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  • br Results br Discussion br Conclusions In summary this

    2023-12-04


    Results
    Discussion
    Conclusions In summary, this study provides novel report of pericardial adipose aromatase expression – in both human and rodent. We show that aromatase expression is remarkably upregulated with aging (Fig. 1C), and that total aromatase cdk1 sale conversion capacity is significantly elevated with obesity-related cardiac adiposity (Fig. 1F). Further, we explore an association between adiposity, aromatase estrogenic capacity and atrial arrhythmogenicity in the rodent ex vivo setting.
    Funding sources Research support provided through National Health and Medical Research Councill (LMDD, SBH, JMK, JRB; #1099352), Australian Research Council (LMDD, LJP, MT; DP160102404) and Victorian Government Operational Infrastructure Support Program (ED). Fellowship support provided through National Health and Medical Research Council (ED; #550905).
    Disclosures
    Introduction Aromatase, a cytochrome P450 enzyme complex present in breast tissues, plays a significant role in the biosynthesis of estrogen from androgen [1], [2], [3]. Inhibition of the aromatase is a crucial strategy to prevent the growth stimulation effect of estrogens in breast cancer tissues in postmenopausal women [4], [5]. So, aromatase inhibitors (AIs) have been developed and used as anti-breast cancer agents. AIs can be catagorized by their mechanisms of action into two types, steroidal and nonsteroidal aromatase inhibitors. The former type of agents are androstenedione analogs which may bind to the active site of the aromatase via irreversible process either by a mechanism-based or by a competitive manner via covalent interactions. The latter AIs interact reversibly to the active site of the aromatase through noncovalent interaction [3], [6], [7]. Di- and triarylmethanes are core structures regularly found in the most potent nonsteroidal AIs such as letrozole (1) and anastrozole (2) bearing triazole ring as well as liarozole (3) containing imidazole and benzimidazole rings [1]. Generally, the important structural feature of the nonsteroidal AIs is represented by a heterocyclic nitrogen atom, which interacts with the heme iron of the aromatase enzyme while the rest of the molecule interacts with amino acid residues of the active site [1], [8], [9]. The indole scaffold, defined as a privileged structure, is one of the most important heterocyclic systems for pharmaceutical applications [10]. A simple naturally occurring indole i.e., indole-3-carbinol (4) is a constituent of cruciferous vegetables of the genus Brassica. 3,3′-Diindolylmethane (5) is a major product of acid condensation of indole 4 formed in the stomach. Indoles 4 and 5 have been proven to be chemoproventive agents. They were developed for preventing, inhibiting and reversing the progession of many cancer cells, especially, estrogen-dependent reproductive cancers [11], [12]. Licznerska et al. have reported that the indoles 4 and 5 could inhibit the aromatase expression in estrogen-dependent MCF7 breast cancer cell line [13]. In addition, many indoles containg-molecules have been reported to display potent aromatase inhibitory activity [14], [15], [16]. Furthermore, sulfonamide scaffold has also been found in AIs [17], [18], [19], [20] in which such group could form hydrogen bond interaction with the target protein [17], [18], [19]. However, due to the expansion of resistance to AIs and the side effects related to currently utilized compounds, the need for improved AIs persists [21], [22]. Computational approaches are considered as effective facilitating tools in drug design and discovery [23]. Molecular docking is a computational method to find possible binding modes of the compound against its biological target, and has been successfully used to investigate binding modes of many classes of aromatase inhibitors [17], [18], [19]. Quantitative structure-activity relationship (QSAR) is a method that correlates chemical structure of the compound with its biological activity [24], [25], [26]. QSAR has been widely used to provide useful information for guiding the design and discovery of many classes of drugs, including aromatase inhibitors [7].