Nonsteroidal progesterone receptor antagonists based on a conformationally-restricted subseries of 6-aryl-1,2-dihydro-2,2,4-trimethylquinolines

Hamann, Lawrence G.; Winn, David T.; Pooley, Charlotte L. F.; Tegley, Christopher M.; West, Sarah J.; Farmer, Luc J.; Zhi, Lin; Edwards, James P.; Marschke, Keith B.; Mais, Dale E.; Goldman, Mark E.; Jones, Todd K.

doi:10.1016/s0960-894x(98)00482-x

Cited by 23 publications

(20 citation statements)

References 18 publications

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“…Internal validation using leave-one-out (LOO) cross-validation gave a correlation coefficient q 2 LOO of 0.637 and a standard error of prediction (SDEP LOO ) of 0.480. A more rigorous cross-validation using 10 random groups yielded an average q 2 10 of 0.601 and SDEP 10 The most rigorous test for the predictive ability of the model was done with the 10 external test set compounds, which were completely excluded from model building but were processed in the same way as the training set compounds. The chosen test set provides both structurewise and activitywise a good representation of the compounds used to build the model.…”

Section: Resultsmentioning

confidence: 99%

“…[10][11][12][13][14] The primary objective with 3D QSAR modeling was to identify the physicochemical properties that have a substantial effect on the binding affinity of the ligands included in the analysis. An additional goal was to derive a 3D QSAR model of PR ligands that is comparable to our recently published 3D QSAR model of nonsteroidal AR ligands.…”

Section: Introductionmentioning

confidence: 99%

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Docking and Three-Dimensional Quantitative Structure−Activity Relationship (3D QSAR) Analyses of Nonsteroidal Progesterone Receptor Ligands

Söderholm

Lehtovuori²,

Nyrönen³

2006

J. Med. Chem.

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We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Docking and Three-Dimensional Quantitative Structure−Activity Relationship (3D QSAR) Analyses of Nonsteroidal Progesterone Receptor Ligands

Söderholm

Lehtovuori²,

Nyrönen³

2006

J. Med. Chem.

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“…In addition to the abovediscussed nonsteroidal antiandrogens, selective estrogen receptor modulators (SERMs) and nonsteroidal modulators for progesterone receptor have been successfully developed (Hamann et al, 1998;Mitlak and Cohen, 1999;Weryha et al, 1999;Zhi et al, 1998Zhi et al, , 2000. These nonsteroidal ligands are known for their better receptor selectivity than steroidal ligands, and are more flexible in structural modification for optimal physicochemical, pharmacokinetic, and pharmacologic properties.…”

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confidence: 99%

Key Structural Features of Nonsteroidal Ligands for Binding and Activation of the Androgen Receptor

et al. 2003

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The purposes of the present studies were to examine the androgen receptor (AR) binding ability and in vitro functional activity of multiple series of nonsteroidal compounds derived from known antiandrogen pharmacophores and to investigate the structure-activity relationships (SARs) of these nonsteroidal compounds. The AR binding properties of sixty-five nonsteroidal compounds were assessed by a radioligand competitive binding assay with the use of cytosolic AR prepared from rat prostates. The AR agonist and antagonist activities of highaffinity ligands were determined by the ability of the ligand to regulate AR-mediated transcriptional activation in cultured CV-1 cells, using a cotransfection assay. Nonsteroidal compounds with diverse structural features demonstrated a wide range of binding affinity for the AR. Ten compounds, mainly from the bicalutamide-related series, showed a binding affinity superior to the structural pharmacophore from which they were derived. Several SARs regarding nonsteroidal AR binding were revealed from the binding data, including stereoisomeric conformation, steric effect, and electronic effect. The functional activity of high-affinity ligands ranged from antagonist to full agonist for the AR. Several structural features were found to be determinative of agonist and antagonist activities. The nonsteroidal AR agonists identified from the present studies provided a pool of candidates for further development of selective androgen receptor modulators (SARMs) for androgen therapy. Also, these studies uncovered or confirmed numerous important SARs governing AR binding and functional properties by nonsteroidal molecules, which would be valuable in the future structural optimization of SARMs.

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“…Binding affinity of nonsteroidal molecules in baculo-virus expressed hPR-A receptor analyses the interactions with the nuclear receptors to agonists, antagonists or partial agonists [9]. The nonsteroidal substituted quinoline derivatives [10] (Fig. 1a–1c) and cyclocymopol monomethyl ethers [7] (Fig.…”

Section: Introductionmentioning

confidence: 99%

Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators

Pal

Islam²,

Hossain³

et al. 2011

Sci. Pharm.

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Considering the significance of progesterone receptor (PR) modulators, the present study is explored to envisage the biophoric signals for binding to selective PR subtype-A using ligand-based quantitative structure activity relationship (QSAR) and pharmacophore space modeling studies on nonsteroidal substituted quinoline and cyclocymopol monomethyl ether derivatives. Consensus QSAR models (Training set (Tr): nTr=100, R2pred=0.702; test set (Ts): nTs=30, R2pred=0.705, R2m=0.635; validation set (Vs): nVs=40, R2pred=0.715, R2m=0.680) suggest that molecular topology, atomic polarizability and electronegativity, atomic mass and van der Waals volume of the ligands have influence on the presence of functional atoms (F, Cl, N and O) and consequently contribute significant relations on ligand binding affinity. Receptor independent space modeling study (Tr: nTr=26, Q2=0.927; Ts: nTs=60, R2pred=0.613, R2m=0.545; Vs: nVs=84, R2pred=0.611, R2m=0.507) indicates the importance of aromatic ring, hydrogen bond donor, molecular hydrophobicity and steric influence for receptor binding. The structure-function characterization is adjudged with the receptor-based docking study, explaining the significance of the mapped molecular attributes for ligand-receptor interaction in the catalytic cleft of PR-A.

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Nonsteroidal progesterone receptor antagonists based on a conformationally-restricted subseries of 6-aryl-1,2-dihydro-2,2,4-trimethylquinolines

Cited by 23 publications

References 18 publications

Docking and Three-Dimensional Quantitative Structure−Activity Relationship (3D QSAR) Analyses of Nonsteroidal Progesterone Receptor Ligands

Docking and Three-Dimensional Quantitative Structure−Activity Relationship (3D QSAR) Analyses of Nonsteroidal Progesterone Receptor Ligands

Key Structural Features of Nonsteroidal Ligands for Binding and Activation of the Androgen Receptor

Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators

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