Identifying and designing chemicals with minimal acute aquatic toxicity

Isothiazolinone Disrupts Reproductive Endocrinology by Targeting the G-Protein-Coupled Receptor Signaling

The unintended exposure of humans and animals to isothiazolinones has led to an increasing concern regarding their health hazards. Isothiazolinones were previously found to disrupt reproductive endocrine homeostasis. However, the long-term reproductive toxicity and underlying mechanism remain unclear. In this study, life-cycle exposure of medaka to dichlorocthylisothiazolinone (DCOIT), a representative isothiazolinone, significantly stimulated the gonadotropin releasing hormone receptor (GnRHR)-mediated synthesis of follicle stimulating hormone and luteinizing hormone in the brain. Chem-Seq and proteome analyses revealed disturbances in the G-protein-coupled receptor, MAPK, and Ca2+ signaling cascades by DCOIT. The G protein αi subunit was identified as the binding target of DCOIT. Gαi bound by DCOIT had an enhanced affinity for the mitochondrial calcium uniporter, consequently changing Ca2+ subcellular compartmentalization. Stimulation of Ca2+ release from the endoplasmic reticulum and blockage of Ca2+ uptake into the mitochondria resulted in a considerably higher cytoplasmic Ca2+ concentration, which then activated the phosphorylation of MEK and ERK to dysregulate hormone synthesis. Overall, by comprehensively integrating in vivo, ex vivo, in silico, and in vitro evidence, this study proposes a new mode of endocrine disrupting toxicity based on isothiazolinones, which is expected to aid the risk assessment of the chemical library and favor the mechanism-driven design of safer alternatives.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Toward Property-Based Regulation

An expanding web of adverse impacts on people and the environment has been steadily linked to anthropogenic chemicals and their proliferation. Central to this web are the regulatory structures intended to protect human and environmental health through the control of new molecules. Through chronically insufficient and inefficient action, the current chemical-by-chemical regulatory approach, which considers regulation at the level of chemical identity, has enabled many adverse impacts to develop and persist. Recognizing the link between fundamental physicochemical properties and hazards, we describe a new paradigm─property-based regulation. By regulating physicochemical properties, we show how governments can delineate and enforce safe chemical spaces, increasing the scalability of chemical assessments, reducing the time and resources to regulate a substance, and providing transparency for chemical designers. We highlight sparse existing property-based approaches and demonstrate their applicability using bioaccumulation as an example. Finally, we present a path to implementation in the United States, prescribing roles and steps for government, nongovernmental organizations, and industry to accelerate this transition, to the benefit of all.

APPLICATION OF MICELLAR LIQUID CHROMATOGRAPHY TO MODEL ECOTOXICITY OF PESTICIDES. COMPARISON WITH IMMOBILIZED ARTIFICIAL MEMBRANE CHROMATOGRAPHY AND N-OCTANOL-WATER PARTITIONING

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC₅₀/ EC₅₀ values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

Identification of neurotoxic compounds in cyanobacteria exudate mixtures

Release of toxic cyanobacterial secondary metabolites threatens biosecurity, foodwebs and public health. Microcystis aeruginosa (Ma), the dominant species in global freshwater cyanobacterial blooms, produces exudates (MaE) that cause adverse outcomes including nerve damage. Previously, we identified > 300 chemicals in MaE. It is critical to investigate neurotoxicity mechanisms of active substances among this suite of Ma compounds. Here, we screened 103 neurotoxicity assays from the ToxCast database to reveal targets of action of MaE using machine learning. We then built a potential Adverse Outcome Pathway (AOP) to identify neurotoxicity mechanisms of MaE as well as key targets. Finally, we selected potential neurotoxins matched with those targets using molecular docking. We found 38 targets that were inhibited and eight targets that were activated, collectively mainly related to neurotransmission (i.e. cholinergic, dopaminergic and serotonergic neurotransmitter systems). The potential AOP of MaE neurotoxicity could be caused by blocking calcium voltage-gated channel (CACNA1A), because of antagonizing neurotransmitter receptors, or because of inhibiting solute carrier transporters. We identified nine neurotoxic MaE compounds with high affinity to those targets, including LysoPC(16:0), 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine, egonol glucoside, polyoxyethylene (600) monoricinoleate, and phytosphingosine. Our study enhances understanding of neurotoxicity mechanisms and identifies neurotoxins in cyanobacterial bloom exudates, which may help identify priority compounds for cyanobacteria management.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Structure-to-process design framework for developing safer pesticides

Rational design of pesticides with tunable degradation properties and minimal ecotoxicity is among the grand challenges of green chemistry. While computational approaches have gained traction in predictive toxicology, current methods lack the necessary multifaceted approach and design-vectoring tools needed for system-based chemical development. Here, we report a tiered computational framework, which integrates kinetics and thermodynamics of indirect photodegradation with predictions of ecotoxicity and performance, based on cutoff values in mechanistically derived physicochemical properties and electronic parameters. Extensively validated against experimental data and applied to 700 pesticides on the U.S. Environmental Protection Agency's registry, our simple yet powerful approach can be used to screen existing molecules to identify application-ready candidates with desirable characteristics. By linking structural attributes to process-based outcomes and by quantifying trade-offs in safety, depletion, and performance, our method offers a user-friendly roadmap to rational design of novel pesticides.

Carbon Nanotubes: Current Perspectives on Diverse Applications in Targeted Drug Delivery and Therapies

Current discoveries as well as research findings on various types of carbon nanostructures have inspired research into their utilization in a number of fields. These carbon nanostructures offer uses in pharmacy, medicine and different therapies. One such unique carbon nanostructure includes carbon nanotubes (CNTs), which are one-dimensional allotropes of carbon nanostructure that can have a length-to-diameter ratio greater than 1,000,000. After their discovery, CNTs have drawn extensive research attention due to their excellent material properties. Their physical, chemical and electronic properties are excellent and their composites provide great possibilities for enormous nanometer applications. The current study provides a systematic review based on prior literature review and data gathered from various sources. The various research studies from many research labs and organizations were systematically retrieved, collected, compiled and written. The entire collection and compilation of this review concluded the use of CNT approaches and their efficacy and safety for the treatment of various diseases such as brain tumors or cancer via nanotechnology-based drug delivery, phototherapy, gene therapy, antiviral therapy, antifungal therapy, antibacterial therapy and other biomedical applications. The current review covers diverse applications of CNTs in designing a range of targeted drug delivery systems and application for various therapies. It concludes with a discussion on how CNTs based medicines can expand in the future.

Structure-Energetics-Property Relationships Support Computational Design of Photodegradable Pesticides

Development of high-performing pesticides with tunable degradation properties is vital to increasing the safety and effectiveness of tomorrow's analogs. Chromophoric dissolved organic matter in the excited triple state (³CDOM*) is known to play a key role in the removal of pesticides via indirect photodegradation. However, the potential of these transformations to guide the design of safer chemicals has not yet been fully realized. Here, we report a two-tier computational framework developed to probe and predict both kinetics and thermodynamics of ³CDOM*-pesticide interactions. In the first tier, robust in silico models were constructed by fitting free energies obtained from density functional theory (DFT) calculations to cell potentials and second-order rate constants for the ³CDOM*-pesticide electron transfer. In the second tier, Gibbs free energies and corresponding free energy barriers, determined in solution using the Marcus theory, were applied to develop a quick yet accurate screening approach based on the frontier molecular orbital (FMO) Theory. Being highly mechanistic and spanning ca. 1500 unique ³CDOM*-pesticide interactions, our approach is both robust and broadly applicable. To that end, the outcomes of our computational models were integrated into an easy-to-use decision framework that can guide structure-based design of less persistent pesticide analogs.

Hydrophobic modifications of hydroxyethyl cellulose polymers: Their influence on the acute toxicity to aquatic biota

The hydrophobic substitution (HS) of cationic cellulose derivatives may be tuned, promoting their efficiency. This work studied the influence of HS on the acute ecotoxicity of quaternized hydroxyethyl cellulose polymers (SL) to aquatic biota. The ecotoxicity of four SL with different HS (SL-5, SL-30, SL-60, SL-100) was assessed for seven species: Vibrio fischeri, Raphidocelis subcapitata, Chlorella vulgaris, Daphnia magna, Brachionus calyciflorus, Heterocypris incongruens, and Danio rerio. The computed median effective concentrations were used to derive hazard concentrations, by using species sensitive distribution curves. All SL suspensions were characterized for particle size, zeta potential and rheological properties. Results indicated instability of the SL in suspension due to their relatively low zeta potential. Raphidocelis subcapitata, C. vulgaris and B. calyciflorus were the most sensitive to the four SL, suggesting that exposure to these compounds may imbalance the lowest trophic levels. Also, HS influenced the toxicity of SL, with the lowest HS (SL-5) revealing lower ecotoxicity. The maximum acceptable concentrations were 14.0, 2.9, 3.9 and 1.4 mg L^-1 for SL-5, SL-30, SL-60, and SL-100, respectively. Accordingly, SL-5 is suggested as the eco-friendliest and is recommended to be used in the production of care products, in detriment of the other three tested variants.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Chemical pollution imposes limitations to the ecological status of European surface waters

Aquatic ecosystems are affected by man-made pressures, often causing combined impacts. The analysis of the impacts of chemical pollution is however commonly separate from that of other pressures and their impacts. This evolved from differences in the data available for applied ecology vis-à-vis applied ecotoxicology, which are field gradients and laboratory toxicity tests, respectively. With this study, we demonstrate that the current approach of chemical impact assessment, consisting of comparing measured concentrations to protective environmental quality standards for individual chemicals, is not optimal. In reply, and preparing for a method that would enable the comprehensive assessment and management of water quality pressures, we evaluate various quantitative chemical pollution pressure metrics for mixtures of chemicals in a case study with 24 priority substances of Europe-wide concern. We demonstrate why current methods are sub-optimal for water quality management prioritization and that chemical pollution currently imposes limitations to the ecological status of European surface waters. We discuss why management efforts may currently fail to restore a good ecological status, given that to date only 0.2% of the compounds in trade are considered in European water quality assessment and management.

Going All In: A Strategic Investment in In Silico Toxicology

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.

Is Green Chemistry a feasible tool for the implementation of a circular economy?

The main goal of this research is to evaluate the contributions of Green Chemistry as a potential tool to drive the transition to circularity. For this, we have carried out a bibliographic study, analyzing those documents, process, or experiences that dealt jointly with the Green Chemistry aspects related to circularity such circular economy, industrial ecology, and closed loop. Findings show that few authors have treated that disciplines together in the last 10 years. Based on an analysis of academic literature, common strategies (design, raw materials, life cycle assessment, processes, normative, new business, and collaboration), specific experiences (catalyst, biobased products or methods, recycling, and reusing), and difficulties to overcome (metrics, transdisciplinary research, unawareness, and competitiveness) have been identified. Finally, different kind of measures, as behind such joint metrics, informal open spaces, closer the industry, education, standards and label are proposed to facilitate the development of Green Chemistry, circular economy, industrial ecology, and closed loop with the ultimate goal of improving sustainable development.From the evidences found, we finally conclude that it is possible to use Green Chemistry and its principles as a tool to drive the transition to circularity, being the development of open spaces for exchange information between different actors from academia, governments and regulatory actors, business and industrial sectors, with the aim of promoting disruptive advances in sustainability.

Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

In life cycle assessment, characterization factors are used to convert the amount of the chemicals and other pollutants generated in a product's life cycle to the standard unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chemicals due to the lack of laboratory experiment data. Here we develop machine learning models to estimate ecotoxicity hazardous concentrations 50% (HC₅₀) in USEtox to calculate characterization factors for chemicals based on their physical-chemical properties in EPA's CompTox Chemical Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The average root mean squared error of the estimated HC₅₀ on the test sets is 0.761. The average coefficient of determination (R²) on the test set is 0.630, meaning 63% of the variability of HC₅₀ in USEtox can be explained by the predicted HC₅₀ from the random forest model. Our model outperforms a traditional quantitative structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide estimates of missing ecotoxicity characterization factors for 552 chemicals in USEtox using the validated random forest model.

Toward Less Hazardous Industrial Compounds: Coupling Quantum Mechanical Computations, Biomarker Responses, and Behavioral Profiles To Identify Bioactivity of SN2 Electrophiles in Alternative Vertebrate Models

Sustainable molecular design of less hazardous chemicals promises to reduce risks to public health and the environment. Computational chemistry modeling coupled with alternative toxicology models (e.g., larval fish) present unique high-throughput opportunities to understand structural characteristics eliciting adverse outcomes. Numerous environmental contaminants with reactive properties can elicit oxidative stress, an important toxicological response associated with diverse adverse outcomes (i.e., cancer, diabetes, neurodegenerative disorders, etc.). We examined a common chemical mechanism (bimolecular nucleophilic substitution (S_N2)) associated with oxidative stress using property-based computational modeling coupled with acute (mortality) and sublethal (glutathione, photomotor behavior) responses in the zebrafish (Danio rerio) and the fathead minnow (Pimephales promelas) models to identify whether relationships exist among biological responses and molecular attributes of industrial chemicals. Following standardized methods, embryonic zebrafish and larval fathead minnows were exposed separately to eight different S_N2 compounds for 96 h. Acute and sublethal responses were compared to computationally derived in silico chemical descriptors. Specifically, frontier molecular orbital energies were significantly related to acute LC₅₀ values and photomotor response (PMR) no observed effect concentrations (NOECs) in both fathead minnow and zebrafish. This reactivity index, LC₅₀ values, and PMR NOECs were also significantly related to whole body glutathione (GSH) levels, suggesting that acute and chronic toxicity results from protein adduct formation for S_N2 electrophiles. Shared refractory locomotor response patterns among study compounds and two alternative vertebrate models appear informative of electrophilic properties associated with oxidative stress for S_N2 chemicals. Electrophilic parameters derived from frontier molecular orbitals were predictive of experimental in vivo acute and sublethal toxicity. These observations provide important implications for identifying and designing less hazardous industrial chemicals with reduced potential to elicit oxidative stress through bimolecular nucleophilic substitution.

Toward Sustainable Environmental Quality: Priority Research Questions for North America

Anticipating, identifying, and prioritizing strategic needs represent essential activities by research organizations. Decided benefits emerge when these pursuits engage globally important environment and health goals, including the United Nations Sustainable Development Goals. To this end, horizon scanning efforts can facilitate identification of specific research needs to address grand challenges. We report and discuss 40 priority research questions following engagement of scientists and engineers in North America. These timely questions identify the importance of stimulating innovation and developing new methods, tools, and concepts in environmental chemistry and toxicology to improve assessment and management of chemical contaminants and other diverse environmental stressors. Grand challenges to achieving sustainable management of the environment are becoming increasingly complex and structured by global megatrends, which collectively challenge existing sustainable environmental quality efforts. Transdisciplinary, systems-based approaches will be required to define and avoid adverse biological effects across temporal and spatial gradients. Similarly, coordinated research activities among organizations within and among countries are necessary to address the priority research needs reported here. Acquiring answers to these 40 research questions will not be trivial, but doing so promises to advance sustainable environmental quality in the 21st century. Environ Toxicol Chem 2019;38:1606-1624. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Immobilized artificial membrane chromatography as a tool for the prediction of ecotoxicity of pesticides

The potential of Immobilized Artificial Membrane (IAM) chromatography to predict ecotoxicological endpoints of pesticides was investigated. For this purpose, retention factors of 39 structurally-diverse pesticides were measured on an IAM stationary phase. A representative test set of 6 pesticides was carefully selected. The training set, involving the remaining pesticides for which experimental data were available, served to establish linear IAM models with LC₅₀/EC₅₀ values in a series of aquatic organisms involving Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values in honey bee, compiled from literature sources. For reasons of comparison, corresponding models were derived by replacing IAM retention factors with octanol-water partition coefficients (logP). Considering the similar regression equations obtained for the 4 fish species, general models to predict toxicity in fish were established. Most models were improved upon inclusion of additional physicochemical parameters. The positive contribution of Molecular Weight to ecotoxicity along with the positive sign of hydrogen bond indices in most cases implies that toxic action is manifested mainly by accumulation on the membrane rather than through diffusion across them. IAM models are generally followed by better statistics and superior predictive performance than those based on experimental or computed logP. Predictions based on IAM chromatography were comparable or even superior with those performed by EPI Suite Software. Hence, IAM retention factors are suggested as promising indices in order to screen or rank chemicals with respect to their ecotoxicological risk, especially in the case of new entities.

Green chemistry & chemical stewardship certificate program: a novel, interdisciplinary approach to green chemistry and environmental health education

The Green Chemistry & Chemical Stewardship Certificate Program was designed using the Community of Inquiry (COI) model as a framework for developing curriculum that engages students across the entire program to meet interdisciplinary, professional development program outcomes. The COI framework allows faculty and course developers to develop courses that consider cognitive, social, and teaching presence as equal components of successful learning experiences. In this program, students focus on systems thinking around green chemistry, business, environmental health, chemical alternative assessment tools, and social and environmental justice. They complete a capstone project that identifies a particular environmental or human health issue associated with a chemical and suggest suitable substitutions that are less harmful but equally effective. This paper describes the program's curriculum, partnerships, delivery modalities, and student feedback as a framework developing professional development opportunities that offer a rich interdisciplinary experience for learners.

Toxicity of nanoparticles_ challenges and opportunities

Nanomaterials (NMs) find widespread use in different industries that range from agriculture, food, medicine, pharmaceuticals, and electronics to cosmetics. It is the exceptional properties of these materials at the nanoscale, which make them successful as growth promoters, drug carriers, catalysts, filters and fillers, but a price must be paid via the potential toxity of these materials. The harmful effects of nanoparticles (NPs) to environment, human and animal health needs to be investigated and critically examined, to find appropriate solutions and lower the risks involved in the manufacture and use of these exotic materials.The vast number and complex interaction of NM/NPs with different biological systems implies that there is no universal toxicity mechanism or assessment method. The various challenges need to be overcome and a number of research studies have been conducted during the past decade on different NMs to explore the possible mechanisms of uptake, concentrations/dosage and toxicity levels. This review article examines critically the recent reports in this field to summarize and present opportunities for safer design using case studies from published literature.

Role of surfactant headgroups on the toxicity of SLEnS-LAS mixed micelles: A case study using microtox test

Mixtures containing ether sulfate based surfactants (SLE_nS) and linear alkylbenzene sulfonic acid (LAS) are relatively common in personal care and household products. When in mixture, they form mixed micelles, which act as reservoirs for the cleaning process. The increase of ethylene oxide (EO) units in the head of SLE_nS lowers the critical micelle concentration, meaning that less quantity of each surfactant is needed to form the micelles. Within an eco-friendly perspective this is advantageous since less chemicals are expected to be released into the environment. But, this advantage will only be effective if variations with the higher number of EO units exhibit a lower toxicity as well. Despite its wide use in commercial products, the ecotoxicity of these micelles and the influence of the EO units on their toxicity still did not receive the necessary attention. In this context, the present study aimed at assessing the influence of the number of ethylene oxide (EO) units in the head groups of SLE_nS on the toxicity of the SLE_nS-LAS mixed micelles to the bacterium Vibrio fischeri (Microtox® assay, here used as a fast and preliminary ecotoxicological indicator). The SLE_nS variants with fewer EO units showed higher toxicity relatively to those with more EO units - EC₅₀ range (0-50 EO units): 0.56-8.59 mg L^-1, thus they can be suggested as environmentally safer variants to be used in personal care and household products. Provided the consistency of Microtox® results as obtained here, this quick and cost-effective procedure can be an important tool towards the development of eco-friendlier surfactant-based formulations.

2-Ethoxycarbonyl-2-β-hydroxy-a-nor-cholest-5-ene-4one: Extraction, structural characterization, antimicrobial, antioxidant, anticancer and acute toxicity studies

Identification and characterization of marine natural products with antimicrobial, antioxidant activity with minimal toxicity has received much interest over the past few years. Among, Acropora formosa is one of the unexplored marine organism for the screening of natural products in marine resources. In this study, a novel steroid 2-ethoxycarbonyl-2-β-hydroxy-A-nor-cholest-5-ene-4one (ECHC) was isolated from butanol extracts of A. formosa using vacuum liquid chromatography and sequentially purified by column chromatography. The chemical structure of the compound was elucidated based on spectroscopic analysis including GC-MS, ¹H NMR and ¹³C NMR and identified as ECHC. Moreover, in vitro antioxidant activity showed that ECHC was highly scavenged the oxidative stress generative molecules. The in vitro cytotoxic activity of ECHC showed excellent activity against human breast cancer cells. Further, in vivo acute toxicity of ECHC on zebrafish Danio rerio was showed no toxicity as well as no morphological damage was observed after 21 days exposure. Histological analysis revealed that there is no apparent difference was observed between ECHC exposure and control group of D. rerio. Together, these results confirmed that ECHC has in vitro antioxidant and anticancer activity and could be developed as a potential drug against most contagious disease like cancer.

Toward sustainable environmental quality: Priority research questions for Europe

The United Nations' Sustainable Development Goals have been established to end poverty, protect the planet, and ensure prosperity for all. Delivery of the Sustainable Development Goals will require a healthy and productive environment. An understanding of the impacts of chemicals which can negatively impact environmental health is therefore essential to the delivery of the Sustainable Development Goals. However, current research on and regulation of chemicals in the environment tend to take a simplistic view and do not account for the complexity of the real world, which inhibits the way we manage chemicals. There is therefore an urgent need for a step change in the way we study and communicate the impacts and control of chemicals in the natural environment. To do this requires the major research questions to be identified so that resources are focused on questions that really matter. We present the findings of a horizon-scanning exercise to identify research priorities of the European environmental science community around chemicals in the environment. Using the key questions approach, we identified 22 questions of priority. These questions covered overarching questions about which chemicals we should be most concerned about and where, impacts of global megatrends, protection goals, and sustainability of chemicals; the development and parameterization of assessment and management frameworks; and mechanisms to maximize the impact of the research. The research questions identified provide a first-step in the path forward for the research, regulatory, and business communities to better assess and manage chemicals in the natural environment. Environ Toxicol Chem 2018;37:2281-2295. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Pemetrexed degradation by photocatalytic process: Kinetics, identification of transformation products and estimation of toxicity

This study employed a UV-A/visible/TiO₂ system to investigate the degradation of pemetrexed, an antifolate agent used in chemotherapy. The laboratory-scale method employed a photostability chamber that could be used to study multiple samples. Reversed-phase HPLC coupled with high-resolution ESI-LTQ-Orbitrap mass spectrometry was used to determine the transformation products (TPs) of PEME. Based on the identified TPs and existing chemical knowledge, the mechanism of degradation of the target compound was proposed. Concentrations were monitored as a function of time, and the degradation kinetics were compared. The structures of seven TPs, four of which have not been described to date, were proposed. Most of the TPs stemmed from OH radical additions to the dihydropyrrole moiety and oxidative decarboxylation of the glutamate residue. Based on the elucidated structures, a computational toxicity assessment was performed, showing that the TPs with higher log D values than the parent compound are more toxic than the PEME itself. To support these findings, the toxicities of irradiated samples on Vibrio fischeri were monitored over time. The experimental results corresponded well with the results of previous computational studies.

Toward the Design of Less Hazardous Chemicals: Exploring Comparative Oxidative Stress in Two Common Animal Models

Sustainable molecular design of less hazardous chemicals presents a potentially transformative approach to protect public health and the environment. Relationships between molecular descriptors and toxicity thresholds previously identified the octanol-water distribution coefficient, log D, and the HOMO-LUMO energy gap, ΔE, as two useful properties in the identification of reduced aquatic toxicity. To determine whether these two property-based guidelines are applicable to sublethal oxidative stress (OS) responses, two common aquatic in vivo models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio), were employed to examine traditional biochemical biomarkers (lipid peroxidation, DNA damage, and total glutathione) and antioxidant gene activation following exposure to eight structurally diverse industrial chemicals (bisphenol A, cumene hydroperoxide, dinoseb, hydroquinone, indene, perfluorooctanoic acid, R-(-)-carvone, and tert-butyl hydroperoxide). Bisphenol A, cumene hydroperoxide, dinoseb, and hydroquinone were consistent inducers of OS. Glutathione was the most consistently affected biomarker, suggesting its utility as a sensitivity response to support the design of less hazardous chemicals. Antioxidant gene expression (changes in nrf2, gclc, gst, and sod) was most significantly (p < 0.05) altered by R-(-)-carvone, cumene hydroperoxide, and bisphenol A. Results from the present study indicate that metabolism of parent chemicals and the role of their metabolites in molecular initiating events should be considered during the design of less hazardous chemicals. Current empirical and computational findings identify the need for future derivation of sustainable molecular design guidelines for electrophilic reactive chemicals (e.g., SN2 nucleophilic substitution and Michael addition reactivity) to reduce OS related adverse outcomes in vivo.

Analysis of the chemical toxicity effects using the enrichment of Gene Ontology terms and KEGG pathways

BACKGROUND

Chemical toxicity is one of the major barriers for designing and detecting new chemical entities during drug discovery. Unexpected toxicity of an approved drug may lead to withdrawal from the market and significant loss of the associated costs. Better understanding of the mechanisms underlying various toxicity effects can help eliminate unqualified candidate drugs in early stages, allowing researchers to focus their attention on other more viable candidates.

METHODS

In this study, we aimed to understand the mechanisms underlying several toxicity effects using Gene Ontology (GO) terms and KEGG pathways. GO term and KEGG pathway enrichment theories were adopted to encode each chemical, and the minimum redundancy maximum relevance (mRMR) was used to analyze the GO terms and the KEGG pathways. Based on the feature list obtained by the mRMR method, the most related GO terms and KEGG pathways were extracted.

RESULTS

Some important GO terms and KEGG pathways were uncovered, which were concluded to be significant for determining chemical toxicity effects.

CONCLUSIONS

Several GO terms and KEGG pathways are highly related to all investigated toxicity effects, while some are specific to a certain toxicity effect.

GENERAL SIGNIFICANCE

The findings in this study have the potential to further our understanding of different chemical toxicity mechanisms and to assist scientists in developing new chemical toxicity prediction algorithms. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study

The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed.

Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical

The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

Effective Strategies for Monitoring and Regulating Chemical Mixtures and Contaminants Sharing Pathways of Toxicity

Traditionally, hazardous chemicals have been regulated in the U.S. on a one-by-one basis, an approach that is slow, expensive and can be inefficient, as illustrated by a decades-long succession of replacing one type of organohalogen flame retardants (OHFRs) with another one, without addressing the root cause of toxicity and associated public health threats posed. The present article expounds on the need for efficient monitoring strategies and pragmatic steps in reducing environmental pollution and adverse human health impacts. A promising approach is to combine specific bioassays with state-of-the-art chemical screening to identify chemicals and chemical mixtures sharing specific modes of action (MOAs) and pathways of toxicity (PoTs). This approach could be used to identify and regulate hazardous chemicals as classes or compound families, featuring similar biological end-points, such as endocrine disruption and mutagenicity. Opportunities and potential obstacles of implementing this approach are discussed.

Toward safer multi-walled carbon nanotube design: Establishing a statistical model that relates surface charge and embryonic zebrafish mortality

Given the increased utility and lack of consensus regarding carbon nanotube (CNT) environmental and human health hazards, there is a growing demand for guidelines that inform safer CNT design. In this study, the zebrafish (Danio rerio) model is utilized as a stable, sensitive biological system to evaluate the bioactivity of systematically modified and comprehensively characterized multi-walled carbon nanotubes (MWNTs). MWNTs were treated with strong acid to introduce oxygen functional groups, which were then systematically thermally reduced and removed using an inert temperature treatment. While 25 phenotypic endpoints were evaluated at 24 and 120 hours post-fertilization (hpf), high mortality at 24 hpf prevented further resolution of the mode of toxicity leading to mortality. Advanced multivariate statistical methods are employed to establish a model that identifies those MWNT physicochemical properties that best estimate the probability of observing an adverse outcome. The physicochemical properties considered in this study include surface charge, percent surface oxygen, dispersed aggregate size and morphology and electrochemical activity. Of the five physicochemical properties, surface charge, quantified as the point of zero charge (PZC), was determined as the best predictor of mortality at 24 hpf. From a design perspective, the identification of this property-hazard relationship establishes a foundation for the development of design guidelines for MWNTs with reduced hazard.

Designing nanomaterials to maximize performance and minimize undesirable implications guided by the Principles of Green Chemistry

A sustainable material design framework is proposed that emphasizes the importance of establishing structure–property–function (SPF) and structure–property–hazard (SPH) relationships to guide the rational design of ENMs.