Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water

Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Environmental metabolomics uncovers oxidative stress, amino acid dysregulation, and energy impairment in Daphnia magna with exposure to industrial effluents

Anthropogenic activities are regarded as point sources of pollution entering freshwater bodies worldwide. With over 350,000 chemicals used in manufacturing, wastewater treatment and industrial effluents are comprised of complex mixtures of organic and inorganic pollutants of known and unknown origins. Consequently, their combined toxicity and mode of action are not well understood in aquatic organisms such as Daphnia magna. In this study, effluent samples from wastewater treatment and industrial sectors were used to examine molecular-level perturbations to the polar metabolic profile of D. magna. To determine if the industrial sector and/or the effluent chemistries played a role in the observed biochemical responses, Daphnia were acutely (48 h) exposed to undiluted (100%) and diluted (10, 25, and 50%) effluent samples. Endogenous metabolites were extracted from single daphnids and analyzed using targeted mass spectrometry-based metabolomics. The metabolic profile of Daphnia exposed to effluent samples resulted in significant separation compared to the unexposed controls. Linear regression analysis determined that no single pollutant detected in the effluents was significantly correlated with the responses of metabolites. Significant perturbations were uncovered across many classes of metabolites (amino acids, nucleosides, nucleotides, polyamines, and their derivatives) which serve as intermediates in keystone biochemical processes. The combined metabolic responses are consistent with oxidative stress, disruptions to energy metabolism, and protein dysregulation which were identified through biochemical pathway analysis. These results provide insight into the molecular processes driving stress responses in D. magna. Overall, we determined that the metabolic profile of Daphnia could not be predicted by the chemical composition of environmentally relevant mixtures. The findings of this study demonstrate the advantage of metabolomics in conjunction with chemical analyses to assess the interactions of industrial effluents. This work further demonstrates the ability of environmental metabolomics to characterize molecular-level perturbations in aquatic organisms exposed to complex chemical mixtures directly.

General structure-activity/selectivity relationship patterns for the inhibitors of the chemokine receptors (CCR1/CCR2/CCR4/CCR5) with application for virtual screening of PubChem database

CC chemokine receptors (CCRs) form a crucial subfamily of G protein-linked receptors that play a distinct role in the onset and progression of various life-threatening diseases. The main aim of this research is to derive general structure-activity relationship (SAR) patterns to describe the selectivity and activity of CCR inhibitors. To this end, a total of 7332 molecules related to the inhibition of CCR1, CCR2, CCR4, and CCR5 were collected from the Binding Database and analyzed using machine learning techniques. A diverse set of 450 molecular descriptors was calculated for each molecule, and the molecules were classified based on their therapeutic targets and activities. The variable importance in the projection (VIP) approach was used to select discriminatory molecular features, and classification models were developed using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN). The reliability and predictability of the models were estimated using 10-fold cross-validation, an external validation set, and an applicability domain approach. We were able to identify different sets of molecular descriptors for discriminating between active and inactive molecules and model the selectivity of inhibitors towards different CCRs. The sensitivities of the predictions for the external test set for the SKN models ranged from 0.827-0.873. Finally, the developed classification models were used to screen approximately 2 million random molecules from the PubChem database, with average values for areas under the receiver operating characteristic curves ranging from 0.78-0.96 for SKN models and 0.75-0.89 for CPANN models.Communicated by Ramaswamy H. Sarma.

Chelating agents supported solar photo-Fenton and sunlight/H2O2 processes for pharmaceuticals removal and resistant pathogens inactivation in quaternary treatment for urban wastewater reuse

In this work, Fe³⁺-iminodisuccinic acid (Fe:IDS) based solar photo Fenton (SPF), an Italian patented method, was investigated in quaternary treatment of real urban wastewater and compared to Fe³⁺-ethylenediamine-N,N'-disuccinic acid (Fe:EDDS) for the first time. Three pharmaceuticals (PCs) (sulfamethoxazole, carbamazepine and trimethoprim) and four pathogens (Escherichia coli, somatic and F-plus coliphages, Clostridium perfringens, consistently with the new EU regulation for wastewater reuse (2020/741)), were chosen as target pollutants. SPF with Fe:EDDS was more effective in PCs removal (80%, 10 kJ L^-1) than the SPF with Fe:IDS (58%), possibly due to the higher capability of generating hydroxyl radicals. On the contrary, Fe:IDS was more effective (4.3 log inactivation for E. coli) than Fe:EDDS (1.9 log) in pathogens inactivation, possibly due to a lower iron precipitation and turbidity which finally promoted an improved intracellular photo-Fenton mechanism. Fe:L based SPF was subsequently coupled to sunlight/H₂O₂. Interestingly, while its combination with Fe:EDDS based SPF slightly increased disinfectant efficacy (2.3 vs 1.9 log inactivation for E. coli), the combination with Fe:IDS decreased inactivation efficiency (3.4 vs 4.3 log reduction). In conclusion, due to the good compromise between PCs removal and disinfection efficiency, Fe:IDS SPF alone is an attractive option for quaternary treatment for urban wastewater reuse.

First report on ecotoxicological QSTR and I-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species

The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that nonpolar characteristics, electronegativity, hydrogen bonding, π-π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.

Application of Fe(III)–EDDS complexes and soybean peroxidase in photo-Fenton processes for organic pollutant removal: insights into possible synergistic effects

Photo-Fenton processes activated by biodegradable Fe(III)–EDDS complexes have attracted huge attention from the scientific community, but the operative mechanism of the photo-activation of H2O2 in the presence of Fe(III)–EDDS has not been fully clarified yet. The application of the Fe(III)–EDDS complex in Fenton and photo-Fenton (mainly under UV-B light) processes, using 4-chlorophenol (4-CP) as a model pollutant was explored to give insights into the operative mechanism. Furthermore, the potential synergistic contribution of soybean peroxidase (SBP) was investigated, since it has been reported that upon irradiation of Fe(III)–EDDS the production of H2O2 can occur. SBP did not boost the 4-CP degradation, suggesting that the possibly produced H2O2 reacts immediately with the Fe(II) ion with a quick kinetics that does not allow the diffusion of H2O2 into the bulk of the solution (i.e., outside the solvent cage of the complex). So, a concerted mechanism in which the photochemically produced H2O2 and Fe(II) react inside the hydration sphere of the Fe(III)–EDDS complex is proposed.

Graphical abstract

Toxicity prediction of 1,2,4-triazoles compounds by QSTR and interspecies QSTTR models

1,2,4-triazole derivatives exhibit various biological activities, including antibacterial and antifungal properties. On the other hand, these chemicals may have unique cumulative and harmful effects on living organisms. The goal of this work is to use quantitative structure-toxicity relationship (QSTR) and interspecies quantitative toxicity-toxicity relationship (iQSTTR) models to predict the acute toxicity of 1,2,4-triazole derivatives. The QSTR models were generated by multiple linear regression (MLR) following the OECD recommendations for QSAR model development and validation. The iQSTTR models were constructed using data on acute oral toxicity in rats and mice, as well as the 2D descriptor. The application domain (AD) analysis was used to identify model outliers and determine if the forecast was credible. Six QSTR models were successfully constructed in rats and mice using various delivery methods, and the scatter plots demonstrated excellent consistency across training and test sets. According to external and internal validation criteria, all six QSTR models may be broadly accepted; however, the orally administered mice model was the optimum one among the six species. Several chemicals with leverage values above the requirements were identified as response or structural outliers in the training sets for six QSTR and two iQSTTR models. All outliers, however, fell slightly outside the threshold or had low prediction errors, which may have had little impact on the capacity to forecast and were therefore preserved in the final models. In fact, neither the QSTR nor the iQSTTR test sets contained any response outliers. Additionally, all external and internal validation results for the iQSTTR models were approved, with the iQSTTR models outperforming the comparable QSTR models, which are deemed more dependable. The QSTR and iQSTTR models performed well in predicting toxicity using test sets, which would be beneficial in evaluating and synthesizing newly discovered 1,2,4-triazoles derivatives with low toxicity and environmental hazard.

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

Accelerate sulfamethoxazole degradation and detoxification by persulfate mediated with Fe2+&dithionite: Experiments and DFT calculation

Advanced oxidation process (AOPs) is one of the most effective technologies for organic pollutants removal. In this study, diverse reactive species generation and enhanced sulfamethoxazole (SMX) degradation were investigated based on persulfate (PDS) activated by Fe²⁺&dithionite (DTN). When involving Fe²⁺&dithionite in PDS, SMX degradation efficiency reached 84 % within 30 min following a pseudo-first-order kinetic, which was higher than those in Fe²⁺/PDS (50.4 %) and Fe²⁺/O₂/DTN (41.3 %). SO₄^•- and ^•OH were identified as dominant reactive species with a crucial role of FeSO₃⁺ based on quenching experiment and electron spin resonance (ESR). The contributions of SO₄^·-, ^·OH, and other species to SMX degradation were 60.1 %, 33.9 %, and 6 %, respectively. In Fe²⁺/DTN/PDS system, SMX was effectively degraded under nearly neutral pH (5.0-9.0), with activation energy of 96.04 kJ·mol^-1. The experiments and density functional theory (DFT) calculation demonstrated that three functional groups (benzenesulfonamido, benzene ring, and oxazole ring) were attacked for SMX degradation. Moreover, acute toxicity to Vibrio fischeri has enhanced in the earlier degradation process due to the intermediates and weaken with the continuous reaction. This work not only provides a high-activity SO₄^·--AOP for refractory pollutant treatment with possible dual radical generation resources, but elucidated diverse reactive species formation with Fe²⁺&dithionite.

Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica

Chemicals used in our daily life show different toxic effects to the aquatic and terrestrial species and thus hamper the ecological balance. In the present time, amphibians are one of them, which are threatened to be extinct. Quantitative structure-activity relationship (QSAR) is an useful tool for prediction involving less time, money and manpower without requiring any animal experiments to assess the unavailable acute toxicity data for the untested molecules. In this study, we have developed QSAR models for ecotoxicity of some waterborne diverse aromatic compounds on an amphibian species Rana japonica (Japanese brown frog) employing Genetic Algorithm (GA) for variable selection followed by Partial Least Squares (PLS) regression method following recommendations of the Organization for Economic Co-operation and Development (OECD) for QSAR model development. Double cross-validation (DCV) followed by Best Subset Selection (BSS) were employed to select suitable models. The models displayed promising statistical quality in terms of R² (= 0.837-0.841), Q²_LOO (= 0.782-0.787), R²_pred or Q²_F1 (= 0.802-0.82) and some other internal and external validation metrics for tadpoles of Rana japonica (N_Training = 44, N_Test = 14). These models can be applied for data gap filling for a new untested compound falling within the applicability domain (AD) of the models.

Prediction reliability of QSAR models: an overview of various validation tools

The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).

Chemometric modeling of plant protection products (PPPs) for the prediction of acute contact toxicity against honey bees (A. mellifera): A 2D-QSAR approach

Honey bees (Apis mellifera) are vital for economic, viable agriculture and for food safety. Although Plant Protection Products (PPPs) are of undeniable importance in the global agricultural system, these have become potential threats for non-target organisms like pollinators (e.g., honey bees etc.), resulting in the disruption of the ecological balance. In the current work, we have used the 113 PPP analogs to develop a 2D-QSAR model and explored the structural features modulating the toxic effects on honey bees, following the Organization for Economic Co-operation and Development (OECD) guidelines. The extensive validation of the developed model has been performed using internal and external validation metrics to make sure that the model is statistically sound and interpretable enough to be acceptable. The obtained results (R² = 0.666, Q² = 0.594, Q²_F1 = 0.647 and Q²_F2 = 0.646) determine the predictability and reliability of the developed model. This model should be useful for the predictions (acute contact toxicity (LD₅₀)) of the new and untested compounds located inside the applicability domain of the developed model. Moreover, we have performed the in-silico prediction of toxicity against honey bees of a total of 709 compounds obtained from the pesticide properties database (PPDB) using the developed model.

Acute toxicity of polychlorinated diphenyl ethers (PCDEs) in three model aquatic organisms (Scenedesmus obliquus, Daphnia magna, and Danio rerio) of different trophic levels

The frequent detection of polychlorinated diphenyl ethers (PCDEs) in aquatic systems has aroused widespread concerns, however, their potential hazard to aquatic ecosystems has been poorly understood. Here the acute toxicity of 12 PCDE congeners was evaluated in three model aquatic organisms representing different trophic levels following OECD test guidelines, including green algae (Scenedesmus obliquus), water flea (Daphnia magna), and zebrafish (Danio rerio). Dose-dependent increases in growth inhibition and mortality were observed for all tested PCDE congeners. Most of the PCDE congeners, in particular 3,3',4,4'-tetra-CDE, were highly toxic to the three aquatic organisms with EC₅₀ or LC₅₀ values below 1 mg L^-1. Their toxicities were generally comparable with those of certain polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). Moreover, D. magna was the most sensitive species among the three aquatic organisms. In addition, the EC₅₀ or LC₅₀ values had an extremely significant correlation with the n-octanol-water partition coefficient (logK_ow) of the PCDE congeners. The established quantitative structure-property relationship (QSPR) models indicated that the molecular polarizability (α) could significantly influence the acute toxicity of PCDEs on Daphnia magna and Danio rerio, and the energy of the lowest unoccupied molecular orbital (E_LUMO) is the key factor of the acute toxicity of PCDEs in Scenedesmus obliquus. In addition, even at environmental levels, 3,3',4,4'-tetra-CDE could induced seveve oxidative damages in the three aquactic species. These findings would contribute to the understanding of adverse effects of PCDEs in aquatic organisms.

Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies

In an effort to explore a class of novel antidiabetic agents, we have made an effort to synergize the α-amylase inhibitory potential of 1,3-benzothiazole and 1,3,4-oxadiazole scaffolds by combining the two into a single structure via an ether linkage. The structure of synthesized benzothiazole clubbed oxadiazole derivatives are established by different spectral techniques. The synthesized hybrids are evaluated for their in vitro inhibitory potential against α-amylase. Compound 8f is found to be the most potent with a significant inhibition (87.5 ± 0.74% at 50 μg/mL, 82.27 ± 1.85% at 25 μg/mL and 79.94 ± 1.88% at 12.5 μg/mL) when compared to positive control acarbose (77.96 ± 2.06%, 71.17 ± 0.60%, 67.24 ± 1.16% at 50 μg/mL, 25 μg/mL and 12.5 μg/mL concentration). Molecular docking of the most potent enzyme inhibitor, 8f, shows promising interaction with the binding site of biological macromolecule Aspergillus oryzae α-amylase (PDB ID: 7TAA) and human pancreatic α-amylase (PDB ID: 3BAJ). To a step further, in-depth QSAR studies show a significant correlation between the experimental and the predicted inhibitory activities with the best R_validation²= 0.8701. The developed QSAR model can provide ample information about the structural features responsible for the increase and decrease of inhibitory activity. The mechanistic interpretation of the structure-activity relationship (SAR) is done with the help of combined computational calculations i.e. molecular docking and QSAR. Finally, molecular dynamic simulations are performed to get an insight into the binding mode of the most potent derivative with α-amylase from A. oryzae (PDB ID: 7TAA) and human pancreas (PDB ID: 3BAJ).

Support vector machine-based model for toxicity of organic compounds against fish

Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC₅₀ of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R² above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC₅₀ of organic compounds against various fish species is successful.

Responses of Cirrhinus mrigala to second-generation fluoroquinolone (ciprofloxacin) toxicity: Assessment of antioxidants, tissue morphology, and inorganic ions

Ciprofloxacin drugs are a second-generation fluoroquinolone highly prescribed medication against various bacterial infections in human and aquaculture practices. These drugs are chemically designed to persist in the body long enough to achieve target objectives. Extensive usage has resulted in ciprofloxacin becoming a ubiquitous contaminant in the environment. Unfortunately, the ecotoxicological profiles for ciprofloxacin are scanty. This study was aimed to assess the ecotoxicity of ciprofloxacin at environmentally relevant concentrations (1 μg/L, and 1.5 μg/L) to a cultivable fish Cirrhinus mrigala. Responses of antioxidant enzymes, histological anomalies, and inorganic ion levels were studied. SOD activity in gill, liver, and kidney tissues was elevated in ciprofloxacin-exposed groups when compared with the control group. CAT activity was predominantly decreased in ciprofloxacin treated groups relative to the control group. GST activity in the ciprofloxacin treated groups was increased (except kidney tissues [Treatment I (1 μg/L)], and gill tissues fifteenth day) significantly (p < .05). The LPO level was elevated in the ciprofloxacin treatment groups throughout the study period (except Treatment II (1.5 μg/L) tenth day in kidney tissues). A series of histological anomalies were noticed in the gill, liver, and kidney tissues of the ciprofloxacin treated groups. Ciprofloxacin exposure caused a significant decrease of sodium, potassium, and chloride levels in the plasma of C. mrigala. A parallel among an imbalanced oxidative defense system, tissue structural changes, and alterations of plasma inorganic ions could be considered as a reliable biomarker for antibiotic toxicity study. This study could be a primary platform for further toxicity studies to understand the potential molecular impacts and adverse effects of ciprofloxacin on aquatic organisms.

Simultaneous removal of contaminants of emerging concern and pathogens from urban wastewater by homogeneous solar driven advanced oxidation processes

Simultaneous removal of contaminants of emerging concern and bacteria inactivation in simulated municipal wastewater effluent (SMWW) through solar advanced oxidation processes, namely sunlight/H₂O₂ and solar photo-Fenton with Ethylenediamine-N,N'-disuccinic acid (EDDS) at neutral pH was investigated. Process efficiency was evaluated in terms of (i) degradation of five contaminants of emerging concern (CECs, namely caffeine, carbamazepine, diclofenac, sulfamethoxazole and trimethoprim) at the initial concentration of 100 μgL^-1 each and (ii) bacteria inactivation (E. coli, S. enteritidis and E. faecalis), at the initial concentration of 10³ CFU mL^-1 each. Solar photo-Fenton process was first investigated at lab scale in a solar simulator to evaluate the effect of iron concentration (0.1 mM and 0.05 mM) and Fe:EDDS ratio (1:2 and 1:1). Subsequently, sunlight/H₂O₂ and solar photo-Fenton with EDDS (molar ratio 1:1, Fe(III) 0.1 mM) at neutral pH were singularly and sequentially investigated at pilot scale in a raceway pond reactor. Sunlight/H₂O₂ (50 mg L^-1) tests resulted in total bacteria inactivation in 60 min (0.69 kJ L^-1) but low CECs removal efficiency. On the opposite, solar photo-Fenton was effective in the removal of the total CECs (87% removal after 20 min and 0.14 kJ L^-1) but not in E. faecalis inactivation (the initial concentration did not change even after 180 min). However, when the two processes were operated sequentially, a complete bacteria inactivation was observed in 15 min (0.17 kJ L^-1), 20 min (0.23 kJ L^-1) and 60 min (0.70 kJ L^-1) of treatment for E. coli, S. enteritidis and E. faecalis, respectively and 80% removal of total CECs was achieved after 10 min of Fe:EDDS addition. Sequential combination of sunlight/H₂O₂ and solar photo-Fenton would be an effective solution for simultaneous CECs removal and bacteria inactivation in the same photo-reactor.

Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation

The present manuscript describes the synthesis, α-amylase inhibition, in silico studies and in-depth quantitative structure-activity relationship (QSAR) of a library of aroyl hydrazones based on benzothiazole skeleton. All the compounds of the developed library are characterized by various spectral techniques. α-Amylase inhibitory potential of all compounds has been explored, where compound 7n exhibits remarkable α-amylase inhibition of 87.5% at 50 µg/mL. Robust QSAR models are made by using the balance of correlation method in CORAL software. The chemical structures at different concentration with optimal descriptors are represented by SMILES. A data set of 66 SMILES of 22 hydrazones at three distinct concentrations are prepared. The significance of the index of ideality of correlation (IIC) with applicability domain (AD) is also studied at depth. A QSAR model with best Rvalidation2 = 0.8587 for split 1 is considered as a leading model. The outliers and promoters of increase and decrease of endpoint are also extracted. The binding modes of the most active compound, that is, 7n in the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) are also explored by in silico molecular docking studies. Compound 7n displays high resemblance in binding mode and pose with the standard drug acarbose. Molecular dynamics simulations performed on protein-ligand complex for 100 ns, the protein gets stabilised after 20 ns and remained below 2 Å for the remaining simulation. Moreover, the deviation observed in RMSF during simulation for each amino acid residue with respect to Cα carbon atom is insignificant.

The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.

Ecotoxicological assessment of chemotherapeutic agents using toxicity tests with embryos of Mellita quinquiesperforata

The consumption of anticancer agents has increased in the recent decades, and these substances may be present in sewage. Consequently, they may reach the environment when sanitation infrastructure is ineffective. This study evaluated the toxicity of three anticancer agents-Tamoxifen (TAM), Cisplatin (CisPt), and Cyclophosphamide (CP)-on the development of embryos of the sand-dollar Mellita quinquiesperforata. Adult individuals were collected in sandy beaches, and gametes were obtained. Freshly-fertilized eggs were exposed to increasing sets of concentrations of each compound, and the effective concentrations needed to cause a 50% effect in the organisms (EC50) were calculated. The three compounds were toxic, and their EC50 values were 16.78 ± 2.42 ng·L^-1 (TAM), 27.20 ± 38.26 ng·L^-1 (CisPt), and 101.82 ± 70.96 ng·L^-1 (CP). There is no information on the environmental levels of these compounds in Brazil, but as they were already detected in ng·L^-1 levels worldwide, it can be expected that these substances pose environmental risks to the marine biota.

Prediction of critical properties of sulfur-containing compounds: New QSPR models

In this study, new models have been proposed for the prediction of different critical properties (critical temperature (T_C), critical pressure (P_C), critical volume (V_C), and acentric factor (ω)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R²) were 0.936, 0.976, 0.990, and 0.917 for T_C, P_C, V_C, and ω, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded.

Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs)

Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q² = 0.648, R²_pred or Q²_F1 = 0.734 and Q²_F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC₁₀ of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R² of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R² of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC₁₀ shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC₅₀ of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.

Effects of lake water level fluctuation due to drought and extreme winter precipitation on mixing and water quality of an alpine lake, Case Study: Lake Arrowhead, California

Lake Arrowhead, an oligotrophic alpine lake in southern California, experienced a drought period from summer 2012 to winter 2018 followed by a season of intense storms in winter 2019 resulting in lake flooding. This study investigates the effects of seasonal variations combined with 3.5 m water level fluctuation from May 2018 to April 2019, on water quality and hydrodynamics of Lake Arrowhead. In-situ measured meteorological data and water quality profiles in five different bays were used to develop and calibrate a three-dimensional lake hydrodynamic model. The mean relative errors between simulated and measured temperature and salinity profiles were 6.1% and 4.2%, respectively. Root mean square errors between the measured and simulated water temperatures were slightly larger during the stratified period. However, no specific pattern was observed in error analysis of salinity simulations. Strong thermal stratification during summer and early-fall resulted in hypoxic hypolimnetic waters with dissolved oxygen (DO) concentrations of <1 mg L^-1. Turbulent kinetic energy (TKE) generated by convective motions in the water column due to surface heat loss was typically more than two times greater than the wind-induced mixing energy during the stratification period. The lake experienced an energetic turbulent mixing regime with TKE fluxes >1.5 m^-3 s^-3, and Lake numbers <0.1 during the winter cooling period, resulting in a complete water column turnover and resuspension of bottom sediments. Entrainment of the hypoxic hypolimnion layers and sediment resuspension resulted in decreased DO and pH in the water column from December 2018 through mid-January 2019. Comparisons of Wedderburn and Lake numbers during different stratification conditions indicated the same trends in the strong stratification period (square of buoyancy frequency >10^-4 s^-2). However, in other conditions, the Lake number, considering the lake bathymetry and density profile, could better reflect vertical mixing conditions.

Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species

In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R², cross-validation coefficient Q²_LOO (leave one out) while the test set predictions were analyzed using Q²F₁ (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE_95%train, average r_m(LOO)² and Δr_m(LOO)² (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE_95%test, average r_m² and Δr_m² (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity.

Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method

This study describes in silico designing of aptamers against the influenza virus using Monte Carlo method. Aptamers are short, single-stranded oligonucleotides and these bind to an ample range of biologically important proteins which are related to many disease conditions. The affinities and specificities of aptamers are comparable to antibodies. In the medicinal chemistry, quantitative structure-activity relationship (QSAR) is an important skill which is used for drug design and development. To study the inhibitory activity of aptamers, we have developed QSAR models based on Monte Carlo method. The nucleobase sequence descriptors B_k, BB_k and BBB_k are used to generate the QSAR models. A number of statistical benchmarks together with index of ideality of correlation (IIC) is considered to validate the build QSAR models. Data set of 98 aptamers is divided into four random splits. The statistical criteria R² = 0.8711 and CCC = 0.9207 of the validation set of split 3 are best, so the build QSAR model of split 3 is the paramount model. The aptamer fragment responsible for the promotors of endpoint increase and decrease are also determined. These fragments are applied to design new nine aptamers from the lead aptamer APT01.Communicated by Ramaswamy H. Sarma.

QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors

In the recent years, ecotoxicological hazard potential of biocidal products has been receiving increasing attention in the industries and regulatory agencies. Biocides/pesticides are currently one of the most studied groups of compounds, and their registration cannot be done without the empirical toxicity information. In view of limited experimental data available for these compounds, we have developed Quantitative Structure-Activity Relationship (QSAR) models for the toxicity of biocides to fish and Daphnia magna following principles of QSAR modeling recommended by the OECD (Organization for Economic Cooperation and Development). The models were developed using simple and interpretable 2D descriptors and validated using stringent tests. Both models showed encouraging statistical quality in terms of determination coefficient R² (0.800 and 0.648), cross-validated leave-one-out Q² (0.760 and 0.602) and predictive R²_pred or Q²_ext (0.875 and 0.817) for fish (n_Training = 66, n_Test = 22) and Daphnia magna (n_Training = 100, n_Test = 33) toxicity datasets, respectively. These models should be applicable for data gap filling in case of new or untested biocidal compounds falling within the applicability domain of the models. In general, the models indicate that the toxicity increases with lipophilicity and decreases with polarity, branching and unsaturation. We have also developed interspecies toxicity models for biocides using the daphnia and fish toxicity data and used the models for data gap filling.

Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis

Organic compounds (OCs) constitute an enormously large class of highly persistent and toxic chemicals widely used for various purposes throughout the world. Their increased detection in water bodies, mainly sewage treatment plants via industrial discharge, has rendered them to become a cause for ecological concern. The limited availability of experimental toxicological data has necessitated development of models that can help us identify the most hazardous and potentially toxic compounds thus prioritizing the experiments on the selected chemicals. Computational tools such as quantitative structure-activity relationship (QSAR) can be used to predict the missing data and classify the chemicals based on their acute predicted responses for existing as well as not yet synthesized chemicals. In the current study, novel, externally validated, highly robust local QSAR models for different chemical classes and moderately robust global QSAR models were developed using partial least squares (PLS) regression technique using a large dataset of 1121 OCs for the fish mortality endpoint. For feature selection, genetic algorithm along with stepwise regression was used while model validation was performed using various stringent validation criteria following the strict rules of OECD guidelines of QSAR validation. The variables included in the models were obtained from simplex representation of molecular structures (SiRMS) (Version 4.1.2.270), Dragon (Version 7.0) and PaDEL-descriptor software (Version 2.20). The final developed models were robust, externally predictive and characterized by a large chemical as well as biological domain. The predictive efficiency of the developed models was then compared with the ECOSAR tool in order to justify the applicability of the developed models in ecotoxicological predictions for organic chemicals. Better predictive efficiency of the developed QSAR models compared to the ECOSAR derived predictions signifies their applicability in early risk assessment of known as well as untested chemicals in order to design safer alternatives to the environment.

Chemometric modeling of Daphnia magna toxicity of agrochemicals

Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

Ecotoxicological QSAR modeling of endocrine disruptor chemicals

This study reports highly robust externally predictive quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models developed using toxicity data of endocrine disruptor chemicals (EDCs) towards 14 different species falling in four different trophic levels. Genetic algorithm followed by Partial Least Squares (PLS) regression was used in model development following the strict OECD guidelines. The models were developed using 2D descriptors having definite physicochemical meaning and validated by several internationally accepted validation metrics. The scope of predictions was defined by estimating applicability domain of the models. Presence of halogens, sulfur and phosphorus in the molecules greatly influenced the toxicity of EDCs as suggested by continuous repetition of 2D atom pair descriptors. Lipophilic contributions as calculated by logP terms (mainly ALOGP2 and XlogP) were the second most important feature controlling the EDC hazards. Hydrophilic moiety such as functionalities like esters, aliphatic ethers, branching and higher oxygen content reduced the EDC toxicity. Interspecies models were employed in data gap filling following the hierarchy of different species. The reliability of predictions was calculated by the "prediction reliability indicator" tool.