Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective

Toxic effects and mechanisms of nanoplastics and sulfonamide antibiotics on Scenedesmus obliquus

The prevalence of nanoplastics (NPs) and sulfonamide antibiotics (SAs) in the aquatic environment is potentially harmful to the environment, and these pollutants are often present in the environment in the form of composite ones, thereby introducing more complex effects and hazards to the environment. Therefore, it is crucial to investigate the toxic effects of the individual target pollutants and their mixtures. In this study, we used Scenedesmus obliquus as the test organisms, two types of NPs: polystyrene (PS) and amine-modified (NH2-PS), four SAs: sulfapyridine (SPY), sulfamethazine (SMR), sulfamethoxypyridazine (SMP), and sulfamethoxazole (SMZ), and their eight binary mixtures were examined. We investigated the toxic interactions of the eight binary mixtures on Scenedesmus obliquus and assessed the impact of the 14 mixtures on the physiological and biochemical properties of Scenedesmus obliquus. Interaction of pollutant assemblages with algal cells observed using field emission scanning electron microscopy. The results showed that the six target pollutants and their eight binary mixtures were significantly toxic to Scenedesmus obliquus within 96 h. The toxicity of individual pollutants was in the order of SPY (EC50: 12.38 mg/L) > SMZ (EC50: 20.43 mg/L) > SMP (EC50: 32.96 mg/L) > SMR (EC50: 41.06 mg/L) > PS (EC50: 284.13 mg/L) > NH2-PS (EC50: 754.13 mg/L); the toxicity of binary mixtures composed of NPs and SAs (89.13 ∼ 1905.46 mg/L) was generally less toxic than that of unitary SAs (12.38 ∼ 41.06 mg/L). Suggesting that the presence of NPs reduced the toxicity of the SAs. The different types of NPs influenced the interaction and toxicity of the mixtures. The effects-based model deviation ratio method was used to quantitatively assess the interactions of the mixture systems in the 10∼90 % experimental effect range. The majority of the PS-containing mixtures exhibited antagonistic interactions. The interactions of NH2-PS-containing mixtures on Scenedesmus obliquus showed different interactions depending on the concentration ratios of the mixture components. The exposure of two NPs and four SAs and their binary mixtures differently promoted or inhibited superoxide dismutase and catalase activities in algal cells to different degrees and resulted in elevated levels of malondialdehyde content, suggesting that oxidative stress led to significant inhibition of chlorophyll content, total protein content, and growth of algal cells. The SEM image can be a more intuitive means of observing the interaction of nanoplastics with algal cells. These findings offer valuable data for the ecological risk assessment of NPs and SAs.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review

Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.

A green approach coupled with molecular dynamics simulations and toxicity assays to infer the mode of action for organophosphate esters

Organophosphate esters (OPEs) have attracted extensive attention due to their toxic effects on human health and biological systems as plasticizers and flame retardants. This study focused on exploring a green approach to get toxicity mechanisms that used less solvents or organisms. The toxicity values of selected organophosphate esters including tri (2-chloroethyl) phosphate (TCEP), tri (1, 3-dichloro-2-propyl) phosphate (TDCP), tripropyl phosphate (TPrP), tri-n-butyl phosphate (TnBP), and tritolyl Phosphate (TCrP) to Photobacterium phosphoreum were determined. Their EC50 range was between 7.27 × 10-8-5.12 × 10-6 mol/L. Based on molecular dynamics simulation the data of binding affinity between OPEs and n-octanol / phospholipid bilayer were calculated respectively. Coupling with binding affinity energies and toxicity values, the mode of action (MOA) of OPEs including types of reactive or anesthetic mechanism could be fast deduced. The proposed hypothesis of n-octanol (C8H18O) as a virtual biofilm to replace phospholipid bilayer was verified. The alternative green approach could simplify toxicity assay and realize fast predicting MOA for different chemicals and model organisms.

Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio

The production of nanoparticles (NPs) has recently become more prevalent owing to their numerous applications in the fast-growing nanotechnology industry. Although nanoparticles have growing applications, there is a significant concern over their environmental impact due to their inevitable release into the environment. With the increasing risk to aquatic organisms, D. magna and zebrafish (Danio rerio) have been preferred as important freshwater model organisms for risk assessment and ecotoxicological studies on metal oxide-based nanoparticles (MeOxNPs) in aquatic environments. It is unfeasible to assess the risks associated with every single NP through in vivo or in vitro experiments. As an alternative, in silico approaches are employed to evaluate the NP toxicity. To evaluate such performance, we have collected data from databases and literature reviews to develop models based on multivariate regression, read-across approach (RA), and machine learning (ML) algorithms following the principles of OECD (Organization for Economic Cooperation and Development) for QSAR modeling. This work has aimed to investigate which features are important drivers of nanotoxicity in D. magna and Danio rerio using simple periodic table-derived descriptors. Further, we have examined the effectiveness of read-across-derived similarity measures compared to traditional QSAR models. The results obtained from model 1 infers that nanoparticles' size, the number of metals, the core environment of the metal present in the metal oxide, and the oxidation number of the metal play a key role in the final expression of toxicity of nanoparticles to D. magna. On the other hand, the presence of higher molecular weight, the core of the metal, and the presence of oxygen influence the enzyme inhibition activity. The enzyme inhibition is correlated with the ability of zebrafish embryos to hatch, and therefore, the inhibition of ZHE1 seems to be the factor driving hatch delay. The study emphasized the importance of developing transferable, reproducible, and easily interpretable models for the early identification of nanoparticle features contributing to aquatic toxicity.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

INTRODUCTION

The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

AREAS COVERED

This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.

EXPERT OPINION

The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

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.

Human health risk assessment for contaminated sites: A retrospective review

Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.

Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective

The preclinical drug discovery stage often requires a large amount of costly and time-consuming experiments using huge sets of chemical compounds. In the last few decades, this process has undergone significant improvements by the introduction of quantitative structure-activity relationship (QSAR) modelling that uses a certain percentage of experimental data to predict the biological activity/property of compounds with similar structural skeleton and/or containing a particular functional group(s). The use of machine learning tools along with it has made life even easier for pharmaceutical researchers. Here, we discuss the toxicity of certain sets of bioactive compounds towards Pimephales promelas and Tetrahymena pyriformis in terms of the global conceptual density functional theory (CDFT)-based descriptor, electrophilicity index (ω). We have compared the results with those obtained by using the commonly used hydrophobicity parameter, logP (where P is the n-octanol/water partition coefficient), considering the greater ease of computing the ω descriptor. The Human African trypanosomiasis (HAT) curing activity of 32 pyridyl benzamide derivatives is also studied against Tryphanosoma brucei. In this review article, we summarize these multiple linear regression (MLR)-based QSAR studies in terms of electrophilicity (ω, ω2) and hydrophobicity (logP, (logP)2) parameters.

Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis has been verified.

Developing a QSPR Model of Organic Carbon Normalized Sorption Coefficients of Perfluorinated and Polyfluoroalkyl Substances

Perfluorinated and polyfluoroalkyl substances (PFASs) are known for their long-distance migration, bioaccumulation, and toxicity. The transport of PFASs in the environment has been a source of increasing concerned. The organic carbon normalized sorption coefficient (Koc) is an important parameter from which to understand the distribution behavior of organic matter between solid and liquid phases. Currently, the theoretical prediction research on log Koc of PFASs is extremely limited. The existing models have limitations such as restricted application fields and unsatisfactory prediction results for some substances. In this study, a quantitative structure–property relationship (QSPR) model was established to predict the log Koc of PFASs, and the potential mechanism affecting the distribution of PFASs between two phases from the perspective of molecular structure was analyzed. The developed model had sufficient goodness of fit and robustness, satisfying the model application requirements. The molecular weight (MW) related to the hydrophobicity of the compound; lowest unoccupied molecular orbital energy (ELUMO) and maximum average local ionization energy on the molecular surface (ALIEmax), both related to electrostatic properties; and the dipole moment (μ), related to the polarity of the compound; are the key structural variables that affect the distribution behavior of PFASs. This study carried out a standardized modeling process, and the model dataset covered a comprehensive variety of PFASs. The model can be used to predict the log Koc of conventional and emerging PFASs effectively, filling the data gap of the log Koc of uncommon PFASs. The explanation of the mechanism of the model has proven to be of great value for understanding the distribution behavior and migration trends of PFASs between sediment/soil and water, and for estimating the potential environmental risks generated by PFASs.

In silico analysis of Typha domingensis Pers. phytocompounds against wound healing biomarkers and ascertaining through in vitro cell migration assay

Typha domingensis Pers. is known for its medicinal properties. Although traditionally T. domingensis Pers. has been used for wound healing, yet scientific investigations reporting its ability to heal wounds are lacking. Phytochemical profiling of T. domingensis Pers. inflorescence crude extract was carried out by LC-MS analysis. Ten phytochemicals were selected for in silico analysis based on retention time, mass-to-charge ratio and resolution of mass spectrum. Molecular docking of all ten compounds was done against selected wound healing biomarkers viz., interleukin 6(IL-6), interleukin β (IL-β), insulin-like growth factor tyrosine kinase receptor (IGF-1R) and transformation growth factor β (TGF-β). Based on this, catechin, mesalazine and piperazine were subjected for in vitro cell migration assay (3T3 L1 mouse fibroblast cell line) to assess their wound healing potentials. Molecular docking revealed that mesalazine, catechin, and piperazine have potential ligands based on lowest docking energy (ranging from - 4.1587 to - 0.972), Glide E score (ranging from - 26.929 to - 57.882), Glide G score (ranging from - 4.16 to - 7.972) and numbers of hydrogen bonds compared to other compounds studied. The migration assay revealed that, compared to control (52.5%), T. domingensis Pers. inflorescence crude extract showed maximum wound healing potential (80%) followed by Catechin (66.8%) Mesalazine (58.3%) and Piperazine (51.2%). The combined in silico and in vitro approach opens new dimension for designing innovative therapeutics to manage different types of wounds.

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R²) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

Prediction of degradability of micropollutants by sonolysis in water with QSPR - a case study on phenol derivates

The increasing quantity and variety of organic contaminants discharged into surface and groundwater increase the necessity of additional and suitable water treatment methods, which can be incorporated into existing wastewater treatment plants. The huge variety of micropollutants and local variability of the composition of the organic load or matrix effects paired with multiple possible degradation processes lead to the requirement of a recommendation tool for the best possible water treatment method under given local conditions. Due to the diversity of physicochemical properties of micropollutants, such predictions are challenging. In this study, a quantitative correlation between the structural properties of certain micropollutants and their degradability using high-frequency sonolysis has been investigated. Therefore, Quantitative Structure-Property Relationship (QSPR) has been applied on a set of phenol derivates. To obtain the kinetic data, all experiments have been conducted in standardized, constant conditions for all 32 investigated phenol derivates. QSPR modelling was then executed using the software PaDEL for descriptor calculation and the software QSARINS for the overall modelling process including genetic algorithm (GA) and multiple linear regression (MLR). The final model consisting of 5 molecular descriptors was selected using a multi-criteria decision-making method based on extensive statistical parameters. The predictive power and robustness of the model was evaluated by means of internal cross validation and external validation using an independent validation set. The final selected model showed very good values for regression abilities, predictive power as well as stability (R²_adj = 0.9455, CCC_tr = 0.9777, Q²_loo = 0.9285, CCC_ext = 0.9797 and Q²_ext-F1 = 0.9711). The applicability domain of the QSPR model was defined based on the Williams plot and Insubria plot. The five OECD principles for the application of QSPR/QSAR modelling in industry and regulation were fulfilled in the whole process to the best of our knowledge, including the collection of the underlying experimental data as well as the entire modelling process. The final QSPR model included the molecular polarity and occurrence of hydrogen bonds as major influences on the reaction rate constants in accordance with previous studies. Nevertheless, potential biases in the selection of these descriptors due to the small size of the dataset were highlighted.

Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach

For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (R_train² = 0.64, RMSE_train = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (R_test²,R_m², Q_F1², Q_F2², Q_F3² and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.

Hormetic dose-responses for silver antibacterial compounds, quorum sensing inhibitors, and their binary mixtures on bacterial resistance of Escherichia coli

Silver antibacterial compounds (SACs) and quorum sensing inhibitors (QSIs), as the potential antibiotic substitutes, have been recommended to prevent and treat microbial infections for the purpose of controlling the increasingly serious bacterial resistance induced by the abuse of antibiotics. However, there is little information regarding the resistance risk of these compounds, especially their mixtures. In this study, bacterial mutation and RP4 plasmid conjugative transfer among bacteria were used to characterize the bacterial endogenous and exogenous resistance, respectively. The effects of SACs (including silver nitrate (AgNO₃) and silver nanoparticle (AgNP)), QSIs, and their binary mixtures on the bacterial resistance were investigated via setting the frequency of mutation and conjugative transfer in Escherichia coli (E. coli) as the test endpoints. The results indicated that these two endpoints exhibited hormetic dose-responses to each treatment. Furthermore, the joint resistance actions between SACs and QSIs were all judged to be antagonism. Correlation analysis suggested that the promotion of the bacterial resistance in each treatment was closely related to its toxicity. It was speculated that AgNO₃ and AgNP might both release Ag⁺ ions to facilitate the E. coli resistance, while QSIs probably acted on LsrR and SdiA proteins to stimulate the bacterial mutation and accelerate the RP4 plasmid conjugative transfer, respectively. These findings imply that the bacteria may generate targeted stress response to the survival pressure from environmental compounds, displaying hormetic phenomenon in resistance-related test endpoints. This study provides a new insight into the resistance risk induced by SACs and QSIs, benefiting the environmental risk assessment of these compounds from the perspective of bacterial resistance.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

A Survey of Systematic Evidence Mapping Practice and the Case for Knowledge Graphs in Environmental Health and Toxicology

Systematic evidence mapping offers a robust and transparent methodology for facilitating evidence-based approaches to decision-making in chemicals policy and wider environmental health (EH). Interest in the methodology is growing; however, its application in EH is still novel. To facilitate the production of effective systematic evidence maps for EH use cases, we survey the successful application of evidence mapping in other fields where the methodology is more established. Focusing on issues of “data storage technology,” “data integrity,” “data accessibility,” and “transparency,” we characterize current evidence mapping practice and critically review its potential value for EH contexts. We note that rigid, flat data tables and schema-first approaches dominate current mapping methods and highlight how this practice is ill-suited to the highly connected, heterogeneous, and complex nature of EH data. We propose this challenge is overcome by storing and structuring data as “knowledge graphs.” Knowledge graphs offer a flexible, schemaless, and scalable model for systematically mapping the EH literature. Associated technologies, such as ontologies, are well-suited to the long-term goals of systematic mapping methodology in promoting resource-efficient access to the wider EH evidence base. Several graph storage implementations are readily available, with a variety of proven use cases in other fields. Thus, developing and adapting systematic evidence mapping for EH should utilize these graph-based resources to ensure the production of scalable, interoperable, and robust maps to aid decision-making processes in chemicals policy and wider EH.

Concentration Addition, Independent Action, and Quantitative Structure-Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pK_a ). The toxicities of synergetic mixtures can be interpreted with the total energy (E_T ) and pK_a of the mixtures. Dipole moment and E_T are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.

Monophosphoramide derivatives: synthesis and crystal structure, theoretical and experimental studies of their biological effects

Synthesizing new chemical compounds and studying their biological applications have been important issues in scientific research. In this investigation, we synthesized and characterized ten new N-acetyl phosphoramidate compounds and explored the crystal structure of three others. Furthermore, not only were some kinetic inhibition parameters measured, like IC₅₀, K_i, k_p, K_D for 7 compounds on human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), but also their hydrophobic parameter was determined by shake-flask technique. All compounds (number 1-10) were investigated for anti-bacterial activity against three Gram-positive and three Gram-negative bacteria, while chloramphenicol was used as a standard antibiotic. In order to find new insecticide, toxicities of 13 acephate (Ace)-derived compounds (number 20-32) were bioassayed on third larval instar of elm leaf beetle and Xanthogaleruca luteola. Additionally, screening in vivo tests revealed that two compounds had had the greatest insecticidal potential in comparison with others. It means these ones inhibited AChE (with mixed mechanisms) and general esterase more than the rest. According to ChE-QSAR models, the inhibitory potency for enzyme and bacteria is directly influenced by the electronic parameters versus structural descriptors. AChE-QSPR model of fluorescence assay indicated that the inhibitory power of AChE is primarily influenced by a set of electronic factors with the priority of: E_HB > PL > δ(³¹P) versus structural descriptor (SA and Mv). Synthesizing new chemical compounds and studying their biological applications have been important issues in scientific research. Toxicities of 13 acephate (Ace)-derived compounds (number 20-32) were bioassayed on third larval instar of elm leaf beetle and Xanthogaleruca luteola. Insect-QSAR equations of these compounds, based on MLR and PCA, showed that non-descriptor net charge nitrogen atom (which was affected by the polarization of N-H group) had the greatest effect on insecticidal potential.

Chemical Diversity of Soft Coral Steroids and Their Pharmacological Activities

The review is devoted to the chemical diversity of steroids produced by soft corals and their determined and potential activities. There are about 200 steroids that belong to different types of steroids such as secosteroids, spirosteroids, epoxy- and peroxy-steroids, steroid glycosides, halogenated steroids, polyoxygenated steroids and steroids containing sulfur or nitrogen heteroatoms. Of greatest interest is the pharmacological activity of these steroids. More than 40 steroids exhibit antitumor and related activity with a confidence level of over 90 percent. A group of 32 steroids shows anti-hypercholesterolemic activity with over 90 percent confidence. Ten steroids exhibit anti-inflammatory activity and 20 steroids can be classified as respiratory analeptic drugs. Several steroids exhibit rather rare and very specific activities. Steroids exhibit anti-osteoporotic properties and can be used to treat osteoporosis, as well as have strong anti-eczemic and anti-psoriatic properties and antispasmodic properties. Thus, this review is probably the first and exclusive to present the known as well as the potential pharmacological activities of 200 marine steroids.

Evaluation of modes of action of pesticides to Daphnia magna based on QSAR, excess toxicity and critical body residues

Agricultural pesticides serve as effective controls of unwanted weeds and pests. However, these same chemicals can exert toxic effects in non-target organisms. To determine chemical modes of action, the toxicity ratio (TR) and critical body residues (CBRs) of 57 pesticides were calculated for Daphnia magna. Results showed that the CBR values of inert compounds were close to a constant while the CBR values of pesticides varied over a wider range. Although herbicides are categorized as specifically-acting compounds to plants, herbicides did not exhibit excess toxicity to Daphnia magna and were categorized as inert compounds with an average logTR = 0.41, which was less than a threshold of one. Conversely, fungicides and insecticides exhibited strong potential for toxic effects to Daphnia magna with an average logTR >2. Many of these chemicals act via disruption of the nervous, respiratory, or reproductive system, with high ligand-receptor binding activity which leads to higher toxicity for Daphnia magna. Molecular docking using acetylcholinesterase revealed that fungicides and insecticides bind more easily with the biological macromolecule when compared with inert compounds. Quantitative structure-activity relationship (QSAR) analysis revealed that the toxicity of fungicides was mainly dependent upon the heat of formation and polar surface area, while the toxicity of insecticides was more related to hydrogen-bond properties. This comprehensive analysis reveals that there are specific differences in toxic mechanisms between fungicides and insecticides. These results are useful for determining relative risk associated with pesticide exposure to aquatic crustaceans, such as Daphnia magna.

Cytotoxicity of Halogenated Tyrosyl Compounds, an Emerging Class of Disinfection Byproducts

Halogenated amino acids and peptides are an emerging class of disinfection byproducts (DBPs), having been detected in drinking water and in washed food products. However, the toxicological significance of these emerging DBPs remains unclear. In this study, the cytotoxicity of eight halogenated tyrosyl compounds was investigated in Chinese hamster ovary (CHO) cells using real-time cell analysis (RTCA). Dihalogenated tyrosyl compounds are more cytotoxic than their monohalogenated analogues. The cytotoxicity of the dihalogenated compounds is associated with their ability to induce intracellular reactive oxygen species (ROS), suggesting that oxidative stress is an important toxicity pathway of these compounds. Pearson correlation analysis of the cytotoxicity (IC₅₀ values) of these compounds with eight physicochemical parameters showed strong associations with their lipophilicity (logP) and reactivity (polarizability, E_LUMO). Finally, cytotoxicity testing of the concentrated extracts of a chloraminated mixture of eight dipeptides with bromide or iodide showed the cytotoxicity of these mixtures in the order: iodinated peptides > brominated peptides ≥ chlorinated peptides. These results demonstrate that halogenated peptide DBPs are toxicologically relevant, and further research is needed to understand the implications of long-term exposure for human health.

An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials

In line with the 3R concept, nanotoxicology is shifting from a phenomenological to a mechanistic approach based on in vitro and in silico methods, with a consequent reduction in animal testing. Risk Assessment (RA) and Life Cycle Assessment (LCA) methodologies, which traditionally rely on in vivo toxicity studies, will not be able to keep up with the pace of development of new nanomaterials unless they adapt to use this new type of data. While tools and models are already available and show a great potential for future use in RA and LCA, currently none is able alone to quantitatively assess human hazards (i.e. calculate chronic NOAEL or ED₅₀ values). By highlighting which models and approaches can be used in a quantitative way with the available knowledge and data, we propose an integrated pathway for the use of in vitro data in RA and LCA. Starting with the characterization of nanoparticles' properties, the pathway then investigates how to select relevant in vitro human data, and how to bridge in vitro dose-response relationships to in vivo effects. If verified, this approach would allow RA and LCA to stir up the development of nanotoxicology by giving indications about the data and quality requirements needed in risk methodologies.

Identification, formation and control of polar brominated disinfection byproducts during cooking with edible salt, organic matter and simulated tap water

Edible salt is essential to the health of humans and serves as a seasoning universally. Besides chloride, edible salt also contains other anions such as bromide, fluoride, sulfate, and carbonate due to incomplete removal during raw salt refinement. In a household cooking (e.g., soup making) process, a chlorine/monochloramine residual in tap water could react with bromide in edible salt and organic matter in food (e.g., rice, wheat) to form numerous brominated disinfection byproducts (Br-DBPs) at significant levels, which might induce adverse health effects to human beings. In this study, we solicited 20 edible salts of different types (i.e., sea salts, well and rock salts, lake salts, and bamboo salts) from nine countries and determined their bromide levels to be 67-375 mg/kg, with an average level of 173 mg/kg. A total of 25 polar Br-DBPs were detected and identified with structures/formulae in cooking water samples using ultra performance liquid chromatography/electrospray ionization-triple quadruple mass spectrometry (UPLC/ESI-tqMS) and high-resolution mass spectrometry. Effects of cooking conditions (e.g., disinfectant type and level, edible salt dose, organic matter type and dose, sequence and time interval of adding organic matter and salt, etc.) on the formation of polar Br-DBPs were investigated, and optimized cooking conditions with minimized formation of polar Br-DBPs were determined. Further aided with an Hep G2 cell cytotoxicity assay, it was found that the overall cytotoxicity of chlorinated and chloraminated cooking water samples prepared after cooking condition optimization was reduced by 57% and 22%, respectively, compared with those prepared before cooking condition optimization.

Regular time-dependent cross-phenomena induced by hormesis: A case study of binary antibacterial mixtures to Aliivibrio fischeri

Time-dependent cross-phenomenon in which the cross between the actual concentration-response curve (CRC) for mixture crosses the CRCs for reference model varies with time has been frequently reported in previous studies, expressed as a heterogeneous pattern of joint toxic action. However, the variation tendency of time-dependent cross-phenomenon is rarely addressed. In this study, the joint toxic actions of binary antibacterial mixtures (i.e., two quorum sensing inhibitors, tetracycline hydrochloride, erythromycin, and chloramphenicol with sulfonamides) were judged using independent action (IA) model to find the variation tendency of time-dependent cross-phenomenon. The results show that the time-dependent cross-phenomena of the test binary antibacterial mixtures follow a unified variation tendency and the corresponding joint toxic actions change regularly with an increase of both concentration and time. Through investigating the relationship between the stimulatory and inhibitory modes of action for the single agents and the time-dependent cross-phenomena of binary mixtures, the regular time-dependent cross-phenomena is speculated to be derived from the hormetic effects of the components in the mixtures. This study offers an advance for the variation tendency and mechanistic explanation of time-dependent cross-phenomenon, which will provide a support for the future development in the exploration of time-dependent cross-phenomenon and environmental risk assessment of pollutant mixtures.

The Application of CA and PCA to the Evaluation of Lipophilicity and Physicochemical Properties of Tetracyclic Diazaphenothiazine Derivatives

The subject of the study was 11 new synthetized tetracyclic diazaphenothiazine derivatives. Using thin-layer chromatography in a reverse phase system (RP-TLC), their R _M0 lipophilicity parameter was determined. The mobile phase was composed of 0.2 M Tris buffer (pH = 7.4) and acetone (POCH S.A., Gliwice, Poland) in different concentrations. Using computer programs, based on different computational algorithms, theoretical values of lipophilicity (AClogP, ALOGP, ALOGPs, miLogP, MLOGP, XLOGP2, and XLOGP3) as well as molecular descriptors (molecular weight, volume of a molecule, dipole moment, polar surface, and energy of HOMO orbitals and LUMO orbitals) and parameters of biological activity: human intestinal absorption (HIA), plasma protein binding (PPB), and blood-brain barrier (BBB), were determined. The correlations between the experimental values of lipophilicity and theoretically calculated lipophilic values and also between experimental values of lipophilicity and values of physicochemical or biological properties were assessed. A certain relationship between structure and lipophilicity was found. On the other hand, the relationships between R _M0 and physicochemical or biological properties were not statistically significant and therefore unusable. For all analysed values, an analysis of similarities and principal component analyses were also made. The obtained dendrograms for the analysis of lipophilicity and physicochemical and biological properties indicate the relationship between experimental values of lipophilicity and structure in the case of theoretical lipophilicity values only. PCA, on the other hand, showed that ALOGP, MLOGP, miLogP, and BBB and molar volume have the largest share in the description of the entire system. Distribution of compounds on the area of factors also indicates the connections between them related to their structure.

Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data

Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure-property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The 'ideal correlation' is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.

Predicting skin permeability using the 3D-RISM-KH theory based solvation energy descriptors for a diverse class of compounds

The state-of-the-art molecular solvation theory is used to predict skin permeability of a large set of compounds with available experimental skin permeability coefficient (logK_P). Encouraging results are obtained pointing to applicability of a novel quantitative structure activity model that uses statistical physics based 3D-RISM-KH theory for solvation free energy calculations as a primary descriptor for the prediction of logK_P with relative mean square error of 0.77 units.

Comparative Quantitative Structure–Activity–Activity Relationships for Toxicity to Tetrahymena pyriformis and Pimephales promelas

An approach for predicting acute aquatic toxicity, in the form of a quantitative structure–activity–activity relationship (QSAAR), is described. This study assessed relative toxic effects to a fish, Pimephales promelas, and a ciliate, Tetrahymena pyriformis, and attempted to form relationships between them. A good agreement between toxic potencies ( R2 = 0.754) was found for a chemically diverse dataset of 364 compounds, when using toxicity to the ciliate as a surrogate to that for fish. This relationship was extended by adding three theoretical structural descriptors of the molecules. The inclusion of these descriptors improved the relationship further ( R2 = 0.824). The structural features that were found to improve the extrapolation between the toxicity to the two different species were related to the electron distribution of the carbon skeleton of the toxicant, its hydrogen-bonding ability, and its relative nitrogen content. Such a QSAAR approach provides a potential tool for predicting the toxicities of chemicals for environmental risk assessment and thus for reducing animal tests.

Methodology of aiQSAR: a group-specific approach to QSAR modelling

Background

Several QSAR methodology developments have shown promise in recent years. These include the consensus approach to generate the final prediction of a model, utilizing new, advanced machine learning algorithms and streamlining, standardization and automation of various QSAR steps. One approach that seems under-explored is at-the-runtime generation of local models specific to individual compounds. This approach was quite likely limited by the computational requirements, but with current increases in processing power and the widespread availability of cluster-computing infrastructure, this limitation is no longer that severe.

Results

We propose a new QSAR methodology: aiQSAR, whose aim is to generate endpoint predictions directly from the input dataset by building an array of local models generated at-the-runtime and specific for each compound in the dataset. The local group of each compound is selected on the basis of fingerprint similarities and the final prediction is calculated by integrating the results of a number of autonomous mathematical models. The method is applicable to regression, binary classification and multi-class classification and was tested on one dataset for each endpoint type: bioconcentration factor (BCF) for regression, Ames test for binary classification and Environmental Protection Agency (EPA) acute rat oral toxicity ranking for multi-class classification. As part of this method, the applicability domain of each prediction is assessed through the applicability domain measure, calculated on the basis of the fingerprint similarities in each local group of compounds.

Conclusions

We outline the methodology for a new QSAR-based predictive tool whose advantages are automation, group-specific approach to modelling and simplicity of execution. Our aim now will be to develop this method into a stand-alone software tool. We hope that eventual adoption of our tool would make QSAR modelling more accessible and transparent. Our methodology could be used as an initial modelling step, to predict new compounds by simply loading the training dataset as an input. Predictions could then be further evaluated and refined either by other tools or through optimization of aiQSAR parameters.

Electronic supplementary material

The online version of this article (10.1186/s13321-019-0350-y) contains supplementary material, which is available to authorized users.

Multiple-species hormetic phenomena induced by indole: A case study on the toxicity of indole to bacteria, algae and human cells

Hormesis is a dose-response relationship phenomenon characterized by low-dose stimulation and high-dose inhibition. Although hormetic phenomena have been reported in broadly ranging biological areas, there is still no unified mechanism of hormesis. Investigating multiple-species hormesis of one compound and then exploring the possible mechanism may be an effective approach to clarify the reason for the occurrence of hormetic phenomena in a broad range of organisms. In this study, indole was selected as the test chemical due to the broad biological and hormetic effects of indole compounds. The results show that indole induces multiple-species hormetic phenomena in bacteria (Aliivibrio fischeri (A. fischeri), Escherichia coli and Bacillus subtilis), algae (Microcystis aeruginosa and Selenastrum capricornutum), and human cells (human skin fibroblasts and human cervical cancer cells). Through in-depth investigation of the time-dependent hormetic effects of indole, indole derivatives and indole's structural analogs on the bioluminescence of A. fischeri, indole ring has been identified as the potential key structure that causes indole to act on quorum sensing of A. fischeri to induce hormetic effects on the bioluminescence at lag, logarithmic, and stationary phases. Therefore, the occurrence of multiple-species hormetic phenomena is speculated to be derived from the action of indole on the cell-to-cell communication of organism cells. This paper can not only further confirm the generalizability of hormesis but also provide a reasonable explanation for hormesis, which will benefit the development of hormesis and the risk assessment of environmental pollutants.

Linking algal growth inhibition to chemical activity: Excess toxicity below 0.1% of saturation

Chemical activity quantifies the energetic level of an organic compound relative to its pure liquid [0-1], and several studies have reported that baseline toxicity generally requires chemical activities of 0.01-0.1. The first aim was to challenge this chemical activity range for baseline toxicity. Algal growth inhibition data (median effective concentrations, EC₅₀) were compiled from two recent studies and included 108 compounds categorised as non-polar (mode of toxic action, MOA1) and polar (MOA2) narcotics. These data were linked to chemical activity by (1) plotting them relative to a regression for (subcooled) liquid solubility (S_L), which served as visual reference for chemical activity of unity and (2) determining EC₅₀/S_L ratios that essentially equal median effective chemical activity (Ea₅₀). Growth inhibition required chemical activity >0.01 for MOA1 and >0.001 for MOA2 compounds. The second aim was to identify compounds exerting excess toxicity, i.e., when growth inhibition occurred at chemical activity <0.001. From a recent review with 2323 data entries, 315 EC₅₀ values passed our selection criteria. 280 of these EC₅₀ values were within or near the baseline toxicity range (Ea₅₀>0.001), and 25 compounds were found to exert excess toxicity (Ea₅₀<0.001). Of these compounds, 16 are pesticides or precursors. Methodologically, this study includes two methods for translating EC₅₀ values into the chemical activity framework, each having advantages and limitations. Scientifically, this study confirms that baseline toxicity generally requires chemical activities of 0.01-0.1 and extends the application of the chemical activity approach beyond baseline toxicity, by demonstrating its utility to identify compounds that exert excess toxicity.

A swinging seesaw as a novel model mechanism for time-dependent hormesis under dose-dependent stimulatory and inhibitory effects: A case study on the toxicity of antibacterial chemicals to Aliivibrio fischeri

Hormesis occurs frequently in broadly ranging biological areas (e.g. plant biology, microbiology, biogerontology), toxicology, pharmacology and medicine. While numerous mechanisms (e.g. receptor and pathway mediated pathway responses) account for stimulatory and inhibitory features of hormetic dose responses, the vast majority emphasizes the inclusion of many doses but only one timepoint or use of a single optimized dose that is assessed over a broad range of timepoints. In this paper, a toxicity study was designed using a large number of properly spaced doses with responses determined over a large number of timepoints, which could help us reveal the underlying mechanism of hormesis. We present the results of a dose-time-response study on hormesis using five antibacterial chemicals on the bioluminescence of Aliivibrio fischeri, measuring expression of protein mRNA based on quorum sensing, simulating bioluminescent reaction and analyzing toxic actions of test chemicals. The findings show dose-time-dependent responses conforming to the hormetic dose-response model, while revealing unique response dynamics between agent induced stimulatory and inhibitory effects within bacterial growth phase dynamics. These dynamic dose-time features reveal a type of biological seesaw model that integrates stimulatory and inhibitory responses within unique growth phase, dose and time features, which has faultlessly explained the time-dependent hormetic phenomenon induced by five antibacterial chemicals (characterized by low-dose stimulation and high-dose inhibition). This study offers advances in understanding cellular dynamics, the biological integration of diverse and opposing responses and their role in evolutionary adaptive strategies to chemicals, which can provide new insight into the mechanistic investigation of hormesis.

How Green is Your Plasticizer?

Plasticizers are additives that are used to impart flexibility to polymer blends and improve their processability. Plasticizers are typically not covalently bound to the polymers, allowing them to leach out over time, which results in human exposure and environmental contamination. Phthalates, in particular, have been the subject of increasing concern due to their established ubiquity in the environment and their suspected negative health effects, including endocrine disrupting and anti-androgenic effects. As there is mounting pressure to find safe replacement compounds, this review addresses the design and experimental elements that should be considered in order for a new or existing plasticizer to be considered green. Specifically, a multi-disciplinary and holistic approach should be taken which includes toxicity testing (both in vitro and in vivo), biodegradation testing (with attention to metabolites), as well as leaching studies. Special consideration should also be given to the design stages of producing a new molecule and the synthetic and scale-up processes should also be optimized. Only by taking a multi-faceted approach can a plasticizer be considered truly green.

Mechanistic explanation of time-dependent cross-phenomenon based on quorum sensing: A case study of the mixture of sulfonamide and quorum sensing inhibitor to bioluminescence of Aliivibrio fischeri

Cross-phenomenon in which the concentration-response curve (CRC) for a mixture crosses the CRC for the reference model has been identified in many studies, expressed as a heterogeneous pattern of joint toxic action. However, a mechanistic explanation of the cross-phenomenon has thus far been extremely insufficient. In this study, a time-dependent cross-phenomenon was observed, in which the cross-concentration range between the CRC for the mixture of sulfamethoxypyridazine (SMP) and (Z-)-4-Bromo-5-(bromomethylene)-2(5H)-furanone (C30) to the bioluminescence of Aliivibrio fischeri (A. fischeri) and the CRC for independent action model with 95% confidence bands varied from low-concentration to higher-concentration regions in a timely manner expressed the joint toxic action of the mixture changing with an increase of both concentration and time. Through investigating the time-dependent hormetic effects of SMP and C30 (by measuring the expression of protein mRNA, simulating the bioluminescent reaction and analyzing the toxic action), the underlying mechanism was as follows: SMP and C30 acted on the quorum sensing (QS) system of A. fischeri, which induced low-concentration stimulatory effects and high-concentration inhibitory effects; in the low-concentration region, the stimulatory effects of SMP and C30 made the mixture produce a synergistic stimulation on the bioluminescence; thus, the joint toxic action exhibited antagonism. In the high-concentration region, the inhibitory effects of SMP and C30 in the mixture caused a double block in the loop circuit of the QS system; thus, the joint toxic action exhibited synergism. With the increase of time, these stimulatory and inhibitory effects of SMP and C30 were changed by the variation of the QS system at different growth phases, resulting in the time-dependent cross-phenomenon. This study proposes an induced mechanism for time-dependent cross-phenomenon based on QS, which may provide new insight into the mechanistic investigation of time-dependent cross-phenomenon, benefitting the environmental risk assessment of mixtures.

A new computational model for the prediction of toxicity of phosphonate derivatives using QSPR

Structural and electronic properties of a series of 25 phosphonate derivatives were analyzed applying density functional theory, with the exchange-correlation functional PBEPBE in combination with the 6-311++G** basis set for all atoms. The chemical reactivity of these derivatives has been interpreted using quantum descriptors such as frontier molecular orbitals (HOMO, LUMO), Hirshfeld charges, molecular electrostatic potential, and the dual descriptor [[Formula: see text]]. These descriptors are directly related to experimental median lethal dose ([Formula: see text], expressed as its decimal logarithm [[Formula: see text]([Formula: see text]] through a multiple linear regression equation. The proposed model predicts the toxicity of phosphonates in function of the volume (V), the load of the most electronegative atom of the molecule (q), and the eigenvalue of the molecular orbital HOMO ([Formula: see text]. The obtained values in the internal validation of the model are: [Formula: see text]%, [Formula: see text]%, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]%. The toxicity of nine phosphonate derivatives used as test molecules was adequately predicted by the model. The theoretical results indicate that the oxygen atom of the O=P group plays an important role in the interaction mechanism between the phosphonate and the acetylcholinesterase enzyme, inhibiting the removal of the proton of the ser-200 residue by the his-440 residue.

Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach

An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compartment, where they do affect the living systems. Unfortunately, there is a huge gap in the availability of ecotoxicological data on pharmaceuticals' environmental behavior and ecotoxicity which force EMEA (European Medicines Agency) to release guidelines for their risk assessment. In silico modeling approaches are vital tools to exploit the existing information to rapidly emphasize the potentially most hazardous and toxic pharmaceuticals and prioritize the most environmentally hazardous ones for focusing further on their experimental studies. The quantitative structure-activity relationship (QSAR) models are capable of predicting missing properties for toxic end-points required to prioritize existing, or newly synthesized chemicals for their potential hazard. This chapter reviews the information regarding occurrence and impact of pharmaceuticals and their metabolites in the environment along with their persistence, environmental fate, risk assessment, and risk management. A bird's eye view about the necessity of in silico methods for fate prediction of pharmaceuticals in the environment as well as existing successful models regarding ecotoxicity of pharmaceuticals are discussed. Available toxicity endpoints, ecotoxicity databases, and expert systems frequently used for ecotoxicity predictions of pharmaceuticals are also reported. The overall discussion justifies the requirement to build up additional in silico models for quick prediction of ecotoxicity of pharmaceuticals economically, without or involving only limited animal testing.

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)^-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q²=0.830 and R²=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.

A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEP_ext), fitting correlation coefficient (R²), and leave-one-out cross-validation (Q²_LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R² and Q²_LOO of 4 kinds of nonlinear models are more than 0.6 and SDEP_ext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R² = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study

The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;36:2227-2233. © 2017 SETAC.

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q²_ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q²_ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors

Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.