Activity Relationship Models Research Articles

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative’s Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R 2 of 0.9092, R 2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies.

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †.

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.

Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.

Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.

In pharmaceutical treatment, many non-cardiac drugs carry the risk of prolonging the QT interval, which can lead to fatal cardiac complications such as torsades de points (TdP). Although the unexpected blockade of ion channels has been widely considered to be one of the main reasons for affecting the repolarization phase of the cardiac action potential and leading to QT interval prolongation, the lack of knowledge regarding chemical structures in drugs that may induce the prolongation of the QT interval remains a barrier to further understanding the underlying mechanism and developing an effective prediction strategy. In this study, we thoroughly investigated the differences in chemical structures between QT-prolonging drugs and drugs with no drug-induced QT prolongation (DIQT) concerns, based on the Drug-Induced QT Prolongation Atlas (DIQTA) dataset. Three categories of structural alerts (SAs), namely amines, ethers, and aromatic compounds, appeared in large quantities in QT-prolonging drugs, but rarely in drugs with no DIQT concerns, indicating a close association between SAs and the risk of DIQT. Moreover, using the molecular descriptors associated with these three categories of SAs as features, the structure-activity relationship (SAR) model for predicting the high risk of inducing QT interval prolongation of marketed drugs achieved recall rates of 72.5% and 80.0% for the DIQTA dataset and the FDA Adverse Event Reporting System (FAERS) dataset, respectively. Our findings may promote a better understanding of the mechanism of DIQT and facilitate research on cardiac adverse drug reactions in drug development.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

QSAR model and microscopic mechanism analysis of dye removal by coagulation of aluminum chloride under alkaline conditions

Introduction: The inorganic coagulant AlCl3 is used in the traditional coagulation method for the decolorization of industrial dye wastewater. We studied its effectiveness in 41 kinds of dye with different structures, including azo, anthraquinone, arylmethane, and indigo dyes.Discussion: The optimal conditions for the removal of dye in the AlCl3 coagulation system were alkaline > neutral > acidic conditions. Under alkaline conditions, the hydrolysis colloid of AlCl3 is positively charged and easily combined with negatively charged anionic dyes by electrostatic adsorption. Therefore, the relationships between the dye removal behavior and molecular parameters under alkaline conditions were analyzed.Methods: Quantitative structure–activity relationship (QSAR) models were built for the color removal rates (Rexp) of 41 dyes and 46 molecular parameters computed by the density functional theory (DFT). Internal validation, external validation, statistical tests, Y-randomization, and applicability domain tests indicated that the optimal models are stable, accurate, reliable, and predictive.Results: The optimal QSAR model showed that surface area (approx.) (SAA) and molecular weight (MW) are two key molecular parameters. Moreover, electrostatic forces and hydrogen bonding are the predominant adsorption forces in this coagulation process.

2D-QSAR study and design of novel pyrazole derivatives as an anticancer lead compound against A-549, MCF-7, HeLa, HepG-2, PaCa-2, DLD-1

In this study, a local quantitative structure–activity relationship (QSAR) models were developed for set of compounds tested for their inhibitory activity against six different cancer cell lines viz. A-549, MCF-7, HeLa, HepG-2, PaCa-2 and DLD-1. Two different statistical approaches Principal Component Analysis (PCA) and Partial Least Square (PLS) analyses were employed to developed QSAR models. Further, activity predictions were carried out for in-house synthesized 63 pyrazole derivatives. Prediction of pIC50 value of all 63 synthesized pyrazole derivatives were estimated based on the most significant QSAR model developed for each cancer cell line. Several statistical parameters such as correlation coefficient R2, RMSE, Cross validated R2, Cross validated RMSE, internal validation Q2 and the external validation R2 revealed that developed models showed a significant value for explaining an acceptable QSAR model. The results derived highlighted some important compounds for being the most promise lead candidate against the six-cancer cell line with a significant pIC50 value. Considering the contribution of most important descriptors, we have designed new molecules which found to have greater inhibitory potentiality than the reference compounds. Overall, the results suggest that the developed QSAR models might be useful as a theoretical reference for experimental studies and designing more potent anti-cancer therapeutic pyrazoles based compounds.

A QSAR study to predict the survival motor neuron promoter activity of candidate diaminoquinazoline derivatives for the potential treatment of spinal muscular atrophy

ABSTRACT Spinal Muscular Atrophy is a genetic neuromuscular disease that leads to muscle weakness and atrophy and it is characterized by the loss of α-motor neurons in the spinal cord’s anterior horn cells. The disease appears due to low levels of the survival motor neuron protein. There are continuing clinical trials for the treatment of Spinal Muscular Atrophy. Quinazoline–based compounds are promising since they were tested on fibroblasts derived from the patients and found to increase the survival motor neuron protein levels. In this study, using multiple linear regression, we generated robust and valid quantitative structure- activity relationship models to predict the survival motor neuron–2 promoter activity of the new candidate compounds using the experimental survival motor neuron–2 promoter activity values of 2,4–diaminoquinazoline derivatives taken from the literature. The novel compounds designed by combining the pyrido[1,2–α]pyrimidin–4–one moeity of the known drug Risdiplam with that of 2,4 – diaminoquinazoline scaffold were predicted to exhibit strong promoter activities.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Structural investigation of carbon nanocone through topological coindices

AbstractTopological descriptors play a significant role in the selection of leading molecules for studies/drug design/research. As these descriptors equip the quantitative structure–activity relationship (QSAR) models with the physical and chemical properties of the compound, they have become alternative for traditional methods of screening the chemical libraries which is expensive and time consuming. The practicality of these descriptors in QSAR models have been largely demonstrated in the studies by various researchers globally. This study focuses on the chemical compound, carbon nanocone, for which nine degree based topological coindices are determined for such as first Zagreb coindex ), second Zagreb coindex (), Randic coindex (), reciprocal Randic coindex ), Forgotten coindex (), Atom bond connectivity coindex (), Geometric arithmetic coindex (), Sombor coindex (), SS coindex (). Accordingly, the structure of the nanocone is considered for , , , and for which numerical comparison is tabulated with respect to the graphs to understand the behavior of the coindices.

Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning

Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibitors, a QSAR classification model built using the PubChem fingerprint along with the extra trees algorithm achieved the best performance, reflected by the accuracy values of 0.933, 0.818, and 0.833 for the training, cross-validation, and test sets, respectively. For nonsteroidal inhibitors, a systematic cheminformatic analysis was applied for exploring the chemical space, Murcko scaffolds, and structure-activity relationships (SARs) for visualizing distributions, patterns, and representative scaffolds for drug discoveries. Furthermore, seven total QSAR classification models were established based on the nonsteroidal scaffolds, and two activity cliff (AC) generators were identified. The best performing model out of these seven was model VIII, which is built upon the PubChem fingerprint along with the random forest algorithm. It achieved a robust accuracy across the training set, the cross-validation set, and the test set, i.e., 0.96, 0.92, and 0.913, respectively. It is anticipated that the results presented herein would be instrumental for further CYP17A1 inhibitor drug discovery efforts.

Quantitative structure–activity relationship models for predicting apparent rate constants of organic compounds with ferrate (VI)

Ferrate (VI) (Fe (VI)) is a promising, environmentally friendly multifunctional oxidant widely applied in organic compound degradation. Oxidative kinetics of the apparent second-order rate constants (kapp) of Fe (VI) with organic compounds are critical for modeling oxidation processes. Herein, a quantitative structure–activity relationship (QSAR) model was developed using particle swarm optimization and an extreme learning machine to better understand the laws of the kapp values of organic compounds, including 33 aliphatic and aromatic hydrocarbon derivatives, during degradation by Fe (VI). Seven components—electronic hardness (H), electronic softness (S), ratio of oxygen to carbon atoms (On/Cn), energy of the highest occupied molecular orbital (EHOMO), vertical ionization potential (VIP), maximum nucleophilic reaction index (f(+)x), and minimum relative electrophilicity index (REn) constitute the critical molecular parameters. The developed QSAR model was verified on the basis of the coefficient of determination (R2) and the root mean square error (RMSE): for the training set, R2 = 0.924 and RMSE = 1.186, whereas for the test set, R2 = 0.996, and RMSE = 0.352. The applicability, reliability, and predictability of the model were verified by estimating the applicability domain (AD) of the model. Furthermore, QSAR models constructed using different methods were compared, and the main impact descriptors and conclusions obtained from previous studies were theoretically analyzed. Results indicate that constructing the QSAR model facilitates kapp prediction for Fe (VI) in the degradation of various organic compounds, improves the understanding of the degradation mechanism, and reduces the pressure on human and material resources caused by experiments.

Design, Synthesis, Herbicidal Activity, and Structure-Activity Relationship Study of Novel 6-(5-Aryl-Substituted-1-Pyrazolyl)-2-Picolinic Acid as Potential Herbicides.

Picolinic acid and picolinate compounds are a remarkable class of synthetic auxin herbicides. In recent years, two new picolinate compounds, halauxifen-methyl (ArylexTM active) and florpyrauxifen-benzyl (RinskorTM active), have been launched as novel herbicides. Using their structural skeleton as a template, 33 4-amino-3,5-dicholor-6-(5-aryl-substituted-1-pytazolyl)-2-picolinic acid compounds were designed and synthesized for the discovery of compounds with potent herbicidal activity. The compounds were tested for inhibitory activity against the growth of Arabidopsis thaliana roots, and the results demonstrated that the IC50 value of compound V-7 was 45 times lower than that of the halauxifen-methyl commercial herbicide. Molecular docking analyses revealed that compound V-7 docked with the receptor auxin-signaling F-box protein 5 (AFB5) more intensively than picloram. An adaptive three-dimensional quantitative structure-activity relationship model was constructed from these IC50 values to guide the next step of the synthetic strategy. Herbicidal tests of the new compounds indicated that compound V-8 exhibited better post-emergence herbicidal activity than picloram at a dosage of 300 gha-1, and it was also safe for corn, wheat, and sorghum at this dosage. These results demonstrated that 6-(5-aryl-substituted-1-pyrazolyl)-2-picolinic acid compounds could be used as potential lead structures in the discovery of novel synthetic auxin herbicides.

Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR

This research manuscript aims to find the most effective epidermal growth factor receptor (EGFR) inhibitors from millions of in house compounds through Machine Learning (ML) techniques. ML-based structure activity relationship (SAR) models were validated to predict biological activity of untested novel molecules. Six ML algorithms, including k nearest neighbour (KNN), decision tree (DT), Logistic Regression, support vector machine (SVM), multilinear regression (MLR), and random forest (RF), were used to build for activity prediction. Among these, RF classifier (accuracy for train and test set is 90% and 81%) and RF regressor (R2 and MSE for trainset is 0.83 and 0.29 and for test set, 0.69 and 0.46) showed good predictive performance. Also, the six most essential features that affect the biological activity parameter and highly contribute to model development were successfully selected by the variable importance technique. RF regression model was used to predict the biological activity expressed as pIC50 of nearly ten million molecules while RF classification model classifies those molecules into active, moderately active, and least active according to their predicted pIC50. Based on two models, thousand molecules from million molecules with higher predicted pIC50 values and classified as active were selected for molecular docking. Based on the docking scores, predicted pIC50, and binding interactions with MET769 residue, compounds, i.e., Zinc257233137, Zinc257232249, and Zinc101379788, were identified as potential EGFR inhibitors with predicted pIC50 7.72, 7.85, and 7.70. Dynamics studies were also performed on Zinc257233137 to illustrate that it has good binding free energy and stable hydrogen bonding interactions with EGFR. These molecules can be used for further research and proved to be the novel drugs for EGFR in cancer treatment. Communicated by Ramaswamy H. Sarma

Using weight of evidence to assess degradation potential of UVCB hydrocarbon solvents.

Hydrocarbon solvents are a diverse group of petrochemical substances that are identified as unknown or variable composition, complex reaction products, or biological materials (UVCBs) and may contain tens of thousands of individual chemical constituents. As such, it is generally not possible to analytically resolve every chemical constituent in a hydrocarbon solvent. This, along with the low water solubility and/or high vapor pressure of constituents, precludes the use of many standardized tests designed to determine biodegradation in the environment (e.g., Organization for Economic Co-operation and Development [OECD] 309). A weight of evidence approach may be needed to reduce uncertainty to an acceptable level such that a determination on the biodegradation of the substance can be drawn. Based on the OECD 2019 weight of evidence guidance, we present a framework using various lines of evidence that can be used to evaluate the biodegradation of a UVCB solvent in a weight of evidence approach. The lines of evidence includewhole substance testing, data on representative constituents, quantitative structure activity relationship (QSAR) models, and biological plausibility. Using these lines of evidence, "Hydrocarbon, C11-C14, normal alkane, isoalkane, cyclic, <2% aromatics" (EC# 926-141-6) was evaluated in a case study. Data from three whole substance tests, 43 constituents (representing 152 data points), three QSAR models and evidence of microbial degradation pathways were evaluated. Based on the available data, it is concluded that the solvent for the case study is not expected to persist in the environment. This framework sets out a real-world example of how the weight of evidence can be used to evaluate hydrocarbon solvents. While focused on persistence, similar approaches can be used to evaluate other endpoints such as bioaccumulation and toxicity. Integr Environ Assess Manag 2023;19:1120-1130. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Molecular Oxygen Activation by Citric Acid Boosted Pyrite–Photo–Fenton Process for Degradation of PPCPs in Water

Pyrite has been used in photo-Fenton reactions for the degradation of pollutants, but the application of photo-Fenton processes with extra H2O2 in real water/wastewater treatment has still been limited by the economic cost of H2O2 and artificial light sources. Herein, citric acid (CA) and simulated/natural sunlight are used to develop a pyrite-based photo-Fenton system (pyrite-CA-light) in situ generating H2O2 through the enhanced activation of molecular oxygen. The degradation of pharmaceuticals and personal care products (PPCPs), especially acetaminophen (APAP) as the main target pollutant, in the pyrite-CA-light system was investigated. The effects of influencing factors such as various organic acids, APAP concentration, pH, pyrite dosage, CA concentration and co-existing anions (HCO3-, Cl-, NO3-, SO42- and H2PO4-) were examined. At a pyrite dosage of 0.1 g L-1, CA concentration of 0.6 mM and an initial pH of 6.0, the degradation efficiency of APAP (30 μM) was 99.1% within 30 min under the irradiation of xenon lamp (70 W, λ ≥ 350 nm). Almost the same high efficiency of APAP degradation (93.9%) in the system was achieved under natural sunlight irradiation (ca. 650 W m-2). The scavenging experiments revealed that the dominant active species for degrading APAP was hydroxyl radical (HO•). Moreover, a quantitative structural-activity relationship (QSAR) model for pseudo-first-order rate constants (kobs) was established with a high significance (R2 = 0.932, p = 0.001) by using three descriptors: octanol-water partition coefficient (logKow), dissociation constant (pKa) and highest occupied molecular orbital (HOMO). This work provides an innovative strategy of the photo-Fenton process for the degradation of PPCPs using natural minerals and ordinary carboxylic acid under sunlight.

Rapid biodegradation of atrazine by a novel Paenarthrobacter ureafaciens ZY and its effects on soil native microbial community dynamic.

An atrazine-utilizing bacterium, designated as ZY, was isolated from agricultural soil and identified as Paenarthrobacter ureafaciens. The P. ureafaciens ZY demonstrated a significant degradation capacity of atrazine, with the degradation efficiency of 12.5 mg L-1 h-1 in liquid media (at pH 7, 30°C, and the atrazine level of 100 mg L-1). The P. ureafaciens ZY contained three atrazine-degrading genes (i.e., trzN, atzB, and atzC) could metabolize atrazine to form cyanuric acid, which showed lower biotoxicity than the parent atrazine as predicted by Ecological Structure Activity Relationships model. A laboratory-scale pot experiment was performed to examine the degradation of atrazine by P. ureafaciens ZY inoculation and investigate its effects on the native microbial communities. The results exhibited that the P. ureafaciens ZY was conductive to the degradation of atrazine, increased the total soil phospholipid fatty acids at the atrazine level of 50, 70, and 100 mg kg-1. By using high-throughput sequencing analysis, Frateuria, Dyella, Burkholderia-Caballeronia-Paraburkholderia were considered as the most important indigenous atrazine-degrading microorganisms due to their relative abundances were positively correlated with the atrazine degradation rate. In addition, P. ureafaciens ZY also increased the abundance of atrazine-degrading genus Streptomyces and Bacillus, indicating that there may be a synergic relationship between them in the process of atrazine degradation. Our work provides a new insight between inoculums and native microorganisms on the degradation of atrazine.

Exposure to high-performance benzotriazole ultraviolet stabilizers: Advance in toxicological effects, environmental behaviors and remediation mechanism using in-silica methods

Benzotriazole ultraviolet stabilizers (BUVSs), as light stabilizers, have attracted widespread attention because of their easy migration in the environment and their acute toxicity and biological toxicity effects, such as immunotoxicity and hepatotoxicity. Accordingly, the treatment and remediation mechanisms of high-performance, environmentally friendly, and low human health risk BUVS substitutes were analyzed. Firstly, the weights and the comprehensive effect (CE) values of migration and toxicity of BUVSs were determined by Topsis assisted by the coefficient of variation (CV) method. From this, a three-dimensional quantitative structure activity relationship (3D-QSAR) model based on the CE values of the 13 BUVSs was constructed. Secondly, EPI software was used to predict the functionality and environmental friendliness of BUVS substitutes, and a partial least squares regression machine learning (ML-PLSR) model was used to analyze the mechanism. Then, ADMET (absorption, distribution, metabolism, excretion, toxicity), TOPKAT, and exposure dose models were used to evaluate the ecological and human health risks of BUVSs and their substitutes. Finally, the key charge information affecting the UV-326 substitutes was deduced by time dependent density functional theory (TDDFT). Using UV-326 as an example, 15 UV-326 substitutes with reduced CE values were designed (reductions of 2.61%–23.18%). Compared with ML-PLSR models of acute toxicity, immunotoxicity, and hepatotoxicity, it was found that the decrease of DM and Qyy values and the increase of Qzz value could further decrease the toxicity of the UV-326 substitutes. Ecological and human health risk assessment showed that the exposure risks of the six UV-326 substitutes were within acceptable limits. TDDFT showed that the change of electron distribution and electron excitation type were the key factors affecting the performance of the UV-326 substitutes, and a charge transfer excitation type was more conducive to obtaining high-performance, environmentally friendly UV-326 substitutes. This study aims to alleviate the toxic damage to the ecological environment and human health caused by BUVS exposure.

In silico package models for deriving values of solute parameters in linear solvation energy relationships

ABSTRACT Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure–activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.