Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

AQUA Tox: A web tool for predicting aquatic toxicity in rotifer species using intrinsic explainable models

The widespread use of chemicals in various industries, including agriculture, cosmetics, pharmaceuticals, and textiles, poses significant environmental risks, particularly in aquatic ecosystems. This study focuses on the toxicity of organic compounds on two rotifer species, Brachionus calyciflorus and Brachionus plicatilis, widely used as bioindicators in ecotoxicology. A database of toxicity data (LC50) was compiled and QSAR/QSTR models were developed to predict chemical toxicity in both freshwater (FW) and saltwater (SW) environments. Using molecular descriptors, the study identified critical factors influencing toxicity, such as hydrophobicity and the presence of chlorine atoms. The models demonstrated strong predictive performance, with R² values exceeding 70 % for both FW and SW conditions. Key descriptors influencing toxicity included hydrophobicity and chlorine content. The models demonstrated strong predictive performance, with R² values exceeding 70 %. A user-friendly web application was developed, enabling the scientific community to assess the aquatic toxicity of chemicals. This tool aids in the design of safer, more sustainable substances, facilitating regulatory compliance and minimizing environmental impacts. The findings highlight the importance of combining computational methods with technological applications for environmental protection.

First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species

Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ongoing synthesis of chemicals and the various potential harmful effects on living organisms are prompting regulatory bodies to view computational approaches as vital supplements and alternatives to traditional animal testing in assessing chemical risks. In this study, we have developed, for the first time, Quantitative Structure-Toxicity Relationship (QSTR) models based on Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict organic chemical toxicity against a rotifer species (Brachionus calyciflorus). The most influential descriptors included in the MLR model are (SM6_B(p), B07[ClCl], B05[ClCl], MaxssCH2, F09[NO], B04[ClCl], and minssO), with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 24 h). The interpretation of the molecular descriptors of the MLR model suggested that substances with high molecular polarizability and lipophilicity (presence of chlorine atoms) positively influence and increase their toxic potency. The analysis of the application domain, conducted using the leverage approach and the standardized residual method, showcased the extensive applicability of each model. In the cross-validation, the best values are presented by Support Vector Regression (SV_R), a value of Q2Loo = 0.754 and RMSEcv = 0.652, which are slightly higher than the results of the other linear and nonlinear techniques used. Furthermore, our research exhibited a high degree of fitness, internal robustness, and external predictive power. These findings suggest that the developed QSTR models are well-suited for the reliable prediction of aquatic toxicity for a wide range of structurally diverse organic chemicals. These models can be valuable for tasks such as screening, prioritizing new compounds, filling data gaps, and mitigating the limitations associated with in vivo and in vitro tests, ultimately contributing to the reduction of the use of dangerous chemicals in the environment.

Developing predictive models for assessing LC50 of organic contaminants in Gammarus species using interpretable structural parameters

Gammarus species are crucial indicators of environmental health, making them important for ecological studies and water quality assessments. They offer a wide range of specific responses regarding the median lethal concentration (LC50) of organic compounds. This research presents four predictive models to determine the LC50 of chemicals impacting selected gammarid amphipods: Gammarus lacustris, Gammarus fasciatus, Gammarus pulex, and Gammarus pseudolimnaeus. These species are recognized for their sensitivity to various pollutants and are among the most sensitive aquatic invertebrates. The new models provide straightforward methods for estimating the pLC50 (-log LC50/molecular weight) of various organic compounds based on interpretable structural parameters including the number of effective functional groups, the types of atoms present, and various structural characteristics of organic molecules. This study aims to leverage the largest available experimental dataset compared to prior quantitative structure-activity relationship (QSAR) models for these gammarid amphipods. The dataset contained toxicity data for 91 compounds affecting Gammarus fasciatus, 50 for Gammarus lacustris, and 48 each for Gammarus pseudolimnaeus and Gammarus pulex, aligning with comparative QSAR models. External datasets included 13 compounds for Gammarus fasciatus, 2 for Gammarus lacustris, and 6 for Gammarus pseudolimnaeus. Efforts focus on using interpretable structural parameters of organic compounds rather than computer-based descriptors, as outlined in the existing QSAR models. For the species G. fasciatus, G. lacustris, G. pseudolimnaeus, and G. pulex, the R² ratios for the new models versus the best QSAR models are 0.915/0.728, 0.955/0.747, 0.976/0.769, and 0.970/0.768, respectively. The higher R² values in the new models demonstrate greater reliability and robustness in capturing the data's underlying relationships.

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Comparison of intestinal toxicity in enhancing intestinal permeability and in causing ROS production of six PPD quinones in Caenorhabditis elegans

As the derivatives of p-phenylenediamines (PPDs), PPD quinones (PPDQs) have received increasing attention due to their possible exposure risk. We compared the intestinal toxicity of six PPDQs (6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ and IPPDQ) in Caenorhabditis elegans. In the range of 0.01-10 μg/L, only 77PDQ (10 μg/L) moderately induced the lethality. All the examined PPDQs at 0.01-10 μg/L did not affect intestinal morphology. Different from this, exposure to 6-PPDQ (1-10 μg/L), 77PDQ (0.1-10 μg/L), CPPDQ (1-10 μg/L), DPPDQ (1-10 μg/L), DTPDQ (1-10 μg/L), and IPPDQ (10 μg/L) enhanced intestinal permeability to different degrees. Meanwhile, exposure to 6-PPDQ (0.1-10 μg/L), 77PDQ (0.01-10 μg/L), CPPDQ (0.1-10 μg/L), DPPDQ (0.1-10 μg/L), DTPDQ (1-10 μg/L), and IPPDQ (1-10 μg/L) resulted in intestinal reactive oxygen species (ROS) production and activation of both SOD-3::GFP and GST-4::GFP. In 6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ, and/or IPPDQ exposed nematodes, the ROS production was strengthened by RNAi of genes (acs-22, erm-1, hmp-2, and pkc-3) governing functional state of intestinal barrier. Additionally, expressions of acs-22, erm-1, hmp-2, and pkc-3 were negatively correlated with intestinal ROS production in nematodes exposed to 6-PPDQ, 77PDQ, CPPDQ, DPPDQ, DTPDQ, and/or IPPDQ. Therefore, exposure to different PPDQs differentially induced the intestinal toxicity on nematodes. Our data highlighted potential exposure risk of PPDQs at low concentrations to organisms by inducing intestinal toxicity.

Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment

Quantitative structure-activity relationship (QSAR) is a cost-effective solution to directly and accurately estimating the environmental safety thresholds (ESTs) of pollutants in the ecological risk assessment due to the lack of toxicity data. In this study, QSAR models were developed for estimating the Predicted No-Effect Concentrations (PNECs) of petroleum hydrocarbons and their derivatives (PHDs) under dietary exposure, based on the quantified molecular descriptors and the obtained PNECs of 51 PHDs with given acute or chronic toxicity concentrations. Three high-reliable QSAR models were respectively developed for PHDs, aromatic hydrocarbons and their derivatives (AHDs), and alkanes, alkenes and their derivatives (ALKDs), with excellent fitting performance evidenced by high correlation coefficient (0.89-0.95) and low root mean square error (0.13-0.2 mg/kg), and high stability and predictive performance reflected by high internal and external verification coefficient (Q2LOO, 0.66-0.89; Q2F1, 0.62-0.78; Q2F2, 0.60-0.73). The investigated quantitative relationships between molecular structure and PNECs indicated that 18 autocorrelation descriptors, 3 information index descriptors, 4 barysz matrix descriptors, 6 burden modified eigenvalues descriptors, and 1 BCUT descriptor were important molecular descriptors affecting the PNECs of PHDs. The obtained results supported that PNECs of PHDs can be accurately estimated by the influencing molecular descriptors and the quantitative relationship from the developed QSAR models, that provided a new feasible solution for ESTs derivation in the ecological risk assessment.

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.