QSAR model for predicting the toxicity of organic compounds to fathead minnow

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.

Derivation of water quality criteria for paraquat, bisphenol A and carbamazepine using quantitative structure-activity relationship and species sensitivity distribution (QSAR-SSD)

The risk assessment of an expanding array of emerging contaminants in aquatic ecosystems and the establishment of water quality criteria rely on species sensitivity distribution (SSD), necessitating ample multi-trophic toxicity data. Computational methods, such as quantitative structure-activity relationship (QSAR), enable the prediction of specific toxicity data, thus mitigating the need for costly experimental testing and exposure risk assessment. In this study, robust QSAR models for four aquatic species (Rana pipiens, Crassostrea virginica, Asellus aquaticus, and Lepomis macrochirus) were developed using leave-one-out (LOO) screening variables and the partial least squares algorithm to predict toxicity data for paraquat, bisphenol A, and carbamazepine. These predicted data can be integrated with experimental data to construct SSD models and derive hazardous concentration for 5 % of species (HC5) for the criterion maximum concentration. The chronic water quality criterion for paraquat, bisphenol A, and carbamazepine were determined at 6.7, 11.1, and 3.5 μg/L, respectively. The QSAR-SSD approach presents a viable and cost-effective method for deriving water quality criteria for other emerging contaminants.

In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME/Tox, and molecular dynamics

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors.Communicated by Ramaswamy H. Sarma.

A novel procedure for predicting chronic toxicities and ecological risks of perfluorinated compounds in aquatic environment

Perfluorinated compounds (PFCs) can pose adverse effect on aquatic species and community structure. However, little is known about how the characteristics of molecules of PFCs affect their chronic toxic potencies to aquatic species, and the species sensitivity distributions (SSDs) and ecological risk assessments of PFCs are hampered by limited available data of chronic toxicity. In the present study, a novel procedure is proposed to obtain the ecological risk of PFCs using existing exposure concentrations of PFCs and SSDs integrated with the chronic toxicity prediction through robust QSAR models. The results showed that the energy of the lowest unoccupied molecular orbital (E_LUMO) exhibited the strongest correlation with the chronic toxicities of 15 PFCs (R² > 0.844, F > 16.206, p < 0.05). SSDs of 15 PFCs on eight species were first constructed, and the SSD fitting parameters were significantly correlated with E_LUMO (R² > 0.610, F > 19.471, p < 0.05). The QSAR-SSDs support the evaluation of hazardous criteria of PFCs for which data are lacking. Given environmental exposure distributions (EEDs) of the national presence of PFCs in aquatic systems in China, the QSAR-SSDs models allow the development of the ecological risk assessment for PFCs. This way, it was concluded that negligible environmental risk (defined as 5% of the species being potentially exposed to concentrations able to cause effects in < 5% of the case) could be expected from exposure to PFCs in surface waters in China. This method may be helpful for providing an evidence-based approach to guide the risk management for PFCs in aquatic environment.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Hazard assessment of beta-lactams: Integrating in silico and QSTR approaches with in vivo zebrafish embryo toxicity testing

Antibiotics have emerged as a well-known representative of pharmaceuticals and personal care products (PPCPs) by causing public health and environmental problems due to their potential toxicity. β-lactams are the most commonly used antibiotics in the world. This study used zebrafish embryos to evaluate the toxicity of β-lactams. The results showed that 23 β-lactam compounds induced malformation and death in a concentration-response manner. Moreover, this study established and validated quantitative structure-toxicity relationship (QSTR) models for the toxicity of β-lactams in zebrafish. These models performed well and fast in the prediction of the acute toxicity of β-lactams. Structural interpretation indicated that the β-lactam ring, the thiazolidine/dihydrothiazine rings, the side chains, and spatial configuration are the main factors responsible for the toxicity of β-lactams. The results from our previous studies and this study also revealed that the potential biological risks caused by β-lactams and their degradation products could not be ignored. This study provided important data for further environmental risk assessment of β-lactams and regulatory purposes.

Review of the toxic effects of ionic liquids

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

BACKGROUND

A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset.

RESULTS

In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools.

CONCLUSION

QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow

Regulatory frameworks require information on acute fish toxicity to ensure environmental protection. The experimental assessment of this property relies on a substantial number of fish to be tested and it is in conflict with the current drive to replace in vivo testing. For this reason, alternatives to in vivo testing have been proposed during the past years. Among these alternatives, there are Quantitative Structure-Activity Relationships (QSAR) that require the sole knowledge of chemical structure to yield predictions of toxicities. In this context, the OECD QSAR Toolbox is one of the leading QSAR tools for regulatory purposes that enables the prediction of fish toxicities. The aim of this work is to provide evidence about the predictive reliability of the automated workflow for predicting acute toxicity in fish which is embedded within this toolbox. The results herein presented show that the logic underpinning this automated workflow can predict with a reliability that, in the majority of cases, is comparable to inter-laboratory variability and, in a significant number of cases, is also comparable with intra-laboratory variability. Moreover, considerations on the toxic mode of action provided by the OECD tool proved to be helpful in refining predictions and reducing the number of prediction outliers.

Towards Rational Biosurfactant Design-Predicting Solubilization in Rhamnolipid Solutions

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logP_BS, measure of purity), solubilizate (logP_sol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R² _val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.

Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo

Zebrafish embryos are highly sensitive to toxicant exposure and have been used to evaluate the potential eco-toxicity caused by organic pollutants in the aquatic environment. This study was to develop four quantitative structure-activity relationship (QSAR) models based on norm descriptors for acute toxicity of different exposure times toward zebrafish embryo of organic compounds with various structures. Norm descriptors were obtained by calculating the norm index of the atomic distribution matrix, which was composed of atomic spatial distribution and atomic properties. These norm index-based QSAR models presented satisfactory results with R² of 0.8549, 0.9162, 0.8335 and 0.8119 for 48, 96, 120 and 132 h, respectively. Validation results including cross validation, external validation, Y-randomized test and applicability domain analysis indicated that the proposed models were stable, robust and reliable. Accordingly, these norm descriptors might be effective in predicting the acute toxicity of various organics to zebrafish embryos, which might be useful for evaluating the potential hazards of organic pollutants to aquatic environment.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics

The n-octanol/water partition coefficient (logK_ow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R² = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logK_ow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logK_ow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logK_ow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logK_ow value of a compound.

Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure-activity relationship prediction

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.

Norm Index-Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout

The aquatic ecological environment is being threatened from overuse of pesticides, and the aquatic toxicity toward rainbow trout (Oncorhynchus mykiss) plays a significant role in environmental risk assessment of agrochemicals. In the present study, 2 norm index formulas were developed, from which several norm descriptors were derived. A quantitative structure-activity relationship (QSAR) model was established for the prediction of acute toxicity (median lethal concentration) toward rainbow trout of various pesticides. Results indicated that the present QSAR model presented an R² of 0.8053. Meanwhile, internal validation (Q_LOO²  = 0.7606), external validation (R_training²  = 0.8011, R_testing²  = 0.8108), Y-randomization test, and applicability domain analysis further demonstrated the stability, reliability, and wide application domain of the present QSAR model. Accordingly, these norm descriptors might be applicable to the structures of pesticides for predicting the acute toxicity to aquatic organism. Environ Toxicol Chem 2020;39:352-358. © 2019 SETAC.

Ecotoxicity of ammonium chlorophenoxyacetate derivatives towards aquatic organisms: Unexpected enhanced toxicity upon oxygen by sulfur replacement

Phenoxyacetate herbicides, such as 2,4-D and MCPA, having a high toxicity to non-target organisms are commonly used for controlling broadleaf weeds in agriculture. However, novel and environmentally friendly analogs are constantly sought after. For this purpose, various substituents at the phenyl group have been tested to find the optimal balance between the potent herbicidal activity and safety for non-target species. In this work, we investigated the influence of the oxygen by sulfur replacement in the phenoxy moiety of ammonium chlorophenoxyacetates on the toxicity towards aquatic organisms, such as bacteria (Vibrio fischeri), water flea (Daphnia magna) and freshwater fish (Pimephales promelas) by determining experimental (Microtox® test - V. fischeri) and predicted (ACD Lab Percepta software - D. magna, P. promelas) EC₅₀/LC₅₀ values. The achieved results showed that in contrary to the literature observations, where O-compounds were more toxic than their S-analogs (urea/thiourea), the O/S replacement in chlorophenoxyacetate significantly increased ecotoxicity of the S-analogs (up to 11 times). Moreover, one- and two-substituted phenoxyacetates in the form of ammonium salts were less toxic to V. fischeri than the commercially available phenoxy herbicides in the acid form. The logP/logD values were also calculated to understand hydro/lipophilic nature of the investigated compounds and differences in their toxicity.