RESUMEN

The clinical use of many potent anticancer agents is limited by their non-selective toxicity to healthy tissue. One of these examples is vorinostat (SAHA), a pan histone deacetylase inhibitor, which shows high cytotoxicity with limited discrimination for cancerous over healthy cells. In an attempt to improve tumor selectivity, we exploited the properties of cobalt(III) as a redox-active metal center through stabilization with cyclen and cyclam tetraazamacrocycles, masking the anticancer activity of SAHA and other hydroxamic acid derivatives to allow for the complex to reach the hypoxic microenvironment of the tumor. Biological assays demonstrated the desired low in vitro anticancer activity of the complexes, suggesting effective masking of the activity of SAHA. Once in the tumor, the bioactive moiety may be released through the reduction of the CoIII center. Investigations revealed long-term stability of the complexes, with cyclic voltammetry and chemical reduction experiments supporting the design hypothesis of SAHA release through the reduction of the CoIII prodrug. The results highlight the potential for further developing this complex class as novel anticancer agents by masking the high cytotoxicity of a given drug, however, the cellular uptake needs to be improved.

Asunto(s)

Antineoplásicos , Cobalto , Complejos de Coordinación , Ácidos Hidroxámicos , Oxidación-Reducción , Vorinostat , Cobalto/química , Antineoplásicos/química , Antineoplásicos/farmacología , Humanos , Ácidos Hidroxámicos/química , Ácidos Hidroxámicos/farmacología , Vorinostat/química , Vorinostat/farmacología , Complejos de Coordinación/química , Complejos de Coordinación/farmacología , Línea Celular Tumoral , Inhibidores de Histona Desacetilasas/química , Inhibidores de Histona Desacetilasas/farmacología , Profármacos/química , Profármacos/farmacología , Compuestos Heterocíclicos/química , Compuestos Heterocíclicos/farmacología

RESUMEN

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.