Tozasertib

Aurora A kinase (AAK) is a serine/threonine kinase that stands out as a crucial regulator of mitosis, the complex process of cell division. Notably, the protein AAK plays vital roles in cell cycle regulation and encompasses centrosome maturation, spindle assembly, and chromosome segregation. All such functionalities are essential for ensuring accurate daughter cell formation. Deregulation of AAK expression and activity has been linked to various human diseases, particularly cancer. However, AAK's significance extends beyond normal cellular function. Increased expression or activity of AAK has been implicated in the development and progression of several human cancers. AAK's critical role in cell division and its association with cancer make it a prominent drug target. Herein, series of advance computational approaches was utilized including multi-step molecular docking through AutoDock Vina and PLANTS docking to screen ChemDiv kinase-specific inhibitor library against AAK. Absolute binding energy was estimated, and finally, a molecular dynamics simulation study was conducted to screen out three hit compounds. Both docking studies revealed perfect binding of all identified ligands in active site pockets of AAK protein with similar amino acids of active sites as compared with standard BindingDB_50433632 compound and co-crystal ligand VX-680 binding mode of AAK protein. Therefore, it can be concluded that computational drug discovery approaches are meticulously implemented to identify potential AAKs inhibitors/modulators, and credential of the work was substantiated through the identification of three potential AAKs inhibitors/modulators that may hold significant promise for improving cancer management, however, need extensive biological assays or pre-clinical trials for assessing the efficacy profile of the identified compounds.

Sepsis, a systemic inflammatory response syndrome precipitated by infection, involves critical immune dysregulation. Here, we aimed to evaluate immune-derived diagnostic markers, investigate immune cell infiltration, and explore targeted pharmacological interventions.

Weighted gene co-expression network analysis and multiple machine learning (ML) algorithms were used to screen hub markers and build a sepsis prediction model. Immune cell infiltration was analyzed using CIBERSORT. In vivo validation of mRNA trends was performed in murine sepsis models. Drug-Gene Interaction and Connectivity Map databases, molecular docking and dynamics simulations were used to identify and validate biomarker - targeted compounds.

Six immune-derived biomarkers were identified: CMTM1, IL18R1, JAK2, MAPK14, S100A6, and S100A8. The prediction model achieved an area under the curve (AUC) of 0.958. Immune infiltration characteristics and biomarker-immune cell correlations were identified. Murine models recapitulated human biomarker expression. Biomarker protein-drug combinations exhibited excellent binding capabilities, highlighting AT9283, tozasertib, SD-169, and LY294002 as potential therapeutic agents.

This study identified key immune biomarkers for sepsis, developed a highly accurate prediction model, and revealed promising therapeutic compounds. Notably, this is the first study to link CMTM1 to sepsis. These findings offer novel perspectives on accurate diagnosis and targeted therapy of immune dysregulation in sepsis.