Active site acidic residues and structural analysis of modelled human aromatase: a potential drug target for breast cancer

A mechanistic toxicology study to grasp the mechanics of zearalenone estrogenicity: Spotlighting aromatase and the effects of its genetic variability

Zearalenone (ZEN) is a mycoestrogen produced by Fusarium fungi contaminating cereals and in grain-based products threatening human and animal health due to its endocrine disrupting effects. Germane to the mechanisms of action, ZEN may activate the estrogen receptors and inhibit the estrogens-producing enzyme aromatase (CYP19A1). Both show single nucleotide variants (SNVs) among humans associated with a diverse susceptibility of being activated or inhibited. These variations might modify the endocrine disrupting action of ZEN, requiring dedicated studies to improve its toxicological understanding. This work focused on human aromatase investigating via 3D molecular modelling whether some of the SNVs reported so far (n = 434) may affect the inhibitory potential of ZEN. It has been also calculated the inhibition capability of α-zearalenol, the most prominent and estrogenically potent phase I metabolite of ZEN, toward those aromatase variants with an expected diverse sensitivity of being inhibited by ZEN. The study: i) described SNVs likely associated with a different susceptibility to ZEN and α-zearalenol inhibition - like T310S that is likely more susceptible to inhibition, or D309G and S478F that are possibly inactive variants; ii) proofed the possible existence of inter-individual susceptibility to ZEN; iii) prioritized aromatase variants for future investigations toward a better comprehension of ZEN xenoestrogenicity at an individual level.

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.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Molecular modeling evaluation of non-steroidal aromatase inhibitors

A recent discovery of aromatase crystal structure triggered the efforts to design novel aromatase inhibitors for breast cancer therapy. While correlating docking scores with inhibitory potencies of known ligands, feeble robustness of scoring functions toward prediction was observed. This prompted us to develop new prediction models using stepwise regression analysis based on consensus of different docking and their scoring methods (GOLD, LIGANDFIT, and GLIDE). Quantitative structure-activity relationships were developed between the aromatase inhibitory activity (pIC(50) ) of flavonoid derivatives (n=39) and docking scores and docking descriptors. QSAR models have been validated internally [using leave-one-out cross-validated r(2)(cv) (LOO-Q2))] and externally to ensure the predictive capacity of the models. Model 2 [M2] developed using consensus of docking scores of scoring functions viz. ASP, potential of mean force and DOCK Score (r(2)(cv)=0.850, r(2) = 0.870, r(2)(pred) = 0.633, RMSE = 0.363 μm, r(2)(m(test)) =0.831, r(2)(m(overall)) =0.832) was found to be better in predicting aromatase inhibitory potency (pIC(50) ) compared to the Model 1 [M1] based on docking descriptors (r(2)(cv)= 0.848, r(2) = 0.825, r(2)(pred) =0.788, RMSE=0.421μm, r(2)(m(test)) =0.808, r(2)(m(overall)) =0.821). It has been observed that the natural flavonoids and their derivatives were less potent compared to these scaffolds with imidazolylmethyl substitution owing to the interaction of nitrogen atom of the imidazole ring toward the heme (Fe(3+) ) of the aromatase. Results confirm the potential of our methodology for the design of new potent non-steroidal aromatase inhibitors.

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.