SAR and QSAR in Environmental Research最新文献

Ibuprofen (IBP) poses potential threats to human health and the environment. Lignin peroxidase (Lip), manganese peroxidase (Mnp), and laccase are considered as safe, friendly enzymes for biodegradation of IBP. However, surfactants like Tween 80 can alter the activity of these enzymes in IBP degradation, yet the underlying molecular details remain unclear. To study the influence of Tween 80 on the interaction between three enzymes, Lip, Mnp, laccase, and IBP, molecular docking and molecular dynamics (MD) simulations were conducted. The results of molecular docking results confirmed that IBP bound to residues around the active site of these three enzymes through hydrophobic interactions and hydrogen bonds. During MD simulations, Tween 80 gradually assemble into micelles that interacted with the enzyme surface. This might have altered the solvent environment and changed the interactions between these enzymes and IBP, thereby achieving more effective degradation of IBP. The results of binding free energy calculations further revealed that Tween 80 could promote the degradation of IBP by these three enzymes, while Lip has the best biodegradation effect on IBP. Our study can offer theoretical insights into the roles of Lip, Mnp, and laccase in the degradation of IBP.

Diabetic nephropathy (DN), a serious complication of chronic diabetes mellitus, remains a leading cause of end-stage renal disease (ESRD). Although therapies targeting the renin-angiotensin-aldosterone system (RAAS) exist, many patients still face progressive renal decline, highlighting the need for improved treatments. Sodium-glucose co-transporter 2 (SGLT2) inhibitors offer both glycaemic and renal benefits, yet glycosidic inhibitors are associated with adverse effects such as genitourinary infections and euglycemic ketoacidosis. This has prompted interest in non-glycosidic scaffolds. Thiazole, a drug-like heterocycle, shows promise as a core for novel SGLT2 inhibitors. In this study, a ligand-based pharmacophore model (DDHRR_1) was developed using known thiazole-based inhibitors and applied to virtual screening of Drug Central and Enamine libraries. Four compounds olodaterol, nebivolol, Z56768840, and Z8314156464 were identified, with olodaterol showing the most stable interactions during a 100 ns molecular dynamics (MD) simulation. Based on structural insights, a novel compound, NC-1, was rationally designed. NC-1 demonstrated a high pharmacophore fitness score (1.64), favourable docking energy (-11.1 kcal/mol), and stable MD interactions. This integrated computational approach offers a valuable platform for discovering non-glycosidic SGLT2 inhibitors and supports further development of NC-1 for potential treatment of DN.

SARS-CoV-2 3C-like protease (3CLpro) is an essential viral enzyme responsible for processing viral polyproteins and is a validated target for small-molecule therapeutic intervention. This study presents an integrative cheminformatics and systems biology framework to identify and evaluate potential 3CLpro inhibitors. A curated dataset of 919 compounds from the CHEMBL database was used to develop predictive QSAR models based on substructure fingerprints and 1D/2D molecular descriptors. The best-performing models achieved strong correlation coefficients (0.9736 for training and 0.7413 for testing), and key molecular features were identified using feature importance analysis. A web-based tool, 3CLpro-Pred was developed to enable rapid prediction of compound bioactivity. Molecular docking and dynamics simulations further validated QSAR findings by elucidating key atomic-level interactions at the protease active site. Top hit compounds were prioritized for systems-level analysis at the host-pathogen interface, where gene ontology and KEGG pathway enrichment revealed their potential to modulate key host signalling pathways. Critical regulatory genes, including TBK1, PIK3CA, IKBKB, GSK3B, and CASP3, were identified as major nodes linking compound action to antiviral and immune processes. This study delivers a combinatorial computational pipeline to accelerate the discovery of 3CLpro-targeted antivirals, providing mechanistic insights and a shortlist of candidates for future experimental validation.

One of the major challenges in cancer treatment is drug resistance, often driven by overexpression of detoxifying enzymes such as aldo-keto reductase family 1 member B10 (AKR1B10). Highly expressed in several cancers, AKR1B10 contributes to chemotherapeutic failure by promoting drug detoxification. Targeting this enzyme could help overcome resistance. In this study, an in silico drug repurposing approach was applied to screen 2,468 FDA-approved drugs from DrugBank for selective AKR1B10 inhibition. A multi-stage molecular docking strategy followed by binding free energy analysis identified ten potential inhibitors with docking scores between ‒10.11 and ‒11.27 kcal/mol and binding energies ranging from ‒31.00 to ‒81.54 kcal/mol. Ezetimibe, a cholesterol-lowering agent, emerged as the top hit with the highest binding energy (‒81.54 kcal/mol) to wild-type AKR1B10. Mutational analysis revealed reduced binding to Lys125, Val301, and Gln303 variants, highlighting residue-specific interactions. Ezetimibe showed weak binding to AKR1B1 (‒17.32 kcal/mol), supporting selectivity. Molecular dynamics simulations (100 ns) confirmed complex stability with average RMSD and RMSF values of 1.29 Å and 0.73 Å, respectively. Quantum mechanical analysis indicated favourable electronic properties and chemical stability. These results suggest that Ezetimibe is a selective and stable AKR1B10 inhibitor, warranting further investigation for drug-resistant cancer therapy.

In this study, an integrative computational approach combining molecular descriptors, genetic algorithm - multiple linear regression (GA-MLR), and toxicokinetic simulations was employed to characterize the preliminary profile of Persistent Organic Pollutants (POPs) in various pelagic fish species from the Adriatic Sea. The molecular basis and toxicokinetic profile based on previously obtained experimental data on POP concentrations were followed by preliminary environmental characterization to assess the relationship between fish-specific parameters (e.g. species, lipid content) and determined pollutant concentrations. A GA coupled with MLR was applied to develop predictive models identifying the most influential physico-chemical properties of POPs that contribute to non-carcinogenic and carcinogenic health risks associated with chronic fish consumption. The toxicokinetic modelling and Hansen solubility parameters (HSPs) were used to assess the consumers' risk, gastrointestinal absorption and bioaccumulation. The use of GA-MLR and 3D-MoRSE descriptors directly linked chemical structure to environmental fate, bioaccumulation, and toxicological outcomes. The modelled concentrations of POPs in key organs and tissues were compared to in vivo data reported in the literature. This integrated analysis establishes a scientific basis for future toxicological and risk assessments of POPs in Adriatic pelagic fish, with emphasis on experimental validation and toxicokinetic profiling relevant to human exposure.

Accurate prediction of immunotoxic effects is essential for chemical safety evaluation and drug development. However, existing methodologies are limited by the scarcity of in vitro data and the inherent complexity of immune responses. This study introduces an interpretable quantitative structure-activity relationship (QSAR)-based modelling framework aimed at assessing immunosuppressive toxicity utilizing IC₅₀ data obtained from three human immune cell lines: Jurkat, peripheral blood mononuclear cells (PBMC) and THP-1. Three tree-based machine learning algorithms, in conjunction with robust feature selection techniques, were employed to identify critical molecular determinants associated with immunosuppressive effects. The implementation of SHapley Additive exPlanations (SHAP) enhanced model interpretability and facilitated the extraction of potential structural alerts, thereby providing mechanistic insights into immunotoxicity pathways. Our findings indicate that the integration of immune cell-specific experimental data with interpretable modelling approaches significantly enhances the reliability of immunotoxicity predictions. This research establishes a scientifically grounded framework that not only supports the early identification of immunotoxic chemicals but also promotes safer chemical design and informed decision-making in drug development and toxicological risk assessment.

The epidermal growth factor receptor (EGFR), a validated therapeutic target in oncology, demonstrates overexpression across multiple neoplastic cell types and plays a critical role in tumorigenesis. This investigation reports the strategic synthesis and evaluation of antiproliferative efficacy for novel sorbamide derivatives designed as EGFR inhibitors. Several synthesized compounds exhibited moderate inhibitory effects against EGFR-overexpressed A431 carcinoma cells, and among them, compound 7d demonstrated superior potency relative to the reference agent Gefitinib with an IC₅₀ value of 19.1 µM. A predictive 4D-QSAR model was successfully developed, exhibiting satisfactory statistical parameters ($r_{tr}^2$ = 0.81, $Q_{LOO}^2$ = 0.62, $Q_{LMO}^2$ = 0.60, $r_{\mathrm{Pred}}^2$ = 0.71). Complementary computational analyses through covalent docking and molecular dynamics simulations elucidated the molecular interaction mechanism, revealing covalent bond formation between the sorbamide scaffold and the conserved Cys797 residue in the EGFR catalytic domain.

The persistent challenge posed by multi-drug resistant Staphylococcus aureus infections worldwide necessitates new solutions. We describe the creation of a multi-epitope vaccine aimed at offering cross-strain immunity. Antigens α-haemolysin (Hla) and staphylococcal enterotoxin B (SEB) were chosen considering their high immunodominance and sequence conservation levels. B-cell and T-cell epitopes were combined into a multi-epitope vaccine with the proper adjuvant and linker sequences included to allow for maximum immunogenicity and structural stability. Physicochemical characterization demonstrated that the construct is non-allergenic, heat-stable, and immunogenic. Structural optimization and modelling were performed, with confirmation by Ramachandran plot analysis and ProSA z-score, which verified the correctness of the model. Molecular docking indicated robust and stable interactions between the vaccine and major immune receptors, such as TLR3, MHC class I, and MHC class II. In addition, 200 ns molecular dynamics simulations and binding free energy calculations indicated stability and longevity of these complexes. Codon optimization and in silico cloning indicated efficient expression in E. coli. Immune simulations also anticipated strong activation of humoral and cellular immune elements such as B-cells, cytotoxic T lymphocytes, and antigen-presenting cells, and rising Ig levels. The vaccine's ability to induce overall immune protection against S. aureus requires further experimental confirmation.

Pheromone-binding proteins (PBPs) help insects communicate effectively and regulate social behaviour by binding and transporting odorants. However, the precise atomic-level interactions of PBP1 in Loxostege sticticalis (LstiPBP1) with odorants remain poorly understood. Therefore, the three-dimensional structure of LstiPBP1 was constructed using homology modelling, after which complex structures of LstiPBP1 with six odorants (cis-3-hexenyl acetate, naphthalene, heptaldehyde, phenethyl alcohol, α-ionone, and (E)-11-tetradecenol), respectively, were obtained by molecular docking. Each complex underwent molecular dynamics simulations to investigate their detailed interactions. In silico site-directed mutagenesis was performed on the key residues to verify the accuracy of the simulation models. Energy analysis and interaction patterns revealed that hydrophobic interactions, mainly stemming from van der Waals interactions, are critical for the interaction between LstiPBP1 and these odorants. Additionally, hotspot residues on LstiPBP1 involved in interacting with different odorants were identified, providing further insight into the specific molecular interactions that govern their recognition. These results facilitate the development of inhibitors targeting the insect olfactory system.

Infectious diseases remain a significant public health threat with global socio-economic impacts. The increasing resistance to current antimicrobial therapies highlights the urgent need for new treatments with novel mechanisms of action. This study investigates the antimicrobial potential of two series of thiazolidinone derivatives, previously synthesized, using conventional and microwave-assisted methods. Antimicrobial activity was evaluated using the microdilution method, and the ability to inhibit Candida albicans biofilm formation was assessed. AutoDock 4.2® software was employed to explore potential molecular targets in bacteria and fungi. Series A compounds exhibited moderate to weak antibacterial activity (MIC/MBC: 1.50-6.00/3.00-12.0 mg/mL), with A8 being the most active (MIC: 1.5-6.0 mg/mL). Series B showed stronger antibacterial effects (MIC/MBC: 0.37-3.00/1.50-6.00 mg/mL), particularly compound B4 (MIC: 0.375-1.50 mg/mL). For antifungal activity, series A compounds were more effective (MIC/MFC: 0.37-3.00/0.75-6.00 mg/mL), with A3 showing the best results (MIC: 0.37-0.75 mg/mL). Series A also inhibited C. albicans biofilm formation, with A2 (57.7%), A4 (65.44%), and A8 (50.35%) outperforming ketoconazole (47%). These findings highlight A2, A4, and A8 as promising candidates for antibiofilm development, with A8 emerging as a lead compound due to its dual antibacterial and antifungal potency.