Cisplatin is a widely used chemotherapeutic agent due to its ability to damage DNA in the treatment of cancer. However, its clinical application is often limited by adverse effects on normal tissues, especially the kidneys. Understanding the molecular mechanisms of cisplatin-induced nephrotoxicity is crucial for developing strategies to mitigate its side effects. In this study, we aimed to elucidate the molecular mechanisms underlying cisplatin-induced DNA damage and apoptosis in human renal epithelial cells, with a focus on key signaling pathways and mediators that drive nephrotoxicity.

To explore these mechanisms, human proximal tubule epithelial cells (HK-2) were treated with cisplatin. The study assessed DNA damage response (DDR) and stress-related protein expression, cell cycle distribution, and apoptosis. Activation of mitogen-activated protein kinases (MAPKs), particularly Extracellular signal-regulated Kinase (ERK), was analyzed, along with the expression and functional role of activating transcription factor 3 (ATF3) and tumor protein p53 (p53).

Cisplatin treatment upregulated DDR and stress response proteins, induced S phase arrest, and increased the SubG1 population, indicating apoptotic cell death. ERK was identified as a critical mediator of cisplatin-induced DNA damage and stress responses. ATF3 expression was significantly elevated in an ERK-dependent manner and required p53 activation. Knockdown of ATF3 reduced cisplatin-induced DNA damage, highlighting its role in the cytotoxic response.

Cisplatin induces nephrotoxicity through ERK- and p53-dependent upregulation of ATF3, which is associated with DNA damage and cell death, suggesting a modulatory role in the cellular stress response. These findings provide novel insights into the molecular basis of cisplatin-induced renal injury and suggest potential therapeutic targets to alleviate its adverse effects.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a critical therapeutic target in hepatocellular carcinoma (HCC) due to its role in angiogenesis and tumor progression. While several inhibitors are currently used, clinical utility is often limited by resistance and adverse effects, necessitating the discovery of novel therapeutic agents. The aim of this study was to identify and characterize novel, highly effective VEGFR2 inhibitors using an integrated computational pipeline to advance the development of new HCC treatments.

A comprehensive dataset from the ChEMBL database was curated and standardized for Quantitative Structure-Activity Relationship (QSAR) modeling. A binary classification framework was employed, where a Light Gradient Boosting Machine (LGBM) model demonstrated superior predictive performance. Two lead compounds and a reference were selected for in-depth molecular modeling. Their binding poses were predicted via molecular docking and subsequently subjected to 200 ns Molecular Dynamics (MD) simulations to assess stability and conformational dynamics. Thermodynamic binding affinities were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method.

The LGBM model achieved high accuracy and a robust Matthews Correlation Coefficient (MCC) on an independent test set. MD analysis, including Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg), confirmed stable binding throughout the 200 ns trajectory. MMPBSA calculations validated the binding affinities, identifying van der Waals and electrostatic interactions as the primary driving forces for complex stability.

This study successfully bridges machine learning with advanced molecular simulations, offering a validated workflow for the rational design and optimization of novel small-molecule VEGFR2 inhibitors.