Tenoxicam

Retention of molecules on immobilized artificial membrane (IAM) chromatography is a key physicochemical property for predictive models of permeability across biological barriers, with applications in drug design and ecotoxicology. Currently, IAM retention is solely experimentally determined, which limits its utility for screening virtual compound libraries or for predictions of yet not synthesized molecules. The present study focuses on developing predictive models of IAM retention factors (logk_w(IAM)) for a structurally diverse set of drug compounds, scrutinizing the role of lipophilicity, experimental and calculated, as well as the contribution of additional molecular parameters, selected from a pool of physicochemical, constitutional, topological and 3D descriptors. After obtaining a data overview by principal component analysis, both multiple linear regression (MLR) and partial least squares (PLS) analyses were used to construct lipophilicity-based models and lipophilicity-independent models. Bulk, polarity and fraction of anionic species were common descriptors in all models. It was demonstrated that calculated lipophilicity values introduced additional uncertainty, depending on the software used. On the other hand, lipophilicity-independent MLR and PLS models, which relied solely on computational descriptors, showed comparable performance with lipophilicity-based models, while offering the advantage to more useful for screening large libraries in early drug discovery. The reliability of lipophilicity-independent MLR and PLS models was assessed by external validation as well as by using a blind test set. Error distribution between lipophilicity-based and lipophilicity-independent models was also investigated and found to be comparable, while it was better than the differences between experimental and calculated lipophilicity values.

Dilated Cardiomyopathy (DCM) is a debilitating cardiovascular disorder that challenges current therapeutic strategies. The exploration of novel drug repositioning opportunities through gene expression analysis offers a promising avenue for discovering effective treatments.

This study aims to identify potential drug repositioning opportunities and lead compounds for DCM treatment by optimizing gene expression characteristics using published data.

Our approach involved analyzing DCM expression profiles from the Gene Expression Omnibus database and identifying differentially expressed genes with GEO2R. A protein interaction network was constructed using the STRING database and visualized with Cytoscape. Enrichment analyses were conducted on these genes through the Omicshare platform, followed by the identification of candidate compounds via the Connectivity Map (CMAP) and validation through molecular docking. The Coremine Medical database was utilized to predict potential herbal medicines.

We identified 29 differentially expressed genes, highlighting MYH6, NPPA, and NPPB as central to DCM pathology. Enrichment analyses indicated significant impacts on biological processes, such as organ morphogenesis and inflammatory responses. The AGE-RAGE signaling pathway was notably affected. From over 6,100 compounds analyzed, tenoxicam emerged as a promising candidate, with Radix Salviae Miltiorrhizae (Danshen) being suggested as a potential herbal treatment.

This study underscores the utility of bioinformatics in uncovering new therapeutic candidates for DCM, offering a foundational step towards novel drug development.