Pharmacophore mapping of flavone derivatives for aromatase inhibition

Inhibitory effect of chrysin on estrogen biosynthesis by suppression of enzyme aromatase (CYP19): A systematic review

The cytochrome P450 enzyme functions as the rate-limiting enzyme in changing androgens to estrogens. Inhibition of aromatase is one of the significant objectives of treatment of hormone-dependent diseases such as breast cancer, especially in post-menopausal women. Natural compounds like chrysin, as a flavor that has a high concentration in honey and propolis, are rich sources of them can be useful in inhibiting aromatase for chemoprevention following treatment or in women at risk of acquiring breast cancer. This study intended to summarize the existing evidence on the effect of chrysin on aromatase activity. We systematically searched Science Direct, PubMed and Google Scholar and hand searched the reference lists of identified relevant articles, up to 5 February, 2019. Articles with English abstracts that reported the effect of chrysin on aromatase inhibition and without publication date restriction were investigated. Twenty relevant articles were chosen from a total of 1721 articles. Only one study was performed on humans and two studies were assayed on rats, while other studies were evaluated in vitro. All the studies except one showed that chrysin had the potency of aromatase inhibition; however, only one study performed on endometrial stromal cells showed that chrysin and naringenin did not indicate aromatase inhibitory properties. Various assay methods and experimental conditions were the important aspects leading to different results between the studies. Chrysin has potency in inhibition of the aromatase enzyme and thus can be useful in preventing and treating the hormone-dependent breast cancer and as an adjuvant therapy for estrogen-dependent diseases.

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Antiestrogenic activity of flavonoid phytochemicals mediated via the c-Jun N-terminal protein kinase pathway. Cell-type specific regulation of estrogen receptor alpha

Flavonoid phytochemicals act as both agonists and antagonists of the human estrogen receptors (ERs). While a number of these compounds act by directly binding to the ER, certain phytochemicals, such as the flavonoid compounds chalcone and flavone, elicit antagonistic effects on estrogen signaling independent of direct receptor binding. Here we demonstrate both chalcone and flavone function as cell type-specific selective ER modulators. In MCF-7 breast carcinoma cells chalcone and flavone suppress ERα activity through stimulation of the stress-activated members of the mitogen-activated protein kinase (MAPK) family: c-Jun N-terminal kinase (JNK)1 and JNK2. The use of dominant-negative mutants of JNK1 or JNK2 in stable transfected cells established that the antiestrogenic effects of chalcone and flavone required intact JNK signaling. We further show that constitutive activation of the JNK pathway partially suppresses estrogen (E2)-mediated gene expression in breast, but not endometrial carcinoma cells. Our results demonstrate a role for stress-activated MAPKs in the cell type-specific regulation of ERα function.

Molecular Docking of Aromatase Inhibitors

Aromatase is an enzyme that plays a critical role in the development of estrogen receptor positive breast cancer. As aromatase catalyzes the aromatization of androstenedione to estrone, a naturally occurring estrogen, it is a promising drug target for therapeutic management. The undesirable effects found in aromatase inhibitors (AIs) that are in clinical use necessitate the discovery of novel AIs with higher selectivity, less toxicity and improving potency. In this study, we elucidate the binding mode of all three generations of AI drugs to the crystal structure of aromatase by means of molecular docking. It was demonstrated that the docking protocol could reliably reproduce the interaction of aromatase with its substrate with an RMSD of 1.350 Å. The docking study revealed that polar (D309, T310, S478 and M374), aromatic (F134, F221 and W224) and non-polar (A306, A307, V370, L372 and L477) residues were important for interacting with the AIs. The insights gained from the study herein have great potential for the design of novel AIs.

Molecular docking and QSAR studies of aromatase inhibitor androstenedione derivatives

Objectives

Aromatase (CYP19) inhibitors have emerged as promising candidates for the treatment of estrogen-dependent breast cancer. In this study, a series of androstenedione derivatives with CYP19 inhibitory activity was subjected to a molecular docking study followed by quantitative structure–activity relationship (QSAR) analyses in search of ideal physicochemical characteristics of potential aromatase inhibitors.

Methods

The QSAR studies were carried out using both two-dimensional (topological, and structural) and three-dimesional (spatial) descriptors. We also used thermodynamic parameters along with 2D and 3D descriptors. Genetic function approximation (GFA) and genetic partial least squares (G/PLS) were used as chemometric tools for QSAR modelling.

Key findings

The docking study indicated that the important interacting amino acids in the active site were Met374, Arg115, Ile133, Ala306, Thr310, Asp309, Val370, Leu477 and Ser478. The 17-keto oxygen of the ligands is responsible for the formation of a hydrogen bond with Met374 and the remaining parts of the molecules are stabilized by the hydrophobic interactions with the non-polar amino acids. The C2 and C19 positions in the ligands are important for maintaining the appropriate orientation of the molecules in the active site. The results of docking experiments and QSAR studies supported each other.

Conclusions

The developed QSAR models indicated the importance of some Jurs parameters, structural parameters, topological branching index and E-state indices of different fragments. All the developed QSAR models were statistically significant according to the internal and external validation parameters.

Modeling of diarylalkyl-imidazole and diarylalkyl-triazole derivatives as potent aromatase inhibitors for treatment of hormone-dependent cancer

Aromatase is an enzyme that catalyzes the final step in the conversion of androgen to estrogen. It has become an attractive target for the treatment of estrogen responsive breast cancer. The study has been focused on designing aromatase inhibitors (AIs) that can be selected as probable drug candidate for the treatment of breast cancer. In the present study, long chain diarylalkyl-imidazole and -triazole scaffolds have been considered for exploring pharmacophores as potent AIs using QSAR (Quantitative SAR) and pharmacophore mapping studies. The model generated in linear free energy QSAR study (R(2) = 0.905, Q(2)= 0.885, R(2)(pred(ts)) = 0.763) showed the importance of hydrophobicity, size and shape of the molecule, van der Waals surface and hydrogen atom contribution influence the activity. 3D QSAR of comparative molecular field analysis (CoMFA, R(2)= 0.921, Q(2) = 0.741, R(2)(pred(ts))= 0.583) showed that steric and electrostatic features along with hydrophobicity and electronic charge contribution at C(4) (Fig. 1) influence on the inhibitory activity. Comparative molecular similarity analysis (CoMSIA, R(2) = 0.874, Q(2) = 0.716, R(2)(pred(ts)) = 0.591) study adjudged the presence of steric, electrostatic and hydrophobic fields together with hydrogen bond (HB) donor and acceptor play significant role in inhibitory activity to aromatase enzyme. Further pharmacophore mapping study (Q(2) = 0.947, Delta(cost) = 113.171, R(2)(pred(ts)) = 0.857) suggested that presence of HB acceptor, hydrophobicity with aromatic ring, and the importance of steric contribution influence on the activity. The critical distances among the features are also important for the inhibitor activity.

QSAR of cytochrome inhibitors

Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure-activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clinically important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure-activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug-drug interactions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.