First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

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.

Risk assessment of industrial chemicals towards salmon species amalgamating QSAR, q-RASAR, and ARKA framework

The extensive use of industrial chemicals poses a serious threat to aquatic species such as the salmon species, which, when consumed, can affect human beings via their dietary intake. Salmon fish is a vital source of protein for maintaining human health. The present study aims to estimate the toxicity of diverse chemicals using in silico-based global model involving three different salmon species: Salmo salar, Oncorhynchus kisutch, and Oncorhynchus tshawytscha encompassing the toxicity endpoint median lethal concentration (LC50). Primarily, a quantitative structure-activity relationship (QSAR) model is developed using molecular descriptors. QSAR model descriptors are integrated with the similarity and error-based measures of read-across to develop the read-across structure-activity relationship (RASAR) model. Another emerging dimensionality reduction modeling algorithm, arithmetic residuals in K-groups analysis (ARKA) is employed to enhance the model's degree of freedom. Model quality was improved by hybrid model development which combined the feature matrix of the QSAR model with those of the RASAR and ARKA descriptors. Finally, to attain more trustworthy results and address the limitations of individual models, a partial least square (PLS)-based stacking model is developed using the predicted response values of QSAR, RASAR, ARKA, and hybrid models as descriptors. The stacking model outperforms the quality of the individual models which is evident from the determination coefficient R2 (0.713), leave-one-out cross-validated correlation coefficient (Q2 LOO:0.697), predictive R2 (Q2 F1 : 0.797), Q2 F2 (0.795) and lower value of root mean square error of prediction RMSEp (0.652). Additionally, classification modelling was performed with the feature matrix of the QSAR model by employing both linear and non-linear approaches. The developed stacking model can thus be used in environmental risk assessment aiding in toxicity data-gap filling and design of safe and green chemicals.

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.

Addressing data gaps in deriving aquatic life ambient water quality criteria for contaminants of emerging concern: Challenges and the potential of in silico methods

The international community is becoming increasingly aware of the threats posed by contaminants of emerging concern (CECs) for ecological security. Aquatic life ambient water quality criteria (WQC) are essential for the formulation of risk prevention and control strategies for pollutants by regulatory agencies. Accordingly, we systematically evaluated the current status of WQC development for typical CECs through literature review. The results revealed substantial disparities in the WQC for the same chemical, with the coefficients of variation for all CECs exceeding 0.3. The reliance on low-quality data, high-uncertainty derivation methods, and limited species diversity highlights a substantial data gap. Newly developed in silico methods, with potential to predict the toxicity of untested chemicals, species, and conditions, were classified and integrated into a traditional WQC derivation framework to address the data gap for CECs. However, several challenges remain before such methods can achieve widespread acceptance. These include unstable model performance, the inability to predict chronic toxicity, undefined model applicability, difficulties in specifying toxicity effects and predicting toxicity for certain key species. Future research should prioritize: 1) improving model accuracy by developing specialized models trained with relevant, chemical-specific data or integrating chemical-related features into interspecies models; 2) enhancing species generalizability by developing multispecies models; 3) facilitating the derivation of environmentally relevant WQC by incorporating condition-related features into models; and 4) improving the regulatory acceptability of in silico methods by evaluating the reliability of "black-box" models.

Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms

The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R2 = 0.727 and Q2(LOO) = 0.723) and external validation metrics (Q2F1 = 0.739, Q2F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.

Comparative QSAR and q-RASAR modeling for aquatic toxicity of organic chemicals to three trout species: O. Clarkii, S. Namaycush, and S. Fontinalis

Oncorhynchus clarkii, Salvelinus fontinalis, and Salvelinus namaycush are vital trout species in North America, crucial for maintaining ecological balance, economic stability, and human health. These species thrive in cold, unpolluted waters and are highly vulnerable to contaminants. Given the rapid proliferation of industrial organic chemicals, traditional in vivo toxicity testing methods are inadequate to ensure timely and comprehensive risk assessments. Therefore, we employed in silico tools, namely Quantitative Structure-Activity Relationship (QSAR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR), to efficiently predict the aquatic toxicity of chemicals. Utilizing acute median lethal concentration (LC50) data from the US EPA's ToxValDB, we developed the first-ever species-specific QSAR and q-RASAR models. The q-RASAR models outperformed traditional QSAR models by achieving higher internal and external statistical quality for each species. Key toxicity-determining descriptors included electrotopological state indices, autocorrelation descriptors, and similarity-based RASAR descriptors. For O. clarkii, the presence of chlorine atoms and rotatable bonds significantly influenced toxicity. S. fontinalis toxicity was strongly affected by polarizability, and van der Waals volumes, while S. namaycush showed sensitivity to weak hydrogen bond acceptors and topological complexity. The models predicted the toxicity of 1172 external compounds, identifying the most and least toxic chemicals for each species. This study not only offers the first comprehensive q-RASAR models for predicting trout species-specific toxicity but also provides novel insights into species-specific toxicological modes of action. The results contribute significantly to chemical screening and prioritization in aquatic risk assessments, effectively filling critical data gaps and advancing predictive modeling techniques.

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%-98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

How safe are wild-caught salmons exposed to various industrial chemicals? First ever in silico models for salmon toxicity data gaps filling

Salmons are crucial to ecosystems and economic activities like commercial fishing and aquaculture, while also serving as an important source of nutrients, underscoring their ecological significance and the need for sustainable management. To better understand the toxicity and biological interactions between the salmon and industrial chemicals in the aquatic environment, we utilized the ToxValDB database to develop first ever computational toxicity models for six salmon subspecies (covering Atlantic and Pacific salmon) across two genera, employing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) methods. For three smaller datasets (Oncorhynchus nerka, Oncorhynchus keta, and Oncorhynchus gorbuscha), we created mathematical models using the entire datasets where QSAR models demonstrated superior statistical quality compared to q-RASAR. Conversely, the three larger datasets (Oncorhynchus kisutch, Oncorhynchus tshawytscha, and Salmon salar) were divided into training and test sets, the q-RASAR models yielded better results compared to QSAR models. Mechanistic interpretations of these models revealed that descriptors such as Burden eigenvalues (BCUT), autocorrelation of topological structure (ATSC), and molecular polarizability were significant predictors of toxicity. For instance, higher polarizability and certain topological features were associated with increased toxicity as per the developed models. Statistically superior models for each subspecies were used to predict the aquatic toxicity of 1085 untested organic chemicals for toxicity data gap filling and risk assessment considering the applicability domain (AD). These insights are pivotal for designing safer chemicals and emphasize the need for sustainable management of salmon populations.

Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles

Metal oxide nanoparticles (MONPs) are widely used in medicine and environmental remediation because of their unique properties. However, their size, surface area, and reactivity can cause toxicity, potentially leading to oxidative stress, inflammation, and cellular or DNA damage. In this study, a nano-quantitative structure-toxicity relationship (nano-QSTR) model was initially developed to assess zebrafish toxicity for 24 MONPs. Previously established 23 first- and second-generation periodic table descriptors, along with five newly proposed third-generation descriptors derived from the periodic table, were employed. Subsequently, to enhance the quality and predictive capability of the nano-QSTR model, a nano-quantitative read across structure-toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR model, despite having one less MONP. This study highlights the effective utilization of the nano-qRASTR algorithm in situations with limited data for modeling, demonstrating superior goodness-of-fit, robustness, and predictability (R 2 = 0.81, Q 2 LOO = 0.70, Q 2 F1/R 2 PRED = 0.76) compared to simple QSTR models. Finally, the developed nano-qRASTR model was applied to predict toxicity data for an external dataset comprising 35 MONPs, addressing gaps in zebrafish toxicity assessment.

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762-0.844; Q2F2: 0.759-0.831 and MAEtest: 0.195-0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.