Insights into the silica scaling behaviors in membrane distillation and anti-scaling mechanism of functional polymers.

Silica scaling imposes a significant limitation on the efficacy of membrane distillation (MD) in the treatment of hypersaline wastewater. The complex dynamic behaviors of silica at the membrane-water-air interface and the poor understanding of molecular-level anti-scaling mechanism hampers the development of effective antiscalants for mitigating silica scaling in MD. Despite using functional polymers to prevent silica polymerization, the inhibition mechanisms are unclear. Here, the kinetic process of silica scaling during MD and the potential anti-scaling mechanism of poly-ethylenimine (PEI) were investigated at the molecular level via molecular dynamics simulations. The investigation reveals that silica scales were more likely to adhere to the water-PTFE interface with a free energy potential well of -40.0 kJ mol-1 than that of the water-air interface with a -11.4 kJ mol-1 potential well. Silica scales falling at the water-air interface also migrated on the water-air interface until captured by the PTFE membrane. In this work, a representative functional amino-rich polymer PEI was constructed as silica inhibitors and its scale inhibition mechanism was elucidated. Notably, the inclusion of PEI increased the free-energy barriers for the silica polymerization reaction from 72.0 kJ mol-1 to 86.1 kJ mol-1, compared to scenarios without the antiscalants. Moreover, quantitative structure-activity relationships (QSAR) model of ΔGwater-silica was developed to predict the anti-scaling efficiencies of typical antiscalants based on machine learning method. These findings provide valuable insights into enhancing the efficiency of silica scaling mitigation strategies.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs.

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

Benchmarking of BMDC assay and related QSAR study for identifying sensitizing chemicals.

The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle. This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method. The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.

Cytotoxicity of emerging halophenylacetamide disinfection byproducts in drinking water: Mechanism and prediction.

Halophenylacetamides (HPAcAms) have been identified as a new group of nitrogenous aromatic disinfection byproducts (DBPs) in drinking water, but the toxicity mechanisms associated with HPAcAms remain almost completely unknown. In this work, the cytotoxicity of HPAcAms in human hepatoma (HepG2) cells was evaluated, intracellular oxidative stress/damage levels were analyzed, their binding interactions with antioxidative enzyme were explored, and a quantitative structure-activity relationship (QSAR) model was established. Results indicated that the EC50 values of HPAcAms ranged from 2353 µM to 9780 µM, and the isomeric structure as well as the type and number of halogen substitutions could obviously induce the change in the cytotoxicity of HPAcAms. Upon exposure to 2-(3,4-dichlorophenyl)acetamide (3,4-DCPAcAm), various important biomarkers linked to oxidative stress and damage, such as reactive oxygen species, 8­hydroxy-2-deoxyguanosine, and cell apoptosis, exhibited a significant increase in a dose-dependent manner. Moreover, 3,4-DCPAcAm could directly bind with Cu/Zn-superoxide dismutase and induce the alterations in the structure and activity, and the formation of complexes was predominantly influenced by the van der Waals force and hydrogen bonding. The QSAR model supported that the nucleophilic reactivity as well as the molecular compactness might be highly important in their cytotoxicity mechanisms in HepG2 cells, and 2-(2,4-dibromophenyl)acetamide and 2-(3,4-dibromophenyl)acetamide deserved particular attention in future studies due to the relatively higher predicted cytotoxicity. This study provided the first comprehensive investigation on the cytotoxicity mechanisms of HPAcAm DBPs.

In-silico design of novel potential HDAC inhibitors from indazole derivatives targeting breast cancer through QSAR, molecular docking and pharmacokinetics studies.

Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via quantitative structure activity relationship (QSAR) and molecular docking simulations were adapted to 43 compounds of indazole derivatives with HDAC inhibition for anticancer activity against breast cancer. The QSAR models were built from multiple linear regression (MLR), and models predictability was cross-validated by leave-one-out (LOO) method. Based on these results, compounds C32, C26 and C31 from model 3 showed superior inhibitory activity with pIC50 of 9.30103, 9.1549 and 9.1549. We designed 10 novel compounds with molecular docking scores ranging from -7.9 to -9.3 kcal/mol. The molecular docking simulation results reveal that amino acid residues ILE1122 and PRO1123 play a significant role in bonding with 6CE6 protein. Furthermore, newly designed compounds P5, P2 and P7 with high docking scores of -9.3 kcal/mol, -8.9 kcal/mol and -8.8 kcal/mol than FDA-approved drug Raloxifene (-8.5 kcal/mol) and aid in establishment of potential drug candidate for HDAC inhibitors. The in-silico ADME functionality is used in the final phase to evaluate newly designed inhibitors as potential drug candidates. The results suggest that newly designed compounds P5, P2 and P7 can be used as a potential anti-breast cancer drug candidate.

Repurposing Phytochemicals against Breast Cancer (MCF-7) using Classical Structure-Based Drug Design.

BACKGROUND: The significant public health effect of breast cancer is demonstrated by its high global prevalence and the potential for severe health consequences. The suppression of the proliferative effects facilitated by the estrogen receptor alpha (ERα) in the MCF-7 cell line is significant for breast cancer therapy. OBJECTIVE: The current work involves in-silico techniques for identifying potential inhibitors of ERα. METHODS: The method combines QSAR models based on machine learning with molecular docking to identify potential binders for the ERα. Further, molecular dynamics simulation studied the stability of the complexes, and ADMET analysis validated the compound's properties. RESULT: Two compounds (162412 and 443440) showed significant binding affinities with ERα, with binding energies comparable to the established binder RL4. The ADMET qualities showed advantageous characteristics resembling pharmaceutical drugs. The stable binding of these ligands in the active region of ERα during dynamic conditions was confirmed by molecular dynamics simulations. RMSD plots and conformational stability supported the ligands' persistent occupancy in the protein's binding site. After simulation, two hydrogen bonds were found within the protein-ligand complexes of 162412 and 443440, with binding free energy values of -27.32 kcal/mol and -25.00 kcal/mol. CONCLUSION: The study suggests that compounds 162412 and 443440 could be useful for developing innovative anti-ERα medicines. However, more research is needed to prove the compounds' breast cancer treatment efficacy. This will help develop new treatments for ERα-associated breast cancer.

Integrated virtual screening and in vitro studies for exploring the mechanism of triterpenoids in Chebulae Fructus alleviating mesaconitine-induced cardiotoxicity via TRPV1 channel.

Background: In traditional Mongolian or Tibetan medicine in China, Chebulae Fructus (CF) is widely used to process or combine with aconitums to decrease the severe toxicity of aconitums. Researches in this area have predominantly focused on tannins, with few research on other major CF components for cardiotoxicity mitigation. The present study aimed to clarify whether triterpenoids can attenuate the cardiotoxicity caused by mesaconitine (MA) and investigate the mechanism of cardiotoxicity attenuation. Methods: Firstly, the pharmacophore model, molecular docking, and 3D-QSAR model were used to explore the mechanism of CF components in reducing the toxicity of MA mediated by the TRPV1 channel. Then three triterpenoids were selected to verify whether the triterpenoids had the effect of lowering the cardiotoxicity of MA using H9c2 cells combined with MTT, Hoechst 33258, and JC-1. Finally, Western blot, Fluo-3AM, and MTT assays combined with capsazepine were used to verify whether the triterpenoids reduced H9c2 cardiomyocyte toxicity induced by MA was related to the TRPV1 channel. Results: Seven triterpenoids in CF have the potential to activate the TRPV1 channel. And they exhibited greater affinity for TRPV1 compared to other compounds and MA. However, their activity was relatively lower than that of MA. Cell experiments revealed that MA significantly reduced H9c2 cell viability, resulting in diminished mitochondrial membrane potential and nuclear pyknosis and damage. In contrast, the triterpenoids could improve the survival rate significantly and counteract the damage of MA to the cells. We found that MA, arjungenin (AR), and maslinic acid (MSA) except corosolic acid (CRA) upregulated the expression of TRPV1 protein. MA induced a significant influx of calcium, whereas all three triterpenoids alleviated this trend. Blocking the TRPV1 channel with capsazepine only increased the cell viability that had been simultaneously treated with MA, and AR, or MSA. However, there was no significant difference in the CRA groups treated with or without capsazepine. Conclusion: The triterpenoids in CF can reduce the cardiotoxicity caused by MA. The MSA and AR function as TRPV1 agonists with comparatively reduced activity but a greater capacity to bind to TRPV1 receptors, thus antagonizing the excessive activation of TRPV1 by MA.

Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms.

Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.

Identification of apigenin-4'-glucoside as bacterial DNA gyrase inhibitor by QSAR modeling, molecular docking, DFT, molecular dynamics, and in vitro confirmation studies.

CONTEXT: It is well known that antibiotic resistance is a major health hazard. To eradicate antibiotic-resistant bacterial infections, it is essential to find a novel antibacterial agent. Hence, in this study, a quantitative structure-activity relationship (QSAR) model was developed using 43 DNA gyrase inhibitors, and 700 natural compounds were screened for their antibacterial properties. Based on molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, the top three leads viz., apigenin-4'-glucoside, 8-deoxygartanin, and cryptodorine were selected and structurally optimized using density functional theory (DFT) studies. The optimized structures were redocked, and molecular dynamic (MD) simulations were performed. Binding energies were calculated by molecular mechanics/Poisson-Boltzmann surface area solvation (MM-PBSA). Based on the above studies, apigenin-4'-glucoside was identified as a potent antibacterial lead. Further in vitro confirmation studies were performed using the plant Lawsonia inermis containing apigenin-4'-glucoside to confirm the antibacterial activity. METHODS: For QSAR modeling, 2D descriptors were calculated by PaDEL-Descriptors v2.21 software, and the model was developed using the DTClab QSAR tool. Docking was performed using PyRx v0.8 software. ORCA v5.0.1 computational package was used to optimize the structures. The job type used in optimization was equilibrium structure search using the DFT hybrid functional ORCA method B3LYP. The basis set was 6-311G (3df, 3pd) plus four polarization functions for all atoms. Accurate docking was performed for optimized leads using the iGEMDOCK v2.1 tool with a genetic algorithm by 10 solutions each of 80 generations. Molecular dynamic simulations were performed using GROMACS 2020.04 software with CHARMM36 all-atom force field.

Multistage in silico approach to identify novel quinoline derivatives as potential c-kit kinase inhibitors.

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of -9.30 kcal/mol and a Δ GBind value of -90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns.Communicated by Ramaswamy H. Sarma.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing.

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 µM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Screening of Oral Potential Angiotensin-Converting Enzyme Inhibitory Peptides from Zizyphus jujuba Proteins Based on Gastrointestinal Digestion In Vivo.

Plant proteins are a good source of active peptides, which can exert physiological effects on the body. Predicting the possible activity of plant proteins and obtaining active peptides with oral potential are challenging. In this study, the potential activity of peptides from Zizyphus jujuba proteins after in silico simulated gastrointestinal digestion was predicted using the BIOPEP-UWM™ database. The ACE-inhibitory activity needs to be further investigated. The actual peptides in mouse intestines after the oral administration of Zizyphus jujuba protein were collected and analyzed, 113 Zizyphus jujuba peptides were identified, and 3D-QSAR models of the ACE-inhibitory activity were created and validated using a training set (34 peptides) and a test set (12 peptides). Three peptides, RLPHV, TVKPGL and KALVAP, were screened using the 3D-QSAR model and were found to bind to the active sites of the ACE enzyme, and their IC50 values were determined. Their values were 6.01, 3.81, and 17.06 µM, respectively. The in vitro digestion stabilities of the RLPHV, TVKPGL, and KALVAP peptides were 82%, 90%, and 78%. This article provides an integrated method for studying bioactive peptides derived from plant proteins.

Theoretical rejection of fifty-four antineoplastic drugs by different nanofiltration membranes.

The rise of nanofiltration technologies holds great promise for creating more effective and affordable techniques aiming to remove undesirable pollutants from wastewaters. Despite nanofiltration's promising potential in removing antineoplastic drugs from liquid matrices, the limited information on this topic makes it important to estimate the rejection rates for a larger number of compounds, particularly the emerging ones, in order to preview the nanofiltration performance. Aiming to have preliminary estimations of the rejection rates of antineoplastic drugs by nanofiltration, 54 antineoplastic drugs were studied in 5 nanofiltration membranes (Desal 5DK, Desal HL, Trisep TS-80, NF270, and NF50), using a quantitative structure-activity relationship (QSAR) model. While this methodology provides useful and reliable predictions of the rejections of compounds by nanofiltration, particularly for hydrophilic and neutral compounds, it is important to note that QSAR results should always be corroborated by experimental assays, as predictions were confirmed to have their limitations (especially for hydrophobic and charged compounds). Out of the 54 studied antineoplastic drugs, 29 were predicted to have a rejection that could go up to 100%, independent of the membrane used. Nonetheless, there were 2 antineoplastic drugs, fluorouracil and thiotepa, for which negligible removals were obtained (<21%). This study's findings may contribute (i) to the selection of the most appropriate nanofiltration membranes for removing antineoplastic drugs from wastewaters and (ii) to assist in the design of effective treatment approaches for their removal.

QSAR Research of Novel Tetrandrine Derivatives against Human Hepatocellular Carcinoma.

BACKGROUND: The new tetrandrine derivative is an anti-human liver cancer cell inhibitor which can be used to design and develop anti-human-liver-cancer drugs. OBJECTIVE: A quantitative structure-activity relationship (QSAR) model was established to predict the physical properties of new tetrandrine derivatives using their chemical structures. METHODS: The best descriptors were selected through CODESSA software to build a multiple linear regression model. Then, gene expression programming (GEP) was used to establish a nonlinear quantitative QSAR model with descriptors to predict the activity of a series of novel tetrandrine chemotherapy drugs. The best active compound 31 was subjected to molecular docking experiments through SYBYL software with a small fragment of the protein receptor (PDB ID:2J6M). RESULTS: Four descriptors were selected to build a multiple linear regression model with correlation coefficients R2, R2CV and S2 with the values of 0.8352, 0.7806 and 0.0119, respectively. The training and test sets with a correlation coefficient of 0.85 and 0.83 were obtained via an automatic problem-solving program (APS) using the four selected operators as parameters, with a mean error of 1.49 and 1.08. Compound 31 had a good docking ability with an overall score of 5.8892, a collision rate of -2.8004 and an extreme value of 0.9836. CONCLUSION: The computer-constructed drug molecular model reveals the factors affecting the activity of human hepatocellular carcinoma cells, which provides directions and guidance for the development of highly effective anti-humanhepatocellular- carcinoma drugs in the future.

Evaluation of the Effects of Heteroaryl Ethylene Molecules in Combination with Antibiotics: A Preliminary Study on Control Strains.

The discovery of compounds with antibacterial activity is crucial in the ongoing battle against antibiotic resistance. We developed two QSAR models to design six novel heteroaryl drug candidates and assessed their antibacterial properties against nine ATCC strains, including Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and also Salmonella enterica and Escherichia coli, many of which belong to the ESKAPE group. We combined PB4, a previously tested compound from published studies, with GC-VI-70, a newly discovered compound, with the best cytotoxicity/MIC profile. By testing sub-MIC concentrations of PB4 with five antibiotics (linezolid, gentamycin, ampicillin, erythromycin, rifampin, and imipenem), we evaluated the combination's efficacy against the ATCC strains. To assess the compounds' cytotoxicity, we conducted a 24 h and 48 h 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on colorectal adenocarcinoma (CaCo-2) cells. We tested the antibiotics alone and in combination with PB4. Encouragingly, PB4 reduced the MIC values for GC-VI-70 and for the various clinically used antibiotics. However, it is essential to note that all the compounds studied in this research exhibited cytotoxic activity against cells. These findings highlight the potential of using these compounds in combination with antibiotics to enhance their effectiveness at lower concentrations while minimizing cytotoxic effects.

Multi-process regulation of novel brominated flame retardants: Environmentally friendly substitute design, screening and environmental risk regulation.

Novel brominated flame retardants (NBFRs), one of the most widely used synthetic flame-retardant materials, have been considered as a new group of pollutants that potentially affect human health. To overcome the adverse effects of NBFRs, a systematic approach for molecular design, screening, and performance evaluation was developed to generate environmentally friendly NBFR derivatives with unaltered functionality. In the present study, the features of NBFRs (long-distance migration, biotoxicity, bioenrichment, and environmental persistence) were determined and characterized by the multifactor comprehensive characterization method with equal weight addition, and the similarity index analysis (CoMSIA) model was constructed. Based on the three-dimensional equipotential diagram of the target molecule 2-ethylhexyl tetrabromobenzoic acid (TBB), 23 TBB derivatives were designed. Of these, 22 derivatives with decreased environmental impact and unaltered functional properties (i.e., flame retardancy and stability) were selected using 3D-QSAR models and density functional theory methods. The health risks of these derivatives to humans were assessed by toxicokinetic analysis; the results narrowed down the number of candidates to three (Derivative-7, Derivative-10, and Derivative-15). The environmental impact of these candidates was further evaluated and regulated in the real-world environment by using molecular dynamics simulation assisted by the Taguchi experimental design method. The relationship between the binding effects and the nonbonding interaction resultant force (TBB derivatives-receptor proteins) was also studied, and it was found that the larger the modulus of the binding force, the stronger the binding ability of the two. This finding indicated that the environmental impact of the designed NBFR derivatives was decreased. The present study aimed to provide a new idea and method for designing NBFR substitutes and to provide theoretical support for restraining the potential environmental risks of NBFRs.

Quantitative structure-activity relationship models for the reaction rate coefficients between dissolved organic matter and PPCPs.

To predict PPCPs' photolysis rate in natural aquatic environment, it is essential to grasp the reaction rates between DOM and PPCPs, yet there are few measured data and no prediction models for this important photochemical parameter. To address this, a reaction rate coefficient (αDOM) was defined to describe the apparent rate of DOM-involved photoreaction for PPCPs. The measured αDOM values for 40 PPCPs in 9 DOM samples varied dramatically, ranging from (-2.1 ± 0.1)× 1010 to (2.2 ± 0.1)× 1011 M-1 s-1. Then the quantitative structure-activity relationship (QSAR) models were developed using chemical and water quality descriptors via the random forest method. We initially separated positive and negative values by a classifier with an AUC value of 0.965, followed by the construction of regression models for positive and negative values, respectively, using a regressor. Positive models achieved satisfactory goodness-of-fit and predictive ability (R2adj=0.92 and Q2ext=0.86), while negative models demonstrated acceptable performance (R2adj=0.71 and Q2ext=0.70). Finally, a comprehensive photolysis model that incorporates the QSAR models for αDOM was established and the significance of water quality parameters was emphasized through sensitive analysis. This model enables more elaborate predictions of PPCPs' photolysis rates in various water samples, providing valuable assistance for forecasting PPCPs' environmental fate.

Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD.

Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade. The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Regression models were developed for each of the biological targets using "mljar-supervised". On their basis, virtual screenings of commercially available compounds derived from the ZINC15 database were performed. A common group of compounds placed within the top results was selected as potential novel chemotypes of multifunctional ligands. This study represents the first attempt to discover the potential MTDLs inhibiting three biological targets. The obtained results prove the usefulness of AutoML methodology in the identification of hits from the big compound databases.