Fasoracetam

Endometrial carcinoma (EC), a prevalent gynecological cancer, is characterized by rising incidence and mortality rates, highlighting the need for novel treatments to improve patient outcomes. Maackiain (MA) is a natural compound isolated from common herbal medicines, that has been reported to have anti-cancer effects. However, the underlying roles and mechanisms concerning EC remain unclear. This study focused on deeply exploring the potential roles and mechanisms of MA against EC by network pharmacology, experimentally validated, metabolomics, and molecular docking. A total of 86 potential targets of MA against EC were identified by network pharmacology. In vitro experiments further confirmed network pharmacology' predictions. In addition to suppressing EC cell proliferation, MA also paused the cell cycle at the G2/M phase in a dose-dependent manner. This effect is accompanied by increased p21 and phospho-p53 expression, as well as reduced expression of CDK1 and CCNB1. Furthermore, cell metabolomics analysis revealed that 285 metabolites were changed after MA administration, which majorly affects glycerophospholipid metabolism, nucleotide metabolism, choline metabolism in cancer, and purine metabolism. Combination network pharmacology, metabolomics, and molecular docking, PLA2G10, PDE4D, and PDE5A were found to be potential targets for therapeutic intervention. These findings underlined that MA has anti-EC potential by modulating multiple targets including PLA2G10, PDE4D, and PDE5A, inhibiting EC cell proliferation, inducing G2/M phase arrest, and causing metabolic shifts. This study provides theoretical support for advanced experimental research on its clinical applications.

The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.