Diapositiva 1 - PublicationsList.org
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Diapositiva 1 - PublicationsList.org
Simone Brogi and Andrea Tafi Dipartimento Farmaco Chimico Tecnologico, Università degli Studi di Siena Via Aldo Moro, I-53100 Siena, Italy Estrogen receptors (ER-α and ER-β subtypes) are members of a superfamily of ligand-activated transcription factors. Stimulation of estrogen receptors by endogenous estrogens plays an important role in both male and female physiology. Estrogens are involved in the regulation of cholesterol and lipid levels, the skeletal system, the central nervous system, and reproductive functions. However, estrogen stimulation is also implicated in the development of breast cancer. Consequently, many estrogen receptor ligands (SERMs: selective estrogen receptor modulators) are being developed with the aim of preventing estrogen mediated tumor growth. MCF-7 cells are a well-characterized estrogen receptor (ER) positive control cell line and therefore are a useful in vitro model to study the activity of new metabolites against breast cancer 1 Pharmacophore generation The Catalyst/HypoRefine algorithm was used.2 (which allows to generate hypotheses with excluded volumes and thus accounting for steric hindrance problems) (Fig.1) HY1 The pharmacophore model for ER-α (PHERA) was generate taking into account every class of SERMs with significant structural diversity (Fig.2) HY2 The computational model was able to accurately estimate the activities of new chemical entities (Fig.3) HY2 HBD The interactions in the binding pocket of ER-α were highlighted by PHERA at their proper position Was used PHERA to perform a Virtual Screening HBA Fig. 1 Superposition of PHERA and 34 (the most active compound in the training set). Pharmacophore features are color-coded: purple for hydrogen bond donor (HBD), green for hydrogen bond acceptor (HBA), sea green for hydrophobic (HY1) and cyan for hydrophobic aromatic (HY2) Virtual screening In our study, the computational model PHERA was used to search Asinex, Maybridge and NCI2000 chemical databases (about 500,000 structurally diversified small molecules) for new chemical structures active against MCF-7 cell line Compounds with a fit cutoff value of 5 were selected by Catalyst software. Other filters were applied to identify entries against MCF-7 cell line: the compounds must satisfy the Lipiniski's rule of five The query identified 43 compounds. These molecules were considered likely to be well-absorbed because satisfied Lipiniski's rule of five 3 These compounds were selected for docking analysis Observed value Virtual screening is a powerful tool to discover new structures and design new ligands of a biological target Calculated/Predicted value Fig. 3 Calculated versus observed value inhibitory activity pIC50 Fig. 2 SERM derivatives used in this study. Arg-394 Arg-394 Arg-394 His-524 Arg-394 His-524 a His-524 b His-524 c d Fig. 4 Molecular Docking: a) compound 34 (the most active compound in the training set); b) Asinex compound 1; c) Asinex compound 2; d) Raloxifene Molecular Docking 43 molecules were selected, after virtual screening and docked with the GOLD software,4 in the binding site (LBD) of ER-α. For each compound several scoring functions and a consensus scoring function were used to evaluate and rank the ER-α ligand binding affinities Asinex Compounds Experimental Activity(µM) Compound 34, the most active molecule in training set, showed higher docking score and formed H-bonding with His-524 one active site residues of ER-α (Fig.4a). In accordance, Brzozowski and coworkers revealed that His-524 and Arg-394 are key residues in the active site (Fig.4d).5 Some of the hits retrieved in database search, also showed good docking scores and formed similar type of interactions with these two active site amino acids (Fig.4b e 4c). The 12 molecules which obtained a higher GOLD docking score were considered as final compounds and subjected to biological evaluation (Fig.5) 1 26.4 2 31.8 3 40.9 4 58.5 5 60.1 6 67.4 7 124.3 8 148.5 9 170.6 10 188.4 11 >200 12 >200 Conclusion A new inclusive pharmacophore was generated for ER-α receptor, which estimated the inhibitory activity of ERMs with high accuracy (Catalyst correlation factor of 0.91). Moreover, the interactions necessary to bind ligands in the LBD were highlighted by PHERA at their proper positions. We used the pharmacophore model to perform virtual screening to discover new structures and design MCF-7 cell line inhibitors After virtual screening 43 potential hits that showed good estimated activities as well as drug-like properties, were docked with the GOLD software Compounds with higher GOLD docking scores, that showed a binding mode very similar with experimentally proved compounds (Fig.4d), were chosen for biological evaluation against MCF-7 cell line giving interesting results (Fig.5) This outcome was obtained with a novel approach to generate the pharmacophore model and now we will work to optimize these potential lead compounds to increase activity against MCF-7 cell line. These promising results encourage us to continue pursuing our target prioritization research program. Expansion of this method to predict bioactivity on the basis of relationship between activities and chemical structures is expected to direct compounds isolated in limited amounts towards targeted pharmacological testing, thereby accelerating the hit discovery process Acknowledgment: We are grateful to Prof. Vassilios Roussis and co-workers for the chemical entities and the biological assay References: (1) Dowers, T. S et al. J. Chem. Res. Toxicol. 2006, 19, 1125; (2) Catalyst 4.08, Accelrys, Inc.: 9685 Scranton Road, San Diego, CA, USA; (3) Walters, W. P.; Murko, M. A. Adv. Drug Deliv. Rev. 2002, 54, 255; (4) Verdonk, M. et al. J. Med. Chem. 2005, 48, 6504; (5) Brzozowski, A. M. et al. Nature 1997, 389, 753 Fig. 5 Biolocical evaluation of the Asinex compound isolated after Virtual Screening and Molecular Docking