Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

Classification Model of Pesticide Toxicity in Americamysis bahia Based on Quantum Chemical Descriptors

A set of quantum chemical descriptors (molecular polarization, heat capacity, entropy, Mulliken net charge of the most positive hydrogen atom, APT charge of the most negative atom and APT charge of the most positive atom with hydrogen summed into heavy atoms) was successfully used to establish the classification models for the toxicity pLC50 of pesticides in Americamysis bahia. The optimal random forest model (Class Model A) yielded predictive accuracy of 100% (training set of 217 pesticides), 95.8% (test set of 72 pesticides) and 99.0% (total set of 289 pesticides), which were very satisfactory, compared with previous classification models reported for the toxicity of compounds in aquatic organisms. Therefore, it is reasonable to apply the quantum chemical descriptors associated with molecular structural information on molecular bulk, chemical reactivity and weak interactions, to develop classification models for the toxicity pLC50 of pesticides in A. bahia.

Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis

Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4'-Dichlorobiphenyl (PCB-15), 2,2',4,4',6,6'-Hexachlorobiphenyl (PCB-155), and 2,2',3,3',4,4',5,5',6,6'-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = -1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = -1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.

Pesticides removal from water using activated carbons and carbon nanotubes

The conventional water treatment technique (CT) widely applied cannot alone remove pesticides efficiently from water. Therefore, this work aimed to provide technical and scientific support for the association of pulverized activated carbon (PACs), granular activated carbon (GACs), and carbon nanotubes (CNT) with CT concerning atrazine (ATZ), simazine (SMZ), and diuron (DIU) removal. Actual conditions of pre/during, and post-treatment points of application, within water production process line, in water treatment plants (WTPs), using the pesticides in two forms, commercial product (CP) and analytical standard (SD). It was possible to demonstrate significant differences regarding the removal of ATZ, SMZ, and DIU in their SD and CP forms for the PACs, GACs, and CNTs. The minimum dosage of CNT required for adequate adsorption of all pesticides was superior to 160 mg. L-1; is 400% higher than the minimum dosage of 40 mg. L-1 is required for PAC application. ATZ, SMZ, and DIU in the SD form were more efficiently removed with percentages superior to 96.4% for ATZ, 98.2% for SMZ, and 99.1% for DIU. The characteristics of the adsorptive materials did not guide the adsorption efficacy. Instead, chemical interaction, contact time, and point application were critical factors. The pre-treatment and post-treatment applications were the most efficient, with removals oscillating from 97.7% to 100% for ATZ, 97.7% to 100% for SMZ, and 99.1 to 100% for DIU PAC and GAC, respectively.Highlights The pesticides forms of application, SD and CP, affect adsorption efficiency.Adsorbent point of application, in WTPLS, and contact time are key factors for pesticide removal.The primary adsorption mechanism in all the materials tested was chemical.The pre-treatment and post-treatment were the most efficient PACs and GACs application forms, respectively.

Hyaluronan nanoplatelets exert an intrinsic anti-inflammatory activity in a rat model of bladder painful syndrome/interstitial cystitis

Glycosaminoglycan (GAG) replenishment therapy consists of the instillation of GAG solutions directly in the bladder to alleviate Bladder Painful Syndrome/Interstitial Cystitis (BPS/IC). However, several issues were reported with this strategy because the GAG solutions are rapidly eliminated from the bladder by spontaneous voiding, and GAG have low bioadhesive behaviors. Herein, GAG nanomaterials with typical flattened morphology were obtained by a self-assembly process. The formation mechanism of those nanomaterials, denoted as nanoplatelets, involves the interaction of α-cyclodextrin cavity and alkyl chains covalently grafted on the GAG. Three GAG were used in this investigation, hyaluronan (HA), chondroitin sulfate (CS), and heparin (HEP). HA NP showed the best anti-inflammatory activity in an LPS-induced in vitro inflammation model of macrophages. They also exhibited the best therapeutic efficacy in a BPS/IC rat inflammation model. Histological examinations of the bladders revealed that HA NP significantly reduced bladder inflammation and regenerated the bladder mucosa. This investigation could open new perspectives to alleviate BPS/IC through GAG replenishment therapy.

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.

Research Progress on Natural Products’ Therapeutic Effects on Atrial Fibrillation by Regulating Ion Channels

Antiarrhythmic drugs (AADs) have a therapeutic effect on atrial fibrillation (AF) by regulating the function of ion channels. However, several adverse effects and high recurrence rates after drug withdrawal seriously affect patients’ medication compliance and clinical prognosis. Thus, safer and more effective drugs are urgently needed. Active components extracted from natural products are potential choices for AF therapy. Natural products like Panax notoginseng (Burk.) F.H. Chen, Sophora flavescens Ait., Stephania tetrandra S. Moore., Pueraria lobata (Willd.) Ohwi var. thomsonii (Benth.) Vaniot der Maesen., and Coptis chinensis Franch. have a long history in the treatment of arrhythmia, myocardial infarction, stroke, and heart failure in China. Based on the classification of chemical structures, this article discussed the natural product components’ therapeutic effects on atrial fibrillation by regulating ion channels, connexins, and expression of related genes, in order to provide a reference for development of therapeutic drugs for atrial fibrillation.

Prediction of Second-Order Rate Constants of Sulfate Radical with Aromatic Contaminants Using Quantitative Structure-Activity Relationship Model

Predicting the second-order rate constants between aromatic contaminants and a sulfate radical (kSO4•−) is vital for the screening of pollutants resistant to sulfate radical-based advanced oxidation processes. In this study, a quantitative structure-activity relationship (QSAR) model was developed to predict the values for aromatic contaminants. The relationship between logkSO4•− and three molecular descriptors (electron density, steric energy, and ratio between oxygen atoms and carbon atoms) was built through multiple linear regression. The goodness-of-fit, robustness, and predictive ability of the model were characterized statistically with indicators showing that the model was reliable and applicable. Electron density was found to be the most influential descriptor that contributed the most to logkSO4•−. All data points fell within the applicability domain, and no outliers existed in the training set. The comparison with other models indicates that the QSAR model performs well in elucidating the mechanism of the reaction between aromatic compounds and sulfate radicals.

Predicting anionic surfactant toxicity to Daphnia magna in aquatic environment: a green approach for evaluation of EC50 values

The median effective concentration (EC₅₀) is the concentration of a substance expected to produce a specific effect in 50% of the populations with a certain density under defined conditions. This parameter is expressed as an acute toxicity and is obtained via chemical toxicity testing. But, the laboratory work is time-consuming, expensive, and not eco-friendly. Therefore, to predict EC₅₀ for new anionic surfactants, a quantitative structure-activity relationship (QSAR) tool was studied for modeling the EC₅₀ of anionic surfactants on Daphnia magna based on the molecular descriptors. The best model (R² = 0.901 and F = 118.077, p<0.01) included 3 variables of the number of carbons, hydrogens, and the octanol-water partition coefficient logarithm. The main contribution to the toxicity was the octanol-water partition coefficient logarithm descriptor that had a negative effect on the toxicity of surfactants. The QSAR approach exhibited good results in predicting anionic surfactants EC₅₀, which allows the building of a simple, valid, and interpretable model that can be utilized as potential tools for rapidly predicting the lnEC50 of new or untested anionic surfactants to Daphnia magna.

Study on quantitative structure-biodegradability relationships of amine collectors by GFA-ANN method

In this study, the biodegradability of 17 amine collectors, categorized by fatty amine, quaternary ammonium compounds and oxygen-containing amine collectors, are tested with the Closed Bottle Test for 90 days, and the results indicate most amine collectors are not readily biodegradable. The oxygen-containing amine collectors have the best biodegradability due to the introduced oxygen-containing functional groups, subsequently fatty amine collectors with branched chains, while the tested quaternary ammonium compounds all have poor biodegradation ability. Besides, we search for and calculate 35 molecular descriptors to develop the quantitative structure biodegradability relationship (QSBR) of amine collectors. With the Genetic Function Approximation (GFA) algorithm, two sets of important molecular descriptors related to biodegradability (q) of amine collectors are selected from 35 molecular descriptors. Based on internal and external validations, the robust and reliable non-linear QSBR model with the squared correlation coefficient above 0.99 is determined via Artificial Neural Network (ANN) method, where the descriptors are respectively CL, N, E_LUMO, δv2, indicating the biodegradable ability of amine collectors is correlated with the alkyl chain lengths (CL), the number of nitrogen atom-containing compounds (N), energy of the lowest unoccupied molecular orbital (E_LUMO) and valence second-order connectivity index (δv2).

Validation Study of QSAR/DNN Models Using the Competition Datasets

Since the QSAR/DNN model showed predominant predictive performance over other conventional methods in the Kaggle QSAR competition, many artificial neural network (ANN) methods have been applied to drug and material discovery. Appearance of artificial intelligence (AI), which is combined various general purpose ANN platforms with large-scale open access chemical databases, has attracting great interest and expectation in a wide range of molecular sciences. In this study, we investigate various DNN settings in order to reach a high-level of predictive performance comparable to the champion team of the competition, even with a general purpose ANN platform, and introduce the Meister setting for constructing a good QSAR/DNNs model. Here, we have used the most commonly available DNN model and constructed many QSAR/DNN models trained with various DNN settings by using the 15 datasets employed in the competition. As a result, it was confirmed that we can constructed the QSAR/DNN model that shows the same level of R2 performance as the champion team. The difference from the DNN setting recommended by the champion team was to reduce the mini-batch size. We have also explained that the R2 performance of each target depends on the molecular activity type, which is related to the complexity of biological mechanisms and chemical processes observed in molecular activity measurements.