Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Hazards of polycyclic aromatic hydrocarbons: a review on occurrence, detection, and role of green nanomaterials on the removal of PAH from the water environment

Organic pollutant contamination in the environment is a serious and dangerous issue, especially for developing countries. Among all organic pollutants, polycyclic aromatic hydrocarbons (PAHs) are the more frequently discovered ones in the environment. PAH contamination is caused chiefly by anthropogenic sources, such as the disposal of residential and industrial waste and automobile air emissions. They are gaining interest due to their environmental persistence, toxicity, and probable bioaccumulation. The existence of PAHs may result in damage to the environment and living things, and there is widespread concern about the acute and chronic threats posed by the release of these contaminants. The detection and elimination of PAHs from wastewater have been the focus of numerous technological developments during recent decades. The development of sensitive and economical monitoring systems for detecting these substances has attracted a lot of scientific attention. Using several nanomaterials and nanocomposites is a promising treatment option for the identification and elimination of PAHs in aquatic ecosystems. This review elaborated on the sources of origin, pathogenicity, and widespread occurrence of PAHs. In addition, the paper highlighted the use of nanomaterial-based sensors in detecting PAHs from contaminated sites and nanomaterial-based absorbents in PAH elimination from wastewater. This review also addresses the development of Graphene and Biofunctionalized nanomaterials for the elimination of PAHs from the contaminated sites.

Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals

Modern toxicology's throughput has dramatically increased due to alternative models, laboratory automation, and machine learning. This has enabled comparative studies across species and assays to prioritize chemical hazard potential and to understand how different model systems might complement one another. However, such comparative studies of high-throughput data are still in their infancy, with more groundwork needed to firmly establish the approach. Therefore, this study aimed to compare the bioactivity of the NIEHS Division of Translational Toxicology's (DTT) 87-compound developmental neurotoxicant (DNT) library in zebrafish and an in vitro high-throughput cell culture system. The early life-stage zebrafish provided a whole animal approach to developmental toxicity assessment. Chemical hits for abnormalities in embryonic zebrafish morphology, mortality, and behavior (ZBEscreen™) were compared with chemicals classified as high-risk by the Cell Health Index (CHI™), which is an outcome class probability from a machine learning classifier using 12 parameters from the SYSTEMETRIC® Cell Health Screen (CHS). The CHS was developed to assess human toxicity risk using supervised machine learning to classify acute cell stress phenotypes in a human leukemia cell line (HL60 cells) following a 4-h exposure to a chemical of interest. Due to the design of the screen, the zebrafish assays were more exhaustive, yielding 86 total bioactive hits, whereas the SYSTEMETRIC® CHS focusing on acute toxicity identified 20 chemicals as potentially toxic. The zebrafish embryonic and larval photomotor response assays (EPR and LPR, respectively) detected 40 of the 47 chemicals not found by the zebrafish morphological screen and CHS. Collectively, these results illustrate the advantages of using two alternative models in tandem for rapid hazard assessment and chemical prioritization and the effectiveness of CHI™ in identifying toxicity within a single multiparametric assay.

Integration of Adverse Outcome Pathways, Causal Networks and ‘Omics to Support Chemical Hazard Assessment

Several approaches have been used in an attempt to simplify and codify the events that lead to adverse effects of chemicals including systems biology, ‘omics, in vitro assays and frameworks such as the Adverse Outcome Pathway (AOP). However, these approaches are generally not integrated despite their complementary nature. Here we propose to integrate toxicogenomics data, systems biology information and AOPs using causal biological networks to define Key Events in AOPs. We demonstrate this by developing a causal subnetwork of 28 nodes that represents the Key Event of regenerative proliferation – a critical event in AOPs for liver cancer. We then assessed the effects of three chemicals known to cause liver injury and cell proliferation (carbon tetrachloride, aflatoxin B₁, thioacetamide) and two with no known cell proliferation effects (diazepam, simvastatin) on the subnetwork using rat liver gene expression data from the toxicogenomic database Open TG-GATEs. Cyclin D1 (Ccnd1), a gene both causally linked to and sufficient to infer regenerative proliferation activity, was overexpressed after exposures to carbon tetrachloride, aflatoxin B₁ and thioacetamide, but not in exposures to diazepam and simvastatin. These results were consistent with known effects on rat livers and liver pathology of exposed rats. Using these approaches, we demonstrate that transcriptomics, AOPs and systems biology can be applied to examine the presence and progression of AOPs in order to better understand the hazards of chemical exposure.

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Advancing the adverse outcome pathway framework-An international horizon scanning approach

Our ability to conduct whole-organism toxicity tests to understand chemical safety has been outpaced by the synthesis of new chemicals for a wide variety of commercial applications. As a result, scientists and risk assessors are turning to mechanistically based studies to increase efficiencies in chemical risk assessment and making greater use of in vitro and in silico methods to evaluate potential environmental and human health hazards. In this context, the adverse outcome pathway (AOP) framework has gained traction in regulatory science because it offers an efficient and effective means for capturing available knowledge describing the linkage between mechanistic data and the apical toxicity end points required for regulatory assessments. A number of international activities have focused on AOP development and various applications to regulatory decision-making. These initiatives have prompted dialogue between research scientists and regulatory communities to consider how best to use the AOP framework. Although expert-facilitated discussions and AOP development have been critical in moving the science of AOPs forward, it was recognized that a survey of the broader scientific and regulatory communities would aid in identifying current limitations while guiding future initiatives for the AOP framework. To that end, a global horizon scanning exercise was conducted to solicit questions concerning the challenges or limitations that must be addressed to realize the full potential of the AOP framework in research and regulatory decision-making. The questions received fell into several broad topical areas: AOP networks, quantitative AOPs, collaboration on and communication of AOP knowledge, AOP discovery and development, chemical and cross-species extrapolation, exposure/toxicokinetics considerations, and AOP applications. Expert ranking was then used to prioritize questions for each category, where 4 broad themes emerged that could help inform and guide future AOP research and regulatory initiatives. In addition, frequently asked questions were identified and addressed by experts in the field. Answers to frequently asked questions will aid in addressing common misperceptions and will allow for clarification of AOP topics. The need for this type of clarification was highlighted with surprising frequency by our question submitters, indicating that improvements are needed in communicating the AOP framework among the scientific and regulatory communities. Overall, horizon scanning engaged the global scientific community to help identify key questions surrounding the AOP framework and guide the direction of future initiatives. Environ Toxicol Chem 2017;36:1411-1421. © 2017 SETAC.