CYP19 (aromatase): Exploring the scaffold flexibility for novel selective inhibitors

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.

Binding mode of triazole derivatives as aromatase inhibitors based on docking, protein ligand interaction fingerprinting, and molecular dynamics simulation studies

Aromatase inhibitors (AIs) as effective candidates have been used in the treatment of hormone-dependent breast cancer. In this study, we have proposed 300 structures as potential AIs and filtered them by Lipinski's rule of five using DrugLito software. Subsequently, they were subjected to docking simulation studies to select the top 20 compounds based on their Gibbs free energy changes and also to perform more studies on the protein-ligand interaction fingerprint by AuposSOM software. In this stage, anastrozole and letrozole were used as positive control to compare their interaction fingerprint patterns with our proposed structures. Finally, based on the binding energy values, one active structure (ligand 15) was selected for molecular dynamic simulation in order to get information for the binding mode of these ligands within the enzyme cavity. The triazole of ligand 15 pointed to HEM group in aromatase active site and coordinated to Fe of HEM through its N4 atom. In addition, two π-cation interactions was also observed, one interaction between triazole and porphyrin of HEM group, and the other was 4-chloro phenyl moiety of this ligand with Arg115 residue.

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.

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

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.

Effects of azole treatments on the physical properties of Candida albicans plasma membrane: a spin probe EPR study

EPR spectroscopy was applied to investigate the effects of the treatment of Candida albicans cells with fluconazole (FLC) and two newly synthesized azoles (CPA18 and CPA109), in a concentration not altering yeast morphology, on the lipid organization and dynamics of the plasma membrane. Measurements were performed in the temperature range between 0°C and 40°C using 5-doxyl- (5-DSA) and 16-doxyl- (16-DSA) stearic acids as spin probes. 5-DSA spectra were typical of lipids in a highly ordered environment, whereas 16-DSA spectra consisted of two comparable components, one corresponding to a fluid bulk lipid domain in the membrane and the other to highly ordered and motionally restricted lipids interacting with integral membrane proteins. A line shape analysis allowed the relative proportion and the orientational order and dynamic parameters of the spin probes in the different environments to be determined. Smaller order parameters, corresponding to a looser lipid packing, were found for the treated samples with respect to the control one in the region close to the membrane surface probed by 5-DSA. On the other hand, data on 16-DSA indicated that azole treatments hamper the formation of ordered lipid domains hosting integral proteins and/or lead to a decrease in integral protein content in the membrane. The observed effects are mainly ascribable to the inhibition of ergosterol biosynthesis by the antifungal agents, although a direct interaction of the CPA compounds with the membrane bilayer in the region close to the lipid polar head groups cannot be excluded.

Luminometric sub-nanoliter droplet-to-droplet array (LUMDA) and its application to drug screening by phase I metabolism enzymes

Here we show the fabrication of the Luminometric Sub-nanoliter Droplet-to-droplet Array (LUMDA chip) by inkjet printing. The chip is easy to be implemented and allows for a multiplexed multi-step biochemical assay in sub-nanoliter liquid spots. This concept is here applied to the integral membrane enzyme CYP3A4, i.e. the most relevant enzymatic target for phase I drug metabolism, and to some structurally-related inhibitors.

Antifungal activity of azole compounds CPA18 and CPA109 against azole-susceptible and -resistant strains of Candida albicans

OBJECTIVES

In this study we investigated the in vitro fungistatic and fungicidal activities of CPA18 and CPA109, two azole compounds with original structural features, alone and in combination with fluconazole against fluconazole-susceptible and -resistant Candida albicans strains.

METHODS

Antifungal activities were measured by MIC evaluation and time-kill studies. Azole binding analysis was performed by UV-Vis spectroscopy. Hyphal growth inhibition and filipin and propidium iodide staining assays were used for morphological analysis. An analysis of membrane lipids was also performed to gauge alterations in membrane composition and integrity. Synergism was calculated using fractional inhibitory concentration indices (FICIs). Evaluation of cytotoxicity towards murine macrophages was performed to verify selective antifungal activity.

RESULTS

Even though their binding affinity to C. albicans Erg11p is comparable to that of fluconazole, CPA compounds are active against resistant strains of C. albicans with a mutation in ERG11 sequences and/or overexpressing the ABC transporter genes CDR1 and CDR2, which encode ATP-dependent efflux pumps. Moreover, CPA18 is fungistatic, even against the two resistant strains, and was found to be synergistic with fluconazole. Differently from fluconazole and other related azoles, CPA compounds induced marked changes in membrane permeability and dramatic alterations in membrane lipid composition.

CONCLUSIONS

Our outcomes suggest that CPA compounds are able to overcome major mechanisms of resistance in C. albicans. Also, they are promising candidates for combination treatment that could reduce the toxicity caused by high fluconazole doses, particularly in immunocompromised patients.

Shu-Gan-Liang-Xue Decoction Simultaneously Down-regulates Expressions of Aromatase and Steroid Sulfatase in Estrogen Receptor Positive Breast Cancer Cells

OBJECTIVE

Estradiol (E2) plays an important role in the development of breast cancer. In postmenopausal women, the estrogen can be synthesized via aromatase (CYP19) pathway and steroid-sulfatase (STS) pathway in peripheral tissues, when the production in ovary has ceased. The objective of our study was to explore the effects of Shu-Gan-Liang-Xue Decoction (SGLXD) on the expressions of CYP19 and STS in estrogen receptor positive breast cancer MCF-7 and T47D cells.

METHODS

The effects of SGLXD on the cell viability of MCF-7 and T47D were analyzed by MTT assay. By quantitative real-time RT-PCR and Western blot, we evaluated the mRNA and protein expressions of CYP19 and STS in MCF-7 and T47D cells after SGLXD treatment.

RESULTS

By MTT assay, the cell viability rates of MCF-7 and T47D were significantly inhibited by SGLXD in a dose-dependent manner, the IC50 values were 40.07 mg/ml for MCF-7 cells and 25.62 mg/ml for T47D cells, respectively. As evidenced by real-time PCR and Western blot, the high concentrations of SGLXD significantly down-regulated the expressions of CYP19 and STS both in the transcript level and the protein level.

CONCLUSION

The results suggest that SGLXD is a potential dual aromatase-sulfatase inhibitor by simultaneously down-regulating the expressions of CYP19 and STS in MCF-7 and T47D cells.

Design, synthesis, and biological evaluation of imidazolyl derivatives of 4,7-disubstituted coumarins as aromatase inhibitors selective over 17-α-hydroxylase/C17-20 lyase

The design, synthesis, and biological evaluation of a series of new aromatase (AR, CYP19) inhibitors bearing an imidazole ring linked to a 7-substituted coumarin scaffold at position 4 (or 3) are reported. Many compounds exhibited an aromatase inhibitory potency in the nanomolar range along with a high selectivity over 17-α-hydroxylase/C17-20 lyase (CYP17). The most potent AR inhibitor was the 7-(3,4-difluorophenoxy)-4-imidazolylmethyl coumarin 24 endowed with an IC(50) = 47 nM. Docking simulations on a selected number of coumarin derivatives allowed the identification of the most important interactions driving the binding and clearly indicated the allowed and disallowed regions for appropriate structural modifications of coumarins and closely related heterocyclic molecular scaffolds.

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.

Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors

In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors.

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.