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DOI 10.1021/acs.jcim.6c00183

Медицинская библиотека · Journal of chemical information and modeling · 2026

In Silico Decoding of Nonsteroidal Mineralocorticoid Receptor Antagonist Binding: Implications for Drug Design.

Pérez-Gordillo Felipe, Carpio Laureano E, Alvarez de la Rosa Diego, Martín-Martínez Mercedes

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Аннотация

The mineralocorticoid receptor (MR), a member of the steroid hormone nuclear receptor subfamily, mediates the effects of aldosterone and glucocorticoids. Initially characterized in epithelial tissues as a key regulator of blood pressure, MR was later found to be widely expressed across multiple tissues, where it mediates processes such as inflammation, oxidative stress, and fibrosis. Accordingly, MR antagonists (MRAs) have important clinical applications beyond their diuretic effect, forming a cornerstone in the treatment of chronic heart failure and showing promise in conditions like diabetic nephropathy. Nonsteroidal MRAs typically show fewer off-target effects than their steroidal counterparts, although the risk of hyperkalemia─especially in patients with renal impairment─remains a concern. A detailed understanding of MRA-binding modes is therefore crucial for the development of improved therapeutics. To this end, we conducted an extensive computational study integrating induced fit docking (IFD) and molecular dynamics (MD) simulations across a comprehensive set of MRAs. This approach highlights key interactions responsible for binding affinity and offers insights into the molecular basis of selectivity and antagonism. Moreover, MD simulations revealed a potential link between the ligand interaction with Trp806 and coregulator recruitment. These findings provide a valuable foundation for in silico rational design of next-generation MRAs with enhanced safety and efficacy profiles.

Для цитирования: Pérez-Gordillo Felipe, Carpio Laureano E, Alvarez de la Rosa Diego, Martín-Martínez Mercedes. In Silico Decoding of Nonsteroidal Mineralocorticoid Receptor Antagonist Binding: Implications for Drug Design.. Journal of chemical information and modeling. 2026. DOI: 10.1021/acs.jcim.6c00183.

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