strategy to generate new hypotheses, that can later be confirmed in experimental assays. Here, we present a literature review of the application of systems biology in embryo development and teratogenesis studies. We provide a glance at the data available in public databases, and evaluate common mechanisms between different teratogens. Finally, we discuss the advantages of using this strategy in future teratogenesis researches.

Systems Modeling of Developmental Vascular Toxicity

The more than 80,000 chemicals in commerce present a challenge for hazard assessments that toxicity testing in the 21^st century strives to address through high-throughput screening (HTS) assays. Assessing chemical effects on human development adds an additional layer of complexity to the screening, with a need to capture complex and dynamic events essential for proper embryo-fetal development. HTS data from ToxCast/Tox21 informs systems toxicology models, which incorporate molecular targets and biological pathways into mechanistic models describing the effects of chemicals on human cells, 3D organotypic culture models, and small model organisms. Adverse Outcome Pathways (AOPs) provide a useful framework for integrating the evidence derived from these in silico and in vitro systems to inform chemical hazard characterization. To illustrate this formulation, we have built an AOP for developmental toxicity through a mode of action linked to embryonic vascular disruption (Aop43). Here, we review the model for quantitative prediction of developmental vascular toxicity from ToxCast HTS data and compare the HTS results to functional vascular development assays in complex cell systems, virtual tissues, and small model organisms. ToxCast HTS predictions from several published and unpublished assays covering different aspects of the angiogenic cycle were generated for a test set of 38 chemicals representing a range of putative vascular disrupting compounds (pVDCs). Results boost confidence in the capacity to predict adverse developmental outcomes from HTS in vitro data and model computational dynamics for in silico reconstruction of developmental systems biology. Finally, we demonstrate the integration of the AOP and developmental systems toxicology to investigate the unique modes of action of two angiogenesis inhibitors.

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.

Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data

The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to assess toxicity as early as possible. To enable the development of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capability. For this, a large scale analysis on the entire database has been performed to build models to predict bioactivities measured in in vitro assays. Simple classical QSAR algorithms (ANN, SVM, LDA, random forest, and Bayesian) were first applied on the data, and the results of these algorithms suggested that they do not seem to be well-suited for data sets with a high proportion of inactive compounds. The study then showed for the first time that the use of an ensemble method named "Stacked generalization" could improve the model performance on this type of data. Indeed, for 61% of 483 models, the Stacked method led to models with higher performance. Moreover, the combination of this ensemble method with an applicability domain filter allows one to assess the reliability of the predictions for further compound prioritization. In particular we showed that for 50% of the models, the ROC score is better if we do not consider the compounds that are not within the applicability domain.

Endocrine-Mediated Mechanisms of Metabolic Disruption and New Approaches to Examine the Public Health Threat

Obesity and metabolic disorders are of great societal concern and generate substantial human health care costs globally. Interventions have resulted in only minimal impacts on disrupting this worsening health trend, increasing attention on putative environmental contributors. Exposure to numerous environmental contaminants have, over decades, been demonstrated to result in increased metabolic dysfunction and/or weight gain in cell and animal models, and in some cases, even in humans. There are numerous mechanisms through which environmental contaminants may contribute to metabolic dysfunction, though certain mechanisms, such as activation of the peroxisome proliferator activated receptor gamma or the retinoid x receptor, have received considerably more attention than less-studied mechanisms such as antagonism of the thyroid receptor, androgen receptor, or mitochondrial toxicity. As such, research on putative metabolic disruptors is growing rapidly, as is our understanding of molecular mechanisms underlying these effects. Concurrent with these advances, new research has evaluated current models of adipogenesis, and new models have been proposed. Only in the last several years have studies really begun to address complex mixtures of contaminants and how these mixtures may disrupt metabolic health in environmentally relevant exposure scenarios. Several studies have begun to assess environmental mixtures from various environments and study the mechanisms underlying their putative metabolic dysfunction; these studies hold real promise in highlighting crucial mechanisms driving observed organismal effects. In addition, high-throughput toxicity databases (ToxCast, etc.) may provide future benefits in prioritizing chemicals for in vivo testing, particularly once the causative molecular mechanisms promoting dysfunction are better understood and expert critiques are used to hone the databases. In this review, we will review the available literature linking metabolic disruption to endocrine-mediated molecular mechanisms, discuss the novel application of environmental mixtures and implications for in vivo metabolic health, and discuss the putative utility of applying high-throughput toxicity databases to answering complex organismal health outcome questions.

The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives

The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.

Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals

Recent efforts aimed at integrating in vitro high-throughput screening (HTS) data into chemical toxicity assessments are necessitating increased understanding of concordance between chemical-induced responses observed in vitro versus in vivo. This investigation set out to (1) measure concordance between in vitro HTS data and transcriptomic responses observed in vivo, focusing on the liver, and (2) identify attributes that can influence concordance. Signal response profiles from 130 substances were compared between in vitro data produced through Tox21 and liver transcriptomic data through DrugMatrix, collected from rats exposed to a chemical for ≤5 days. A global in vitro-to-in vivo comparative analysis based on pathway-level responses resulted in an overall average percent agreement of 79%, ranging on a per-chemical basis between 41% and 100%. Whereas concordance amongst inactive chemicals was high (89%), concordance amongst chemicals showing in vitro activity was only 13%, suggesting that follow-up in vivo and/or orthogonal in vitro assays would improve interpretations of in vitro activity. Attributes identified to influence concordance included experimental design attributes (eg, cell type), target pathways, and physicochemical properties (eg, logP). The attribute that most consistently increased concordance was dose applicability, evaluated by filtering for experimental doses administered to rats that were within 10-fold of those related to likely bioactivity, derived using Tox21 data and high-throughput toxicokinetic modeling. Together, findings suggest that in vitro screening approaches to predict in vivo toxicity are viable particularly when certain attributes are considered, including whether activity versus inactivity is observed, experimental design, chemical properties, and dose applicability.