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