Quantitative Structure-activity Relationship Analysis Research Articles

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy.

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

Computer prediction of toxicity of new S-alkyl derivatives of 1,2,4-triazole

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Oxygen vacancies-enriched spent lithium-ion battery cathode materials loaded catalytic membrane for effective peracetic acid activation and organic pollutants degradation

Advanced oxidation processes combined with membrane filtration technique offer a promising approach for pollution mitigation and catalyst recovery. Herein, a waste ternary lithium-ion battery cathode material of LiNixCoyMnzO2 (LNCM) loaded polytetrafluoroethylene (PTFE) membrane was synthesized for peracetic acid (PAA) activation (LNCM-PTFE/PAA) and 2,4,6-trichlorophenol (TCP) degradation. Such a novel membrane, with a catalyst loading of 10 mg (0.796 mg/cm2) of LNCM achieved 96.4 % removal of TCP (2 mg/L) within 20 min in neutral pH. The redox cycles of surface metals (such as Co3+/Co2+, Ni3+/Ni2+, and Mn4+/Mn3+/Mn2+) in spent LNCM efficiently enhance charge transfer and mediated PAA activation. And intrinsic oxygen vacancies in LNCM facilitated PAA adsorption and its cleavage. The resulting carbon-centered radicals (R-C•, CH3C(O)OO•) and 1O2 are identified as the primary reactive species that collaboratively participate in TCP degradation. Quantitative structure-activity relationship analysis demonstrated a substantial reduction in product toxicity. The successful practical application of the LNCM-PTFE/PAA membrane was exemplified by treating chlorophenol industrial wastewater. This study presents a new LNCM-PTFE/PAA catalytic membrane for high-efficiency water treatment and a novel perspective for green utilization of waste LNCM.

Identification of novel DNA gyrase inhibitor by combined pharmacophore modeling, QSAR analysis, molecular docking, molecular dynamics, ADMET and DFT approaches

DNA gyrase, an ATP-dependent enzyme, plays a critical role in DNA replication, transcription, and recombination in Mycobacterium tuberculosis (MTB). While fluoroquinolones are effective antibacterial agents targeting DNA gyrase, their clinical use is often limited due to side effects and the emergence of bacterial resistance. In this study, we developed a quantitative structure-activity relationship (QSAR) model to predict the anti-tubercular activity of Quinoline-Aminopiperidine derivatives targeting the DNA gyrase enzyme, using a dataset of 48 compounds obtained from the literature. The QSAR model was validated using both internal and external validation metrics. Model 4, the best predictive model, demonstrated a strong fit with an R² of 0.8393, an adjusted R² (R²adj) of 0.8010, and a lack of fit (LOF) parameter of 0.0626. The QSAR results revealed that DNA gyrase inhibition is significantly influenced by factors such as partition coefficient, molecular flexibility, hydrogen bonding potential, and the presence of fluorine atoms. Twelve quinoline-aminopiperidine derivatives were designed, and their anti-tubercular activity was predicted using QSAR model-4. These compounds were further assessed for pharmacokinetic properties, toxicity, and binding affinity to DNA gyrase. Pharmacophore modeling was also performed and validated using MOE software. The final pharmacophore model includes the features of two aromatic hydrophobic features, one hydrogen bond acceptor, and one hydrogen bond donor. The results indicated that designed compounds QA-3 and dataset compounds C-34 exhibit favorable drug-likeness properties. Molecular dynamics simulations confirmed the stable binding of compounds QA-3 and C-34 to the DNA gyrase protein, highlighting their potential as promising anti-tubercular agents. The developed QSAR Model-4 will facilitate the prediction of anti-tubercular activity in Quinoline-Aminopiperidine derivatives.

Verification of the interaction between human bitter taste receptor T2R46 and polyphenols; Computational chemistry approach

Recent studies have indicated that the activation of bitter taste receptors (T2R) expressed in gastrointestinal secretory cells has a regulatory effect on the secretion of gastrointestinal hormones. Polyphenols are known to be ingested at a daily intake of 5 g or more and commonly have a bitter taste. Consequently, the interaction between the bitter taste receptor T2R46 and 490 polyphenols was investigated using in silico simulation techniques. It was demonstrated that W883.32 and E2657.39 play a pivotal role in the recognition of polyphenols and known ligands by T2R46, with frequent interactions observed, particularly with flavonoids. The results of the quantitative structure-activity relationship (QSAR) analysis demonstrated a high degree of correlation (R2=0.9359) between polyphenols and T2R46 in a model that incorporated molecular interaction field regions and branching scales. Furthermore, known ligands were also found to fit this model (R2=0.9155). These findings suggest that polyphenols may act as T2R46 ligands.

Metabolite identification of emerging disinfection byproduct dibromo-benzoquinone in vivo and in vitro: Multi-strategy mass-spectrometry annotation and toxicity characterization

Halobenzoquinones (HBQs) are emerging disinfection byproducts (DBPs) of high toxicity and also are shared active toxic intermediates of multiple halogenated organic pollutants. Due to the strong oxidizing property and electrophilicity, HBQs exhibit extremely diverse metabolism pathways in organisms. The identification of toxic-decisive metabolites is pivotal, albeit challenging, for understanding the toxicity mechanisms of HBQs. We employed dibromo-benzoquinone (DBBQ) as a representative HBQ, and established a systematic analytical strategy using high-resolution mass spectrometry, which collectively coupled suspect screening (SS), mass defect filtering (MDF), product ion filtering (PIF), isotopic signature filtering (ISF), and molecular networking (MN). As a result, 20 biotransformation products of DBBQ were identified in vivo and in vitro, involving metabolism reactions such as hydroxylation, methylation, methoxylation, acetylation, sulfonation, glucuronidation, glutathionylation, dimerization, and conjugation with amino acids or fatty acids. Quantitative structure-activity relationship (QSAR) analysis and cytotoxicity experiments consistently demonstrated the significantly high toxicity of the fatty acid conjugate compared to the parent compound DBBQ and other metabolites, pinpointing the important role of the fatty acid conjugation in determining the metabolism and toxicity of HBQs. The research conducted a comprehensive evaluation of the metabolism of a typical HBQ with the combination of multiple analytical and toxicity characterization methods, therefore screen out the most important metabolism pathway of HBQs.

Aromatase as a novel target of parabens in human and rat placentas: 3D-quantitative structure-activity relationship and docking analysis

Aromatase (CYP19A1), a pivotal enzyme in the biosynthesis of estradiol from testosterone, is predominantly expressed in reproductive tissues including placentas. This study investigated the effects of paraben acid and nine parabens on the activity of human and rat CYP19A1 using microsomes derived from human and rat placentas and on estradiol secretion in human choriocarcinoma BeWo cells. The results showed that propyl, butyl, hexyl, heptyl, and nonyl parabens significantly inhibited human CYP19A1 activity, with IC50 values of 66.37, 61.08, 55.65, 48.26, and 27.24 μM, respectively. In BeWo cells, these parabens notably diminished estradiol secretion at concentrations of 100 μM. Similarly, rat CYP19A1 was inhibited by these parabens, with IC50 values of 98.07, 70.10, 41.30, 27.93, and 6.33 μM for propyl, butyl, hexyl, heptyl, and nonyl parabens, respectively. Kinetic analysis identified these compounds as mixed inhibitors. Bivariate correlation analysis revealed a negative correlation between the partition coefficient value, molecular weight, the number of carbon atoms in the alcohol moiety, as well as heavy atom number and IC50 values. Three-dimensional quantitative structure-activity relationship analysis highlighted the critical role of hydrophobic regions in determining inhibitory potency. Docking studies suggested that parabens interact with the heme-iron binding site of both human and rat CYP19A1. This study elucidates the inhibitory effects of various parabens on CYP19A1 and their binding mechanisms, thereby providing a deeper understanding of their potential impact on estrogen biosynthesis.

Photocatalytic hydrogen production and sulfamerazine degradation via a novel dual S-scheme photocatalyst: Nanocomposite synthesis, characterization and mechanism insights

Creating highly effective photocatalysts is crucial for harnessing solar energy to degrade pollutants and produce hydrogen (H₂). In this study, we successfully synthesized a novel dual S-scheme iron oxide (Fe₂O₃)/bismuth oxide (Bi₂O₃)/titanium dioxide (TiO₂) ternary photocatalyst using a straightforward method. This photocatalyst was employed for efficient photocatalytic water splitting and the degradation of the antibiotic sulfamerazine (SMZ) under visible light. Various characterization and photoelectrochemical techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), Brunauer–Emmett–Teller surface area analysis (BET), photocurrent measurements, Mott-Schottky analysis, photoluminescence (PL), electrochemical impedance spectroscopy (EIS), and electron spin resonance (ESR), were utilized to analyze the synthesized materials. Among the synthesized nanocomposites, the 15 wt% Fe₂O₃/Bi₂O₃/TiO₂ (15FeBi/TiO₂) composite demonstrated exceptional photocatalytic efficiency, achieving 98 % SMZ degradation and a hydrogen production rate of 590.36 μmol/g·h. Experimental results, including scavenging tests and ESR findings, highlighted the crucial role of hydroxyl radicals (•OH) and superoxide radicals (•O₂−) in the photocatalytic process. Moreover, liquid chromatography-mass spectrometry (LC-MS) results proposed three degradation pathways, and quantitative structure-activity relationship (QSAR) analysis showed that the toxicity of intermediates was effectively reduced. The 15FeBi/TiO₂ photocatalyst also exhibited excellent reusability, retaining about 85 % of its initial activity after five cycles, and proved effective against various pollutants and in real water matrices. This research contributes to the design and development of high-activity heterojunction photocatalysts for superior clean energy generation and pollutant degradation under visible light.

Computational Study of Coumarin Compounds as Potential Inhibitors of Casein Kinase 2: DFT, 2D-QSAR, ADMET and Molecular Docking Investigations

Casein kinase 2 (CK2) is an ubiquitous, essential, and highly pleiotropic protein kinase whose abnormally high constitutive activity is suspected to underlie its pathogenic potential in neoplasia and other diseases. Recently, there has been a notable increase in interest in the use of casein kinase 2 (CK2) inhibitors to improve the treatment of a specific form of cancer while minimizing the risk of undesirable side effects. Recently, using different virtual screening approaches, we have identified several novel CK2 inhibitors. In particular, we have discovered that the coumarin moiety can be considered an attractive CK2 inhibitor scaffold. In the present work, quantitative structure-activity relationship (QSAR) analysis has been employed to envisage the inhibitory effects of 32 coumarin derivatives on the CK2 protein. The most efficient model is found by using multiple linear regression (MLR). Its capability is considered by the external and internal validation values found (R2 = 0.884, Q2cv = 0.822, R2pred = 0.821, and R2p = 0.811), which aligned well with Tropsha and Golbraikh’s approach. The highest docking score founded for the newly designed coumarins is −7.50 kcal mol-1, which indicates that candidates can bind to the CK2 receptor with greater affinity. Based on the results of the ADMET properties and drug similarity analyses, a DFT investigation was conducted to confirm the stability of the newly explored compounds. It appears that the most stable complexes are those of compound with the highest binding affinity with a lower risk of toxicité.

Diversity Oriented Strategy (DOS) for the Efficient Synthesis of Benzofuro[2,3-b]pyridine Derivatives with Anticancer Activity.

Benzofuropyridines (BFP) are polycyclic compounds with known applications in neuronal diseases. However, its derivatization patterns and anticancer potential remains unexplored. Leveraging the idea of diversity-oriented synthesis (DOS), we developed a highly efficient synthetic route for BFP to increase the library of available analogs producing three compounds in one reaction set up, including the 2O-, 6O-, and the 1N-substituted species, also producing the unusual 2-pyridone derivatives. Key bromination reaction of the BFP moiety was successfully described which can widen the available variation in the compounds' structure. The cytotoxic activity of the compounds was assessed against SH-SY5Y (neuroblastoma), HepG2 (hepatocellular carcinoma), Kb (human oral epidermoid), HeLa (cervical) and MCF-7 (breast) cancer cell lines. In the series, the m-bromobenzyl (5b), methylcyano (5g) and propargyl (5h) 2O-derivatives demonstrated good selectivity against cancer cells with selectivity index (SI) of >71 for 5g against HeLa over the normal cells, as compared to the standard drug, Doxorubicin (SI = 6.7). The quantitative structure-activity relationship (QSAR) analysis revealed an impressive correlation of the defined descriptors with the bioactivity having an R2 value of 0.971 and 0.893 for Kb and HeLa respectively. Altogether, our work highlighted new information on the synthesis of BFP derivatives with potent cytotoxic activity.

Interpretable discovery of patterns in tabular data via spatially semantic topographic maps.

Tabular data-rows of samples and columns of sample features-are ubiquitously used across disciplines. Yet the tabular representation makes it difficult to discover underlying associations in the data and thus hinders their analysis and the discovery of useful patterns. Here we report a broadly applicable strategy for unravelling intertwined relationships in tabular data by reconfiguring each data sample into a spatially semantic 2D topographic map, which we refer to as TabMap. A TabMap preserves the original feature values as pixel intensities, with the relationships among the features spatially encoded in the map (the strength of two inter-related features correlates with their distance on the map). TabMap makes it possible to apply 2D convolutional neural networks to extract association patterns in the data to aid data analysis, and offers interpretability by ranking features according to importance. We show the superior predictive performance of TabMap by applying it to 12 datasets across a wide range of biomedical applications, including disease diagnosis, human activity recognition, microbial identification and the analysis of quantitative structure-activity relationships.

Steiner 3-Wiener Index of Zigzag Polyhex Nanotubes.

Let G be a connected graph and S be a k element subset of the vertex set V(G) of G. Steiner distance is a natural generalization of the usual graph distance. The Steiner-k distance dG(S) between the vertices of S is the minimum size among all connected subgraphs whose vertex set contains S. The generalized indices based on Steiner distances have several applications in the real world. It is a well-known fact that "Steiner Problem" is NP-complete and hence any parameter based on Steiner distance is also an NP problem. The objective of this work is to determine the Steiner 3-Wiener index for an important chemical structure called the zigzag polyhex nanotube. In this paper, we present an algorithm for computing the Steiner 3-Wiener index (SW3) for zigzag polyhex nanotubes. The developed algorithm addresses the complexities associated with exponential increments in the number of vertices as the nanotube's circumference or length expands. The obtained SW3 values for zigzag polyhex nanotubes can be used for Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) analyses. We have presented an algorithm and listed the numerical values of SW3 for various parameters of circumference and length of a zigzag polyhex nanotube to facilitate their utilization in QSAR/ QSPR analyses. We have obtained the time complexity for the algorithm, which shows that the SW3 values are computationally intensive. To explain this complex nature, we have used multiple linear regression to fit log SW3 values corresponding to log p and log q, the radius and length of a nanotube. The algorithm addresses the complexities associated with the exponential increase in vertices as the nanotube's circumference or length expands. Furthermore, we provide SW3 values for various parameter combinations up to circumference or length 17, and a general relation to determine the value of SW3, facilitating its utilization in real-world applications. These values serve as crucial descriptors for understanding the structural nuances of zigzag polyhex nanotubes, that find applications in material science, drug design, drug discovery, etc.

In-situ nitrogen-doped porous carbon derived from penicillin mycelial residue for CO2 adsorption and adsorption mechanism investigation

The in-situ nitrogen-doped porous carbon materials was synthesized using penicillin mycelial residue (PMR) as the sole endogenous nitrogen-rich precursor through a two-step process applied for CO2 adsorption. Precise control over the structural properties and CO2 adsorption performance of the porous carbon was achieved by varying the KOH dosage and activation temperature. The porous carbon prepared under mild activation conditions (600 ℃, KOH/BC = 1) exhibited high nitrogen content and abundant ultramicroporous structures (pore size ≤ 0.7 nm), and demonstrated outstanding adsorption performance. The CO2 adsorption capacities reached 6.55 mmol/g at 0 ℃ and 3.48 mmol/g at 25 ℃, showing promising CO2/N2 selectivity and robust cyclic adsorption stability. Quantitative structure–activity relationship analysis displayed a strong correlation between adsorption density, CO2/N2 selectivity, and nitrogen content. Density functional theory calculations further confirmed that nitrogen-doping, especially pyrrolic-N, serves as favorable structural sites for CO2 adsorption. Additionally, Grand Canonical Monte Carlo simulations verified that the ultramicroporous structures significantly enhances CO2 adsorption due to their strong affinity for CO2. This study provides an economic strategy for the preparation of in-situ nitrogen-doped porous carbon with enriched micropore using PMR, highlighting their promising performances in CO2 adsorption, synchronously extending the harmless treatment and resource utilization of PMR to demonstrate a sustainable approach for environmental remediation.

New binary mixtures of fungicides against Macrophomina phaseolina: Machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation

Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q2LMO of 0.81, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts.

Genotoxicity of beauvericin

Abstract The European Commission (EC) asked EFSA to assess the genotoxicity of beauvericin (BEA). Relevant information, including that which has become available since the 2014 Scientific Opinion on the risks to human and animal health related to the presence of BEA and enniatins in food and feed, was reviewed. In the previous Opinion the Panel concluded that in vitro genotoxicity data were equivocal and there were no in vivo genotoxicity data available. New in vitro studies in mammalian cell lines provided no convincing evidence for induction of chromosomal damage by BEA as measured by micronucleus and chromosome aberration tests or an increase of DNA strand breaks as assessed by the Comet assay. In these studies, no concentration‐dependent effects or potential for interference from associated cytotoxicity were observed. In addition, DNA double‐strand breaks as measured by γ‐H2AX analysis were only observed following exposure to highly cytotoxic BEA concentrations. In vivo studies (Comet and Pig‐a assays, micronucleus test) with BEA were negative. In vitro gene expression studies showed no indication of a DNA damage response and (quantitative) structure activity relationship analysis was also not indicative of genotoxic potential. Some effects of BEA might play an indirect role in the formation of DNA strand breaks. These include increased reactive oxygen species, induction of cell cycle arrest and apoptosis, associated with interference in mitochondrial function and cell signalling. There was no compelling evidence of inflammatory and immunosuppressive effects. Taken together, the available data indicate that BEA is devoid of genotoxic potential.

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%–98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

Selective elimination of organic pollutants and analysis of effects and novel mechanisms of aged microplastics on wavelength-dependent UV-LED/H2O2 system

The selective removal of organic pollutants and potential impact of aged microplastics (MPs) as emerging pollutants in wavelength-dependent UV-LED/H2O2 system are not fully understood. This study found that cefalexin (CFX) degradation efficiency in UV-LED alone system was highly correlated with its UV molar absorbance (R2=0.994), while in UV-LED/H2O2 system, it was correlated with ·OH yield (R2=0.991) across various wavelengths. Quantitative structure-activity relationship (QSAR) analysis showed selective degradation of six pollutants based on their e--donating capabilities (R2=0.748–0.916). The coexistence of aged MPs, introducing C-O/C=O groups and rearranging their surface e-, potentially affected the elimination efficiency of CFX. Aged polystyrene (PS) decreased the degradation efficiency of CFX by shorting the O-O bond length (lO-O) in H2O2 and capturing e- from H2O2, whereas aged polyethylene (PE), polypropylene (PP) and polyvinyl chloride (PVC) had negligible effects as the lO-O elongation balanced the e--donating effect of H2O2. Additionally, phenol released from aged PS, with strong nucleophilicity, competing with CFX for ·OH, further decreasing CFX degradation efficiency. This study provides valuable insights into organic pollutant selective removal and reveals a novel inhibitory mechanism of aged PS on the performance of UV-LED/H2O2 technology.

Evaluating the behavior and environmental risks of carbamazepine and its metabolites in soil aquifer treatment: Insights from deconjugation dynamics and toxicity assessment

The presence of pharmaceuticals in the environment has been a growing concern. Recent studies highlight the ecological risks of pharmaceuticals, but most risk assessments focus on the parent drug, neglecting metabolites. This study examines the behavior and environmental risks of carbamazepine (CBZ) and its metabolites in soil aquifer treatment (SAT) for wastewater reclamation. Findings indicate that CBZ metabolites' total concentration exceeds that of CBZ. Notably, carbamazepine-N-glucuronide (CBZ-N-Glu) concentration decreased from 48.12 ng/L to undetectable levels during SAT, while CBZ concentration increased from 64.87 to 95 ng/L, suggesting possible deconjugation of CBZ-N-Glu. Batch and column experiments confirmed the hypothesis, showing a gradual disappearance of CBZ-Glu and a corresponding rise in CBZ concentration when CBZ-N-Glu was spiked into a recirculated SAT system. Quantitative structure-activity relationships (QSAR) analysis revealed that CBZ exhibits higher acute and chronic toxicity, with metabolites showing varying levels of developmental toxicity. The study also evaluates the persistence, mobility, and toxicity (PMT) characteristics of CBZ and its metabolites, highlighting CBZ-N-Glu's particularly adverse PMT characteristics compared to CBZ. In summary, the residual pharmaceuticals in the reclaimed water process should be evaluated systematically, considering both the parent compounds and their metabolites.

In-silico screening of Staphylococcus aureus antibacterial agents based on Isatin scaffold: QSAR, molecular docking, molecular dynamics simulation and DFT analysis

The design of novel antimicrobial drugs is necessary due to the increasing resistance to many existing antibiotics. Considering that the drug discovery process requires a lot of time and resources, computational chemistry and molecular docking as the basic components in the drug discovery process, can help the acceleration of this prolonged process and minimise the research costs. In this study, 64 molecules containing the Isatin scaffold were subjected to quantitative structure–activity relationship analysis to identify the necessary features for designing effective antimicrobial agents. Three chemometrics methods were employed to establish a connection between structural parameters and antibacterial effects. MLR was identified as the best model, demonstrating high accuracy compared to the other models. Based on the obtained model, ten novel lead compounds were designed, and their antibacterial potency of these compounds was assessed. The outputs of the molecular docking study revealed that compound S4 fits well in the ATP-binding site and blocks ATPase activity. Additionally, it was observed that compound S4 exhibited adequate binding affinity against the DNA gyrase target. An appropriate relationship was observed between QSAR, DFT and molecular modelling approaches. Therefore, compound S4 can be considered as a promising candidate for further development as an antibacterial agent.