Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk

Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals

Accurate prediction of parameters related to the environmental exposure of chemicals is crucial for the sound management of chemicals. However, the lack of large data sets for training models may result in poor prediction accuracy and robustness. Herein, integrated transfer learning (TL) and multitask learning (MTL) was proposed for constructing a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model) using n-octanol/water partition coefficients as a source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation parameters based on enlarged data sets that cover 2496 compounds with at least one bioaccumulation parameter. Results show that the TL-MTL-GNN model outperformed single-task GNN models with and without the TL, as well as conventional machine learning models trained with molecular descriptors or fingerprints. Applicability domains were characterized by a state-of-the-art structure-activity landscape-based (abbreviated as ADSAL) methodology. The TL-MTL-GNN model coupled with the optimal ADSAL was employed to predict bioaccumulation parameters for around 60,000 chemicals, with more than 13,000 compounds identified as bioaccumulative chemicals. The high predictive accuracy and robustness of the TL-MTL-GNN model demonstrate the feasibility of integrating the TL and MTL strategy in modeling small-sized data sets. The strategy holds significant potential for addressing small data challenges in modeling environmental chemicals.

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6

Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

In silico simulation of benzo[a]pyrene toxicity in the worm Caenorhabditiselegans

This study aimed to develop a toxicological screening tool using a virtual (in silico) population of Caenorhabditis elegans exposed to different concentrations of benzo[a]pyrene (BAP). The model used computational tools based on a previous study to simulate the life cycle and characteristics of C. elegans. The model was implemented in Python and adapted with fewer repetitions of simulations to reduce execution time. The toxicity function was based on in vivo data from previous studies, and the results of the model were compared with experimental results. The model showed good accuracy in reproducing the survival data of worms exposed to BAP since the lethal concentration for 50% (LC50) and the 95% confidence interval of exposed worms during 72 h was 77.92 μg/L (71.32-85.12 μg/L). The LC50 of the simulated data was 87.10 μg/L (76.13-99.85 μg/L). It was concluded that the in silico model can be a useful alternative to conventional in vivo testing methods, saving cost and time and addressing ethical concerns.

Enhanced biodegradation of endocrine disruptor bisphenol A by food waste composting without bioaugmentation: Analysis of bacterial communities and their relative abundances

Composting with food waste was assessed for its efficacy in decontaminating Bisphenol A (BPA). In a BPA-treated compost pile, the initial concentration of BPA 847 mg kg-1 fell to 6.3 mg kg-1 (99% reduction) over a 45-day composting period. The biodegradation rate was at its highest when bacterial activity peaked in the mesophilic and thermophilic phases. The average rate of total biodegradation was 18.68 mg kg-1 day-1. Standard methods were used to assess physicochemical parameters of the compost matrix and gas chromatography combined with mass spectrometry (GC/MS) was used to identify BPA intermediates. Next-generation sequencing (NGS) was used to detect BPA degraders and the diverse bacterial communities involved in BPA decomposition. These communities were found consist of 12 phyla and 21 genera during the composting process and were most diversified during the maturation phase. Three dominant phyla, Firmicutes, Pseudomonadota, and Bacteroidetes, along with Lactobacillus, Proteus, Bacillus, and Pseudomonas were found to be the most responsible for BPA degradation. Different bacterial communities were found to be involved in the food waste compost biodegradation of BPA at different stages of the composting process. In conclusion, food waste composting can effectively remove BPA, resulting in a safe product. These findings might be used to expand bioremediation technologies to apply to a wide range of pollutants.

Evaluation of a high-throughput H295R homogenous time resolved fluorescence assay for androgen and estrogen steroidogenesis screening

The H295R test guideline assay evaluates the effect of test substances on synthesis of 17β-estradiol (E2) and testosterone (T). The objective of this study was to leverage commercial immunoassay technology to develop a more efficient H295R assay to measure E2 and T levels in 384-well format. The resulting Homogenous Time Resolved Fluorescence assay platform (H295R-HTRF) was evaluated against a training set of 36 chemicals derived from the OECD inter-laboratory validation study, EPA guideline 890.1200 aromatase assay, and azole fungicides active in the HT-H295R assay. Quality control performance criteria were met for all conditions except E2 synthesis inhibition where low basal hormone synthesis was observed. Five proficiency chemicals were active for both the E2 and T endpoints, consistent with guideline classifications. Of the nine OECD core reference chemicals, 9/9 were concordant with outcomes for E2 and 7/9 for T. Likewise, 9/13 and 11/13 OECD supplemental chemicals were concordant with anticipated effects for E2 and T, respectively. Of the 10 azole fungicides screened, 7/10 for E2 and 8/10 for T exhibited concordant outcomes for inhibition. Generally, all active chemicals in the training set demonstrated equivalent or greater potency in the H295R-HTRF assay, supporting the sensitivity of the platform. The adaptation of HTRF technology to the H295R model provides an efficient way to evaluate E2 and T modulators in accordance with guideline specifications.

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn't raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

Lignin-modifying enzymes (LMEs) are impactful biocatalysts in environmental remediation applications. However, LMEs-assisted experimental degradation neglects the molecular basis of pollutant degradation. Furthermore, throughout the remediation process, the inherent hazards of environmental pollutants remain untapped for in-depth toxicological endpoints. In this investigation, a predictive toxicological framework and a computational framework adopting LMEs were employed to assess the hazards of Priority Pollutants (PP) and its possible LMEs-assisted catalytic screening. The potential hazardous outcomes of PP were assessed using Quantitative structure-activity relationship (QSARs)-based techniques including Toxtree, ECOSAR, and T.E.S.T. tools. Toxicological findings revealed positive outcomes in a multitude of endpoints for all PP. The PP compound 2,3,7,8-TCDD (dioxin) was found to exhibit the lowest concentration of aquatic toxicity implementing aquatic model systems; LC₅₀ as 0.01, 0.01, 0.04 (mg L^-1) for Fish (96 H), Daphnid (48 H), Green algae (96 H) respectively. T.E.S.T. results revealed that chloroform, and 2-chlorophenol both seem to be developmental toxicants. Subsequently, LMEs-assisted docking procedure was employed in predictive mitigation of PP. The docking approach as predicted degradation revealed the far lowest docking energy score for Versatile peroxidase (VP)- 2,3,7,8-TCDD docked complex with a binding energy of -9.2 (kcal mol^-1), involved PHE-46, PRO-139, PRO-141, ILE-148, LEU-165, HIS-169, LEU-228, MET-262, and MET-265 as key interacting amino acid residues. Second most ranked but lesser than VP, Lignin peroxidase (LiP)- 2,3,7,8-TCDD docked complex exhibited a rather lower binding affinity score (-8.8 kcal mol^-1). Predictive degradation screening employing comparative docking revealed varying binding affinities, portraying that each LMEs member has independent feasibility to bind PP as substrate. Predictive findings endorsed the hazardous nature of associated PP in a multitude of endpoints, which could be attenuated by undertaking LMEs as a predictive approach to protect the environment and implement it in regulatory considerations.

Adverse Outcome Pathway 'Footprinting': A Novel Approach to the Integration of 21st Century Toxicology Information into Chemical Mixtures Risk Assessment

For over a decade, New Approach Methodologies (NAMs) such as structure-activity/read-across, -omics technologies, and Adverse Outcome Pathway (AOP), have been considered within regulatory communities as alternative sources of chemical and biological information potentially relevant to human health risk assessment. Integration of NAMs into applications such as chemical mixtures risk assessment has been limited due to the lack of validation of qualitative and quantitative application to adverse health outcomes in vivo, and acceptance by risk assessors. However, leveraging existent hazard and dose-response information, including NAM-based data, for mixture component chemicals across one or more levels of biological organization using novel approaches such as AOP 'footprinting' proposed herein, may significantly advance mixtures risk assessment. AOP footprinting entails the systematic stepwise profiling and comparison of all known or suspected AOPs involved in a toxicological effect at the level of key event (KE). The goal is to identify key event(s) most proximal to an adverse outcome within each AOP suspected of contributing to a given health outcome at which similarity between mixture chemicals can be confidently determined. These key events are identified as the 'footprint' for a given AOP. This work presents the general concept, and a hypothetical example application, of AOP footprinting as a key methodology for the integration of NAM data into mixtures risk assessment.

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.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Pluripotent Stem Cell Assays: Modalities and Applications For Predictive Developmental Toxicity

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A systematic scoping review of the literature evaluated the embryonic stem cell test (EST).

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1533 publications included 18 publications testing 10 or more compounds in human or mouse EST.

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Selected case examples included 5-fluorouracil, thalidomide, and caffeine.

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Applicability, limitations, and recommendations for further work are discussed.

This manuscript provides a review focused on embryonic stem cell-based models and their place within the landscape of alternative developmental toxicity assays. Against the background of the principles of developmental toxicology, the wide diversity of alternative methods using pluripotent stem cells developed in this area over the past half century is reviewed. In order to provide an overview of available models, a systematic scoping review was conducted following a published protocol with inclusion criteria, which were applied to select the assays. Critical aspects including biological domain, readout endpoint, availability of standardized protocols, chemical domain, reproducibility and predictive power of each assay are described in detail, in order to review the applicability and limitations of the platform in general and progress moving forward to implementation. The horizon of innovative routes of promoting regulatory implementation of alternative methods is scanned, and recommendations for further work are given.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Overall lack of genotoxic activity among five common low- and no-calorie sweeteners: A contemporary review of the collective evidence

Low- and no-calorie sweeteners (LNCS) are food additives that have been widely consumed for many decades. Their safety has been well established by authoritative bodies globally and is re-evaluated periodically. The objective herein was to survey and summarize the genotoxicity potential of five commonly utilized LNCS: acesulfame potassium (Ace-K), aspartame, saccharin, steviol glycosides and sucralose. Data from peer-reviewed literature and the ToxCast/Tox21 database were evaluated and integrated with the most recent weight-of-evidence evaluations from authoritative sources. Emphasis was placed on assays most frequently considered for hazard identification and risk assessment: mutation, clastogenicity and/or aneugenicity, and indirect DNA damage, such as changes in DNA repair mechanisms or gene expression data. These five sweeteners have been collectively evaluated in hundreds of in vivo or in vitro studies that employ numerous testing models, many of which have been conducted according to specific testing guidelines. The weight-of-evidence demonstrates overall negative findings across assay types for each sweetener when considering the totality of study design, reliability and reporting quality, as well as the lack of carcinogenic responses (or lack of responses relevant to humans) in animal cancer bioassays as well as observational studies in humans. This conclusion is consistent with the opinions of authoritative sources that have consistently determined that these sweeteners lack mutagenic and genotoxic potential.

SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens

Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

A mechanistic effect modeling approach to the prioritization of hidden drivers in chemical cocktails

Exposure to a single chemical does not exist in reality. Mixtures, which are the ecological norm, are often characterized by numerous intrinsic driving factors with unknown combined effects. Interactions between heterogeneous chemicals, or chemical and nonchemical stressors, could alter their toxicity traits relative to single exposure. Hence, revealing the hidden environmental effects affecting multiple stressor interactions is essential to expand our knowledge about uncertainty sources in chemical risk-based decision contexts. Global sensitivity analysis (GSA) techniques involving Morris method sampling and elementary effects (EE) sensitivity analysis was applied to investigate the driving factors underlying the combined effects on Scenedesmus obliquus, and identify the mode of interaction in mixtures at environmentally-relevant concentrations. One hundred mixed-exposure formulas were generated with 9 variables (8 chemicals and temperature) via the Morris method, representing environmental perspective in the field. Subsequently, EE sensitivity analysis combined with quantitative high-throughput screening (q-HTS) was adopted to identify the most critical mixture and its primary drivers. Combined exposure exerted significantly increased effects on S. obliquus compared to the effects of individual exposure. The critical drivers were identified and validated by the control variate method. For the mode of combined action, mixture toxicity did not match the additivity relationship, and a strong interaction existed among chemicals. Collectively, the data provides evidence that a combination of specific pesticides and emerging brominated flame retardants can produce comparable, or even stronger, bionegative effects than pure chemicals due to complicated interactions. The method used offers direct comparison of multifarious factors in a unified standard scale, bridges the actual interaction scenarios in the field to toxicity simulations in the laboratory, and fill a gap in ecotoxicology.

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.

Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training

Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure-activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.

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.

In silico prediction of toxicity and its applications for chemicals at work

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

Lack of potential carcinogenicity for sucralose - Systematic evaluation and integration of mechanistic data into the totality of the evidence

Sucralose is widely used as a sugar substitute. Many studies and authoritative reviews have concluded that sucralose is non-carcinogenic, based primarily on animal cancer bioassays and genotoxicity data. To add to the body of knowledge on the potential carcinogenicity of sucralose, a systematic assessment of mechanistic data was conducted. This entailed using a framework developed for the quantitative integration of data related to the proposed key characteristics of carcinogens (KCCs). Data from peer-reviewed literature and the ToxCast/Tox21 database were evaluated using an algorithm that weights data for quality and relevance. The resulting integration demonstrated an overall lack of activity for sucralose across the KCCs, with no "strong" activity observed for any KCC. Almost all data collected demonstrated inactivity, including those conducted in human models. The overall lack of activity in mechanistic data is consistent with findings from animal cancer bioassays. The few instances of activity across the KCC were generally accompanied by limitations in study design in the context of either quality and/or dose and model relevance, highlighted upon integration of the totality of the evidence. The findings from this comprehensive and integrative evaluation of mechanistic data support prior conclusions that sucralose is unlikely to be carcinogenic in humans.

Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals

Membrane/water partition coefficient (K_m/w) is a vital parameter used to characterize the membrane permeability of compounds. Considering the K_m/w value is difficult to observe experimentally for real biological membranes, liposome/water partition coefficient (K_lip/w) is employed to approximate K_m/w. Here, quantitative structure property relationship (QSPR) models for logK_lip/w of the neutral organic chemicals and the neutral form of ionogenic organic chemicals (IOCs) (logK_{lip/w-neutral}), ionic form of IOCs (logK_lip/w-ionic), the speciation-corrected liposome-water distribution ratios at a pH = 7.40 (logD_{lip/w-(pH=7.40)}) were developed. In the modeling, two modeling methods (multiple linear regressions (MLR) and k-nearest neighbor (kNN)) were used. The predictive variables employed here could be calculated from the molecular structure directly. For logK_{lip/w-neutral} and logD_{lip/w-(pH=7.40)}, the logK_OW and logD_OW-based, non-logK_OW and non-logD_OW-based kNN-QSPR and MLR-QSPR models were developed, respectively. The evaluation results implied that the predictive performance of kNN-QSPR models is better than that of MLR-QSPR models. For logK_lip/w-ionic, only one acceptable MLR-QSPR model was developed for cation and anion, respectively. The model quality of the derived models was evaluated following the OECD QSPR models validation guideline. The determination coefficient (R²), leave-one-out cross validation Q² (Q²_LOO) and bootstrapping coefficient (Q²_BOOT), the external validation coefficient (Q²_EXT) of all the models met the acceptable criteria (Q² > 0.600, R² > 0.700); while the root-mean-square error (RMSE) range from 0.351 to 0.857. All the results implied that the models had good goodness-of-fit, robustness and predictive ability. Therefore, the developed models could be used to fill the data gap for substances within the applicability domain on their missing logK_{lip/w-neutral}, logK_lip/w-ionic, logD_{lip/w-(pH=7.40)} values.

Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human Transthyretin Disruptors: An in Vitro and in Silico Study

Phenolic disinfection byproducts (phenolic-DBPs) have been identified in recent years. However, the toxicity data for phenolic-DBPs are scarce, hampering their risk assessment and the development of regulations on the acceptable concentration of phenolic-DBPs in water. In this study, the binding potency and underlying interaction mechanism between human transthyretin (hTTR) and five groups of representative phenolic-DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids) were determined and probed by competitive fluorescence displacement assay integrated with in silico methods. Experimental results implied that 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, and 3,5-dihalo-4-hydroxybenzaldehydes have a high binding affinity with hTTR. The hTTR binding potency of the chemicals with electron-withdrawing groups on their molecular structures were higher than that with electron-donor groups. Molecular modeling methods were used to decipher the binding mechanism between model compounds and hTTR. The results documented that ionic pair, hydrogen bonding and hydrophobic interactions were dominant interactions. Finally, a mechanism-based model for predicting the hTTR binding affinity was developed. The determination coefficient ( R²), leave-one-out cross validation Q² ( Q_LOO²), bootstrapping coefficient ( Q_BOOT²), external validation coefficient ( Q_EXT²) and concordance correlation coefficient ( CCC) of the developed model met the acceptable criteria ( Q² > 0.600, R² > 0.700, CCC > 0.850), implying that the model had good goodness-of-fit, robustness, and external prediction performances. All the results indicated that the phenolic-DBPs have the hTTR disrupting effects, and further studies are needed to investigate their other mechanism of endocrine disruption.

Analysis of Sublethal Toxicity in Developing Zebrafish Embryos Exposed to a Range of Petroleum Substances

The Organisation for Economic Co-operation and Development (OECD) test guideline 236 (fish embryo acute toxicity test; 2013) relies on 4 endpoints to describe exposure-related effects (coagulation, lack of somite formation, tail-bud detachment from the yolk sac, and the presence of a heartbeat). Danio rerio (zebrafish) embryos were used to investigate these endpoints along with a number of additional sublethal effects (cardiac dysfunction, pericardial edema, yolk sac edema, tail curvature, hatch success, pericardial edema area, craniofacial malformation, swim bladder development, fin development, and heart rate) following 5-d exposures to 7 petroleum substances. The substances investigated included 2 crude oils, 3 gas oils, a diluted bitumen, and a petrochemical containing a mixture of branched alcohols. Biomimetic extraction-solid-phase microextraction (BE-SPME) was used to quantify freely dissolved concentrations of test substances as the exposure metric. The results indicated that the most prevalent effects observed were pericardial and yolk sac edema, tail curvature, and lack of embryo viability. A BE-SPME threshold was determined to characterize sublethal morphological alterations that preceded embryo mortality. Our results aid in the understanding of aquatic hazards of petroleum substances to developing zebrafish beyond traditional OECD test guideline 236 endpoints and show the applicability of BE-SPME as a simple analytical tool that can be used to predict sublethal embryo toxicity. Environ Toxicol Chem 2019;38:1302-1312. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

A review on machine learning methods for in silico toxicity prediction

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

Food Safety Analysis of Milk and Beef in Southwestern Uganda

Background

Inorganic pollutants in milk and beef are of major public health concern; however, information in Africa is still limited due to low food safety monitoring practices. In this study, we established levels of lead (Pb), zinc (Zn), cadmium (Cd), copper (Cu), and iron (Fe) in milk and beef and obtained the estimated daily intake (EDI) and incremental lifetime cancer risk (ILCR) as measures of risk to the Ugandan population.

Materials and Methods

This was a cross-sectional study in which a total of 40 samples of milk and beef were collected from Bushenyi district in southwestern Uganda. Samples were analyzed by atomic absorbance spectrophotometer, and the EDI and ILCR were computed using the US EPA reference values.

Results and Discussion

Heavy metal concentrations were highest in the order of Zn > Fe > Pb > Cu in milk samples, while in beef samples, concentrations were highest in the order of Zn > Pb > Fe > Cu and no Cd was detected. Furthermore, beef had significantly higher (P < 0.05) Pb and Fe concentrations than milk. The EDI was highest in children, and this was followed by very high ILCR levels, showing that milk and beef are not safe for children in Uganda. Bearing in mind that a high HI was shown, beef and milk from these regions are not recommended for consumption especially by children although more studies remain to be conducted.

Conclusion

Heavy metals in milk and beef of Uganda may predispose the indigenous community to cancer and other health-related illnesses, showing a need for improved food safety screening to promote food safety.

Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach

The development and application of high throughput in vitro assays is an important development for risk assessment in the twenty-first century. However, there are still significant challenges to incorporate in vitro assays into routine toxicity testing practices. In this paper, a robust learning approach was developed to infer the in vivo point of departure (POD) with in vitro assay data from ToxCast and Tox21 projects. Assay data from ToxCast and Tox21 projects were utilized to derive the in vitro PODs for several hundred chemicals. These were combined with in vivo PODs from ToxRefDB regarding the rat and mouse liver to build a high-dimensional robust regression model. This approach separates the chemicals into a majority, well-predicted set; and a minority, outlier set. Salient relationships can then be learned from the data. For both mouse and rat liver PODs, over 93% of chemicals have inferred values from in vitro PODs that are within ± 1 of the in vivo PODs on the log₁₀ scale (the target learning region, or TLR) and R² of 0.80 (rats) and 0.78 (mice) for these chemicals. This is comparable with extrapolation between related species (mouse and rat), which has 93% chemicals within the TLR and the R² being 0.78. Chemicals in the outlier set tend to also have more biologically variable characteristics. With the continued accumulation of high throughput data for a wide range of chemicals, predictive modeling can provide a valuable complement for adverse outcome pathway based approach in risk assessment.

Ecotoxicology Goes on a Chip: Embracing Miniaturized Bioanalysis in Aquatic Risk Assessment

Biological and environmental sciences are, more than ever, becoming highly dependent on technological and multidisciplinary approaches that warrant advanced analytical capabilities. Microfluidic lab-on-a-chip technologies are perhaps one the most groundbreaking offshoots of bioengineering, enabling design of an entirely new generation of bioanalytical instrumentation. They represent a unique approach to combine microscale engineering and physics with specific biological questions, providing technological advances that allow for fundamentally new capabilities in the spatiotemporal analysis of molecules, cells, tissues, and even small metazoan organisms. While these miniaturized analytical technologies experience an explosive growth worldwide, with a substantial promise of a direct impact on biosciences, it seems that lab-on-a-chip systems have so far escaped the attention of aquatic ecotoxicologists. In this Critical Review, potential applications of the currently existing and emerging chip-based technologies for aquatic ecotoxicology and water quality monitoring are highlighted. We also offer suggestions on how aquatic ecotoxicology can benefit from adoption of microfluidic lab-on-a-chip devices for accelerated bioanalysis.

Molecular Docking for Predictive Toxicology

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.

Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure

Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performance was assessed based on F1 scores using 5-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%), and these gains were correlated (ρ = 0.92) with the number of chemicals. Overall, the results demonstrate that a combination of bioactivity and chemical descriptors can accurately predict a range of target organ toxicity outcomes in repeat-dose studies, but specific experimental and methodologic improvements may increase predictivity.

Systems Toxicology: Real World Applications and Opportunities

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the federal Tox21 research program, can generate biological data to inform models for predicting potential toxicity. However, physicochemical properties are also needed to model environmental fate and transport, as well as exposure potential. The purpose of the present study was to generate an open-source quantitative structure-property relationship (QSPR) workflow to predict a variety of physicochemical properties that would have cross-platform compatibility to integrate into existing cheminformatics workflows. In this effort, decades-old experimental property data sets available within the EPA EPI Suite were reanalyzed using modern cheminformatics workflows to develop updated QSPR models capable of supplying computationally efficient, open, and transparent HTS property predictions in support of environmental modeling efforts. Models were built using updated EPI Suite data sets for the prediction of six physicochemical properties: octanol-water partition coefficient (logP), water solubility (logS), boiling point (BP), melting point (MP), vapor pressure (logVP), and bioconcentration factor (logBCF). The coefficient of determination (R2) between the estimated values and experimental data for the six predicted properties ranged from 0.826 (MP) to 0.965 (BP), with model performance for five of the six properties exceeding those from the original EPI Suite models. The newly derived models can be employed for rapid estimation of physicochemical properties within an open-source HTS workflow to inform fate and toxicity prediction models of environmental chemicals.

Toxicology of metals and metalloids: Promising issues for future studies in environmental health and toxicology

The function and behavior of chemical elements in ecosystems and in human health probably comprise one of the most studied issues and a theme of great interest and fascination in science. Hot topics are emerging on an annual basis in this field. Bearing this in mind, some promising themes to explore in the field of metals and metalloids in the environment and in toxicology are highlighted and briefly discussed herein.

Identification of time-dependent biomarkers and effects of exposure to volatile organic compounds using high-throughput analysis

Volatile organic compounds (VOCs) can be easily taken up by humans, leading to various diseases, such as respiratory system and central nervous system disorders. Environmental risk assessment is generally conducted using traditional tests, which may be time-consuming and technically challenging. Therefore, analysis of the effects of VOCs, such as toluene, ethylbenzene, and xylene, may be improved by use of novel, high-throughput methods, such as microarray analysis. In this study, we examined the effects of VOCs exposure in humans on gene expression and methylation using microarray analysis. We recruited participants who had short-term exposure, long-term exposure, or no exposure. We then analyzed changes in gene expression in blood samples from these participants. A total of 866 genes were upregulated, while 366 genes were downregulated in the short-term exposure group. Similarly, in the long-term exposure group, a total of 852 and 480 genes were up- or downregulated, respectively. Hierarchical clustering analysis was used to divide the clustered genes into nine clusters to investigate the expression of variations in accordance with the exposure period. And the methylation microarray was performed at the same time to see whether this expression variation is related to the epigenetic study. Finally, we have 5 genes that were upregulated and 12 genes that were downregulated, gradually and respectively, so these genes are expected to function as biomarkers of the duration of exposure to VOCs. Further research is required to determine the time-dependent effects of VOCs on epigenetic regulation of gene expression. © 2015 Wiley Periodicals, Inc. Environ Toxicol 31: 1563-1570, 2016.

In vitro toxicity testing of cigarette smoke based on the air-liquid interface exposure: A review

Cigarette smoke is a complex aerosol comprising particulate phase and gaseous vapour phase. The air-liquid interface exposure provides a possible technical means to implement whole smoke exposure for the assessment of tobacco products. In this review, the research progress in the in vitro toxicity testing of cigarette smoke based on the air-liquid interface exposure is summarized. The contents presented involve mainly cytotoxicity, genotoxicity, oxidative stress, inflammation, systems toxicology, 3D culture and cigarette smoke dosimetry related to cigarette smoke, as well as the assessment of electronic cigarette aerosol. Prospect of the application of the air-liquid interface exposure method in assessing the biological effects of tobacco smoke is discussed.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND

Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program.

OBJECTIVES

We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.

METHODS

CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies.

RESULTS

Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.

CONCLUSION

This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.

CITATION

Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016.

CERAPP

Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.

Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment

BACKGROUND

Computational exposure science represents a frontier of environmental science that is emerging and quickly evolving.

OBJECTIVES

In this commentary, we define this burgeoning discipline, describe a framework for implementation, and review some key ongoing research elements that are advancing the science with respect to exposure to chemicals in consumer products.

DISCUSSION

The fundamental elements of computational exposure science include the development of reliable, computationally efficient predictive exposure models; the identification, acquisition, and application of data to support and evaluate these models; and generation of improved methods for extrapolating across chemicals. We describe our efforts in each of these areas and provide examples that demonstrate both progress and potential.

CONCLUSIONS

Computational exposure science, linked with comparable efforts in toxicology, is ushering in a new era of risk assessment that greatly expands our ability to evaluate chemical safety and sustainability and to protect public health.

CITATION

Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, Judson RS, Buckley TJ. 2016. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect 124:697-702; http://dx.doi.org/10.1289/ehp.1509748.

Machine learning algorithms for mode-of-action classification in toxicity assessment

Background

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances.

Results

In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters.

Conclusions

Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0098-0) contains supplementary material, which is available to authorized users.

ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model

The ToxCast EPA challenge was managed by TopCoder in Spring 2014. The goal of the challenge was to develop a model to predict the lowest effect level (LEL) concentration based on in vitro measurements and calculated in silico descriptors. This article summarizes the computational steps used to develop the Rank-I model, which calculated the lowest prediction error for the secret test data set of the challenge. The model was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM), and it is freely available at http://ochem.eu/article/68104. Surprisingly, this model does not use any in vitro measurements. The logic of the decision steps used to develop the model and the reason to skip inclusion of in vitro measurements is described. We also show that inclusion of in vitro assays would not improve the accuracy of the model.