Isorhapontigenin (ISO), a tetrahydroxylated stilbenoid, is an analog of resveratrol (Rsv). The various biological activities of Rsv and its derivatives have been previously reported in the context of both cancer and inflammation. However, the anti-cancer effect of ISO against breast cancer has not been well established, despite being an orally bioavailable dietary polyphenol. In this study, we determine the anti-cancer effects of ISO against breast cancer using MCF7, T47D, and MDA-MB-231 cell lines. We observed that ISO induces breast cancer cell death, cell cycle arrest, oxidative stress, and the inhibition of cell proliferation. Additionally, sphingosine kinase inhibition by ISO controlled tubulin polymerization and cancer cell growth by regulating MAPK/PI3K-mediated cell cycle arrest in MCF7 cells. Interestingly, SPHK1/2 gene silencing increased oxidative stress, cell death, and tubulin destabilization in MCF7 cells. This suggests that the anti-cancer effect of ISO can be regulated by SPHK/tubulin destabilization pathways. Overall, ISO successfully induced breast cancer cell death and cell growth arrest, suggesting this phytochemical is a better alternative for breast cancer treatment. Further studies in animal models could confirm the potency and usability of ISO over Rsv for targeting breast cancer, potentially posing an alternative candidate for improved therapy in the near future.

Alzheimer’s disease (AD) is the most common dementia in late life memory related issues. It is estimated that worldwide 46.8 million people suffer from dementia. The hypoxia inducible factor (HIF) upregulation could be a potential neuroprotective strategy by modulating the multiple survival pathways in Alzheimer’s disease. Hence, the development of small molecules that induce HIF-α activation via inhibition of prolyl hydroxylase enzyme (PHD) has been suggested as a potentially useful therapeutic strategy for the treatment of AD. Thus, to unveil a novel human PHD inhibitor, 2D-QSAR (Quantitative Structure Activity Relationship) modeling was performed with the 213 PHD inhibitors reported in the USA patent database. Best model with the r² score of 0.8273 and q² value of 0.8284 with dendritic fingerprint was developed and visualized by kernel partial least squares (KPLS) methods, which were used for visualization and screening of natural/derivative compounds database for novel and effective drug or scaffold. The docking was performed as a secondary strategy for screening the top 5000 compounds retrieved by 2D-QSAR based screening. Further, the docked complexes were screened by molecular dynamics (MD) simulation and molecular mechanics/generalized Born surface area (MM-GBSA) based binding free energy calculations to determine the binding energy of the inhibitors and to identify crucial interacting energy contributors. Three leads have demonstrated good binding free energy and the better binding affinity for PHD compared to other selected ligands. Thus, the results obtained from QSAR, docking and MD simulations depicted that hydantoin scaffold could be effectively used as a potent inhibitor toward human PHD in AD therapeutics.

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure–activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR–ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR–ligand complexes. As a result, we developed GPCR–ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out ‘residue level’ (for flexible transmembrane helices of GPCRs), (2) zoomed-in ‘pocket level’ (for sophisticated mode of action) and (3) ‘atom level’ (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (G_i/o, G_q/11, G_s and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.