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As bisphenol A (BPA) has gradually become restricted in production scenarios, the ecological risk level of its main replacement chemicals, i.e., bisphenol S (BPS) and bisphenol F (BPF), should be noted. To overcome the limitations of toxicity data, two kinds of in silico toxicology models (quantitative structure-activity relationship (QSAR) and interspecies correlation estimation (ICE) models) were used to predict enough toxicity data for multiple species. The accuracy of the coupled in silico toxicology models was verified by comparing experimental and predicted data results. Reliable predicted no-effect concentrations (PNECs) of 8.04, 35.2, and 34.2 μg/L were derived for BPA, BPS, and BPF, respectively, using species sensitivity distribution (SSD). Accordingly, the ecological risk quotient (RQ) values of BPA, BPS, and BPF for aquatic organisms were assessed in 32 major Chinese surface waters; they ranged from nearly 0 to 1.86, but were <0.1 in most cases, which indicated that the overall ecological risk level of BPA and its alternatives was low. However, in some cases, the ecological risks posed by BPA alternatives have reached equivalent levels to those posed by BPA (e.g., Liuxi River, Taihu Lake, and Pearl River), which requires further attention. This study provides evidence that the application of coupled in silico toxicology models can effectively predict toxicity data for new chemicals, avoiding time-consuming and laborious animal experiments. The main findings of this study can support environmental risk assessment and management for new chemicals that lack toxicity data.
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http://dx.doi.org/10.3390/toxics13080671 | DOI Listing |
RSC Med Chem
August 2025
Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Suez Canal University 4.5 Km the Ring Road Ismailia 41522 Egypt.
Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability.
View Article and Find Full Text PDFCurr Pharm Des
September 2025
Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan, 45142, Saudi Arabia.
Introduction: Cervical cancer (CC) is among the most prevalent cancers affecting women globally, with a substantial number of deaths reported annually. Despite advancements in treatment, the persistently high mortality rate underscores the urgent need for novel and effective therapeutic strategies.
Methods: This study screened a library of 240 flavonoids against maternal embryonic leucine zipper kinase (MELK) and LYN using molecular docking methods to achieve precise calculations.
Toxicon
September 2025
Department of Pathology, College of Medicine, King Khalid University, P.O. 641, Abha, 61421, Saudi Arabia; Department of Forensic Medicine and Clinical Toxicology, Mansoura University, Egypt.
Titanium dioxide nanoparticles (TiO-NPs) are used in the production of various industrial and commercial products and reported to cause neurotoxicity in Sprague Dawley rats. Fortunellin (FRN) is a potent flavonoid with diverse biological properties. This research experiment was performed to explore the protective role FRN against TiO-NPs induced brain damage.
View Article and Find Full Text PDFBioorg Med Chem
September 2025
Goel Institute of Pharmacy and Sciences, Lucknow, Uttar Pradesh 226028, India. Electronic address:
N-methyl-d-aspartate (NMDA) receptors are validated druggable targets for the treatment of Alzheimer's and other associated neurological conditions, particularly in individuals with disabilities. Considering the excitotoxicity associated with NMDA receptors, which leads to neuronal damage, cognitive impairment, and limitations of current therapeutic regimens, better therapeutic candidates are required. One of the validated drug discovery approaches is computer-assisted drug discovery, supplemented by molecular docking, mechanics, and dynamics.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
Drug-induced liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation. In this study, we developed a graph convolutional network embedded with a biological graph learning (BioGL) module-named BioGL-GCN(Biological Graph Learning-Graph Convolutional Network)-for drug-induced liver injury prediction using toxicogenomic profiles.
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