Marco A. C. Neves

Non-peptidic thrombospondin-1-mimics as fibroblast growth factor-2 inhibitors: an integrated strategy for the development of new antiangiogenic compounds

Endogenous inhibitors of angiogenesis, such as thrombospondin-1 (TSP-1), are promising sources of therapeutic agents to treat angiogenesis-driven diseases, including cancer. TSP-1 regulates angiogenesis through different mechanisms, including binding and sequestration of the angiogenic factor fibroblast growth factor-2 (FGF-2), through a site located in the calcium binding type III repeats. We hypothesized that the FGF-2 binding sequence of TSP-1 might serve as a template for the development of inhibitors of angiogenesis. Using a peptide array approach followed by binding assays with synthetic peptides and recombinant proteins, we identified a FGF-2 binding sequence of TSP-1 in the 15-mer sequence DDDDDNDKIPDDRDN. Molecular dynamics simulations, taking the full flexibility of the ligand and receptor into account, and nuclear magnetic resonance identified the relevant residues and conformational determinants for the peptide-FGF interaction. This information was translated into a pharmacophore model used to screen the NCI2003 small molecule databases, leading to the identification of three small molecules that bound FGF-2 with affinity in the submicromolar range. The lead compounds inhibited FGF-2-induced endothelial cell proliferation in vitro and affected angiogenesis induced by FGF-2 in the chicken chorioallantoic membrane assay. These small molecules, therefore, represent promising leads for the development of antiangiogenic agents. Altogether, this study demonstrates that new biological insights obtained by integrated multidisciplinary approaches can be used to develop small molecule mimics of endogenous proteins as therapeutic agents.

An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors.

Aromatase, an enzyme involved in the conversion of androgens into estrogens, is an important target for the endocrine treatment of breast cancer. Aromatase inhibition is usually achieved with steroids structurally related to the substrate of catalysis or, alternatively, with azole non-steroid compounds. Substituted androstenedione derivatives with Delta(1), Delta(6) and Delta(1,6) unsaturations and 6-alkyl/6-phenyl aliphatic substitutions, are among the most potent steroid aromatase inhibitors known to date. In this paper we have combined the common pharmacophoric and shape features of these molecules into a new pharmacophore model, useful for virtual screening of large compound databases. Small subsets of the best fitting anti-aromatase candidates were extracted from the NCI database and experimentally tested on an in vitro assay with human placental aromatase. New potent aromatase inhibitors were identified such as compounds 8 and 14. Considering the lack of a crystal structure for the aromatase enzyme, this ligand-based method is a valuable tool for the virtual screening of new aromatase inhibitors.

Fast three dimensional pharmacophore virtual screening of new potent non-steroid aromatase inhibitors.

Suppression of estrogen biosynthesis by aromatase inhibition is an effective approach for the treatment of hormone sensitive breast cancer. Third generation non-steroid aromatase inhibitors have shown important benefits in recent clinical trials with postmenopausal women. In this study we have developed a new ligand-based strategy combining important pharmacophoric and structural features according to the postulated aromatase binding mode, useful for the virtual screening of new potent non-steroid inhibitors. A small subset of promising drug candidates was identified from the large NCI database, and their antiaromatase activity was assessed on an in vitro biochemical assay with aromatase extracted from human term placenta. New potent aromatase inhibitors were discovered to be active in the low nanomolar range, and a common binding mode was proposed. These results confirm the potential of our methodology for a fast in silico high-throughput screening of potent non-steroid aromatase inhibitors.

Molecular Recognition and Drug-Lead Identification: What Can Molecular Simulations Tell Us?

Molecular recognition and ligand binding involving proteins underlie the most important life processes within the cell, such as substrate transport, catalysis, signal transmission, receptor trafficking, gene regulation, switching on and off of biochemical pathways. Despite recent successes in predicting the structures of many protein-substrate complexes, the dynamic aspects of binding have been largely neglected by computational/theoretical investigations. Recently, several groups have started tackling these problems with the use of experimental and simulation methods and developed models describing the variation of protein dynamics upon complex formation, shedding light on how substrate or inhibitor binding can alter protein flexibility and function. The study of ligand-induced dynamic variations has also been exploited to review the concept of allosteric changes, in the absence of major conformational changes. In this context, the study of the influence of protein motions on signal transduction and on catalytic activities has been used to develop pharmacophore models based on ensembles of protein conformations. These models, taking flexibility explicitly into account, are able to distinguish active inhibitors versus nonactive drug-like compounds, to define new molecular motifs and to preferentially identify specific ligands for a certain protein target. The application of these methods holds great promise in advancing structure-based drug discovery and medicinal chemistry in general, opening up the possibility to explore broader chemical spaces than is normally done in an efficient way. In this review, examples illustrating the extent to which simulations can be used to understand these phenomena will be presented along with examples of methodological developments to increase physical understanding of the processes and improve the possibility to rationally design new molecules.

Combining Computational and Biochemical Studies for a Rationale on the Anti-Aromatase Activity of Natural Polyphenols

Aromatase, an enzyme of the cytochrome P450 family, is a very important pharmacological target, particularly for the treatment of breast cancer. The anti‐aromatase activity of a set of natural polyphenolic compounds was evaluated in vitro. Strong aromatase inhibitors including flavones, flavanones, resveratrol, and oleuropein, with activities comparable to that of the reference anti‐aromatase drug aminoglutethimide, were identified. Through the application of molecular modeling techniques based on grid‐independent descriptors and molecular interaction fields, the major physicochemical features associated with inhibitory activity were disclosed, and a putative virtual active site of aromatase was proposed. Docking of the inhibitors into a 3D homology model structure of the enzyme defined a common binding mode for the small molecules under investigation. The good correlation between computational and biological results provides the first rationalization of the anti‐aromatase activity of polyphenolic compounds. Moreover, the information generated in this approach should be further exploited for the design of new aromatase inhibitors.

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach.

CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure–function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

Biochemical and computational insights into the anti-aromatase activity of natural catechol estrogens

High levels of endogenous estrogens are associated with increased risks of breast cancer. Estrogen levels are mainly increased by the activity of the aromatase enzyme and reduced by oxidative/conjugative metabolic pathways. In this paper, we demonstrate for the first time that catechol estrogen metabolites are potent aromatase inhibitors, thus establishing a link between aromatase activity and the processes involved in estrogen metabolism. In particular, the anti-aromatase activity of a set of natural hydroxyl and methoxyl estrogen metabolites was investigated using biochemical methods and subsequently compared with the anti-aromatase potency of estradiol and two reference aromatase inhibitors. Catechol estrogens proved to be strong inhibitors with an anti-aromatase potency two orders of magnitude higher than estradiol. A competitive inhibition mechanism was found for the most potent molecule, 2-hydroxyestradiol (2-OHE(2)) and a rational model identifying the interaction determinants of the metabolites with the enzyme is proposed based on ab initio quantum-mechanical calculations. A strong relationship between activity and electrostatic properties was found for catechol estrogens. Moreover, our results suggest that natural catechol estrogens may be involved in the control mechanisms of estrogen production.

6-[(4-Fluorophenyl)(1H-imidazol-1-yl)methyl]-1,3-benzodioxol-5-ol and 6-[(4-methoxyphenyl)(1H-imidazol-1-yl)methyl]-1,3-benzodioxol-5-ol.

The title compounds, C(17)H(13)FN(2)O(3) and C(18)H(16)N(2)O(4), are new potent aromatase inhibitors combining the common features of second- and third-generation nonsteroid anti-aromatase compounds. The molecules have a propeller shape, with dihedral angles between adjacent planes in the range 49-86°. A quantum mechanical ab initio Roothaan-Hartree-Fock calculation for the isolated molecules shows values for these angles close to the ideal value of 90°. Docking studies of the molecules in the aromatase substrate show that their strong inhibitor potency can be attributed to molecular flexibility, hydrophobic interactions, heme Fe coordination and hydrogen bonding.

6β-Methyl-B-norandrostenedione

The title compound, C(19)H(26)O(2), a B-norandrogen with a 6beta-methyl group, is a recently identified and experimentally tested potent new aromatase inhibitor. It shares structural and physicochemical similarities both with the natural substrate of the enzyme, androstenedione, and with exemestane, another potent aromatase inhibitor having a 6-methylidene group. X-ray diffraction results indicate that the B-nor molecule and exemestane have nearly the same oxygen-oxygen and methyl-methyl separations, though they have distinct configurations of the hydrophobic groups at the 6-position. These structural comparisons allow correlations to be inferred between the active site geometry of the molecules and the aromatase inhibition power of the studied compound.