A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways

Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening

Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integrated machine learning, nontargeted analysis, and in vitro biotesting to identify neurotoxic drivers of acetylcholinesterase (AChE) inhibition in estuarine waters along the coast of China. Machine learning was used to predict AChE inhibitors in a large chemical space. The prediction output was profiled into a suspect screening list to guide high-resolution mass spectrometry (HRMS) screening of AChE inhibitors in estuarine water samples. Ultimately, 60 chemicals with diverse known and presently unknown structures were identified, explaining 82.1% of the observed AChE inhibition. Polyunsaturated fatty acids were unexpectedly found to be neurotoxic drivers, accounting for 80.5% of the overall effect. This proof-of-concept study demonstrates that machine learning-based toxicological prediction can achieve a virtual fractionation role to pinpoint HRMS features with the bioactivity potential. Our approach is expected to enable rapid and comprehensive screening of organic pollutants associated with various in vitro end points for large-scale monitoring of water quality.

Ascertaining sensitive exposure biomarkers of various metal(loid)s to embryo implantation

Close relationships exist between metal(loid)s exposure and embryo implantation failure (EIF) from animal and epidemiological studies. However, there are still inconsistent results and lacking of sensitive metal(loid) exposure biomarkers associated with EIF risk. We aimed to ascertain sensitive metal(loid) biomarkers to EIF and provide potential biological explanations. Candidate metal(loid) biomarkers were measured in the female hair (FH), female serum (FS), and follicular fluid (FF) with various exposure time periods. An analytical framework was established by integrating epidemiological association results, comprehensive literature searching, and knowledge-based adverse outcome pathway (AOP) networks. The sensitive biomarkers of metal(loid)s along with potential biological pathways to EIF were identified in this framework. Among the concerned 272 candidates, 45 metal(loid)s biomarkers across six time periods and three biomatrix were initially identified by single-metal(loid) analyses. Two biomarkers with counterfactual results according to literature summary results were excluded, and a total of five biomarkers were further determined from 43 remained candidates in mixture models. Finally, four sensitive metal(loid) biomarkers were eventually assessed by overlapping AOP networks information, including Se and Co in FH, and Fe and Zn in FS. AOP networks also identified key GO pathways and proteins involved in regulation of oxygen species biosynthetic, cell proliferation, and inflammatory response. Partial dependence results revealed Fe in FS and Co in FH at their low levels might be potential sensitive exposure levels for EIF. Our study provided a typical framework to screen the crucial metal(loid) biomarkers and ascertain that Se and Co in FH, and Fe and Zn in FS played an important role in embryo implantation.

Screening and Evaluation of Children's Sensitively Toxic Chemicals in New Mosquito Repellent Products Based on a Nationwide Investigation

New mosquito repellent products (NMRPs) are emerging popular repellents among children. There are increasing reports on children's sensitization reactions caused by NMRPs, while regulations on their productions, sales, or usage are still lacking. One of the reasons could be the missing comprehensive risk assessment. We first conducted a nationwide investigation on children's NMRP usage preferences. Then, we high-throughput screened volatile or semivolatile organic chemicals (VOCs/SVOCs) in five representative NMRPs by the headspace gas chromatography-orbitrap high-resolution mass spectrometry analytical method. After that, toxic compounds were recognized based on the toxicity forecaster (ToxCast) database. A total of 277 VOCs/SVOCs were recognized, and 70 of them were identified as toxic compounds. In a combination of concentrations, toxicities, absorption, distribution, metabolism, and excretion characteristics in the body, 28 chemicals were finally proposed as priority-controlled compounds in NMRPs. Exposure risks of recognized toxic chemicals through NMRPs by inhalation and dermal intake for children across the country were also assessed. Average daily intakes were in the range of 0.20-7.31 mg/kg/day for children in different provinces, and the children in southeastern coastal provinces were found to face higher exposure risks. By controlling the high-priority chemicals, the risks were expected to be reduced by about 46.8% on average. Results of this study are therefore believed to evaluate exposure risks, encourage safe production, and promote reasonable management of NMRPs.

Recognition and Health Impacts of Organic Pollutants with Significantly Different Proportions in the Gas Phase and Size-Fractionated Particulate Phase in Ambient Air

The distributions of organic pollutants in the gas phase and size-fractionated particle phases can largely affect human health risks posed by them. Gas-particle partitioning and particle-size distributions of some known pollutants have been investigated. However, the pollutants which are more likely to enter the human body and cause strong adverse effects may be neglected. In this study, a nontargeted screening approach combining comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry and chemometrics was developed. Eighty-eight compounds with markedly different proportions in the gas phase and PM₁, as well as 50 contaminants with significant differences in PM₁ and particles with diameters of 1-2.5 μm, were identified. Of these compounds, 18 were found in the air for the first time. There were obvious discrepancies between the measured and predicted gas-particle partitioning coefficients for some pollutants, suggesting unexpected environmental fates and health risks. The human daily intakes through inhalation and dermal exposure to these pollutants were estimated with the International Commission on Radiological Protection deposition model and transdermal permeability model. A risk-based prioritization was performed. The results indicated that adverse effects posed by 9H-fluoren-9-one, 2-ethylhexyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate, p-cumenol, 2,4-diisocyanato-1-methyl-benzene, bis(2-ethylhexyl) phthalate, perylene, (E)-cinnamaldehyde, 4-methyl-2-nitro-phenol, benzoic acid, and bis(2-methylpropyl) ester hexanedioic acid in ambient air may be more severe than those posed by conventionally monitored pollutants. The findings would facilitate raising concerns about these pollutants before they cause further severe and widespread impacts.

Screening of ToxCast Chemicals Responsible for Human Adverse Outcomes with Exposure to Ambient Air

Air pollution poses a major threat to global public health. Although there have been a few investigations into the relationships between organic pollutants and adverse outcomes, the responsible components and molecular mechanisms may be ignored. In this study, a suspect screening method combining comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS) with the Toxicity Forecaster (ToxCast) database was applied to analyze complex hydrophobic compounds in ambient air and prospectively figure out toxicologically significant compounds. Seventy-six ToxCast compounds were screened, including seven pollutants receiving less attention and five chemicals never published in the air previously. Given the concentrations, bioactivities, as well as absorption, distribution, metabolism, and excretion properties in vivo, 29 contaminants were assigned high priority since they had active biological effects in the vascular, lung, liver, kidney, prostate, and bone tissues. Phenotypic linkages of key pollutants to potential mechanistic pathways were explored by systems toxicology. A total of 267 chemical-effect pathways involving 29 toxicants and 31 molecular targets were mapped in bipartite network, in which 12 key pathogenic pathways were clarified, which not only provided evidence supporting the previous hypothesis but also provided new insights into the molecular targets. The results would facilitate the development of pollutant priority control, population intervention, and clinical therapeutic strategies so as to substantially reduce human health hazards induced by urban air.

High-throughput screening for identifying acetylcholinesterase inhibitors: Insights on novel inhibitors and the use of liver microsomes

Rapid, higher throughput, and predictive toxicological methods are needed to assess vast numbers of chemicals with unknown safety profiles. A current effort towards this goal is Toxicology in the 21st Century (Tox21), a United States government consortium using a battery of in vitro assays to screen a library of 10,000 compounds relevant to food, drug, and environmental safety. Recently, we implemented in vitro assays for measuring acetylcholinesterase (AChE) inhibition, a mechanism of toxicity, into Tox21's high-throughput screening campaign (Li S., et al. Environ Health Persp 2021;129:047008, doi:10.1289/EHP6993). In this Commentary, we provide detailed insights on two topics related to our article: (1) prioritizing recently discovered AChE inhibitors from our screening based upon physiological relevance and (2) incorporating human liver microsomes into the AChE inhibition assay to identify metabolically active AChE inhibitors.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

In vitro to in vivo extrapolation for high throughput prioritization and decision making

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

The role of high-throughput screening in ecotoxicology and engineered nanomaterials

The field of environmental toxicology developed as a result of growing concerns about anthropogenic influences on the environment and how to ameliorate ecological impact. Many governmental bodies are beginning to emphasize prevention rather than mitigation when addressing novel products, leading to more of a focus on identifying potential toxicity prior to release. With the exponential advances in their development and sale, novel metamaterials and biotechnology are set to dramatically outpace the capabilities of current testing strategies. To address the need for a fast, cost-effective means of testing chemicals, high-throughput screening (HTS) is currently being used in toxicology and being adapted to ecotoxicology in projects such as ToxCast and Tox21. Despite the growth of research using HTS platforms, its role in ecotoxicology is still uncertain, particularly in how it should be applied in regulation. The aim of the present review is to discuss common test strategies used in designing HTS platforms, the current potential applications for ecotoxicological research, its role in regulatory policies, and its ability to address growing concerns such as engineered nanomaterials. Environ Toxicol Chem 2017;36:1704-1714. © 2017 SETAC.

From the exposome to mechanistic understanding of chemical-induced adverse effects

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health.

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.

Estimating Margin of Exposure to Thyroid Peroxidase Inhibitors Using High-Throughput in vitro Data, High-Throughput Exposure Modeling, and Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling

Some pharmaceuticals and environmental chemicals bind the thyroid peroxidase (TPO) enzyme and disrupt thyroid hormone production. The potential for TPO inhibition is a function of both the binding affinity and concentration of the chemical within the thyroid gland. The former can be determined through in vitro assays, and the latter is influenced by pharmacokinetic properties, along with environmental exposure levels. In this study, a physiologically based pharmacokinetic (PBPK) model was integrated with a pharmacodynamic (PD) model to establish internal doses capable of inhibiting TPO in relation to external exposure levels predicted through exposure modeling. The PBPK/PD model was evaluated using published serum or thyroid gland chemical concentrations or circulating thyroxine (T4) and triiodothyronine (T3) hormone levels measured in rats and humans. After evaluation, the model was used to estimate human equivalent intake doses resulting in reduction of T4 and T3 levels by 10% (ED10) for 6 chemicals of varying TPO-inhibiting potencies. These chemicals were methimazole, 6-propylthiouracil, resorcinol, benzophenone-2, 2-mercaptobenzothiazole, and triclosan. Margin of exposure values were estimated for these chemicals using the ED10 and predicted population exposure levels for females of child-bearing age. The modeling approach presented here revealed that examining hazard or exposure alone when prioritizing chemicals for risk assessment may be insufficient, and that consideration of pharmacokinetic properties is warranted. This approach also provides a mechanism for integrating in vitro data, pharmacokinetic properties, and exposure levels predicted through high-throughput means when interpreting adverse outcome pathways based on biological responses.

Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery

Ecological risk assessment is the process of evaluating how likely it is that the environment may be impacted as the result of exposure to one or more chemicals and/or other stressors. It is not playing as large a role in environmental management decisions as it should be. A core challenge is that risk assessments often do not relate directly or transparently to protection goals. There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance - whole organisms, populations, communities, and ecosystems - are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.