(+)-(2R,5S)-4-[4-Cyano-3-(trifluoromethyl)phenyl]-2,5-dimethyl-N-[6-(trifluoromethyl)pyridin-3- yl]piperazine-1-carboxamide (YM580) as an Orally Potent and Peripherally Selective Nonsteroidal Androgen Receptor Antagonist

Kinoyama, Isao; Taniguchi, Nobuaki; Toyoshima, Akira; Nozawa, Eisuke; Kamikubo, Takashi; Imamura, Masakazu; Matsuhisa, Akira; Samizu, Kiyohiro; Kawanimani, Eiji; Niimi, Tatsuya; Hamada, Naoko; Koutoku, Hiroshi; Furutani, Takashi; Kudoh, Masafumi; Okada, Minoru; Ohta, Mitsuaki; Tsukamoto, Shin‐ichi

doi:10.1021/jm050293c

Cited by 21 publications

(26 citation statements)

References 50 publications

(121 reference statements)

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“…For three proteins (UniProt ID P10275 (androgen receptor), P11299 (muscarinic acetylcholine receptor M1) and P353367 (histamine H1 receptor)), ligand data which were not included in the DrugBank dataset were collected from relevant literature [16]–[18] and public databases, PDSP Ki database [19] and GLIDA [20], in February 2008. Overall, 35 androgen receptor-ligand pairs, 49 muscarinic acetylcholine receptor M1-ligand pairs, and 1,060 histamine H1 receptor-ligand pairs were supplemented.…”

Section: Resultsmentioning

confidence: 99%

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

et al. 2009

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Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space.

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Section: Resultsmentioning

confidence: 99%

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

et al. 2009

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“…From the second approach, we created YM155, a selective survivin suppressant for multiple tumors (14,15). From the third approach, we created potential treatments for hormone-dependent cancers including YM511, an aromatase inhibitor (16), and YM580, a non-steroidal androgen receptor antagonist (17). Through our experience in these drug discovery programs and subsequent clinical trials, we had fostered the notion that we must deliver novel therapies for improving the health of well-defined populations of patients suffering from a number of cancers.…”

Section: Our Research Approachmentioning

confidence: 99%

Astellas' Drug Discovery Strategy: Focus on Oncology

Yanagita

Takenaka

2012

Japanese Journal of Clinical Oncology

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“… N ‐Arylpiperazine derivatives 49 – 55 . The IC 50 values were measured on the CHO cell line (data taken from References 41–43). …”

Section: Nonsteroidal Antiandrogensmentioning

confidence: 99%

“…YM175735 ( 53 ) showed about a fourfold higher activity than R ‐bicalutamide ( 2 ) as an AR antagonist, and had a slightly increased in vivo antiandrogenic activity. However, it exhibited little activity in mature rats, probably due to the high lipophilicity (PrologD 7.4 =4.61 calculated using PALLAS),43 thereby penetrating the blood–brain barrier and interrupting the negative feedback of testosterone on gonadotropin secretion. Hence, it presumably increased the serum testosterone level, which can inhibit AR antagonists in the central nervous system (CNS) 43.…”

Section: Nonsteroidal Antiandrogensmentioning

confidence: 99%

Developments in Nonsteroidal Antiandrogens Targeting the Androgen Receptor

Liu¹,

Su²,

Geng³

et al. 2010

ChemMedChem

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Addition of catalytic amounts of a phosphite‐based gold(I) catalyst efficiently triggers the intermolecular [2+2] cycloaddition of allenes and alkenes substituted by electron‐donor groups. The reaction is fast and furnishes cyclobutane derivatives in a stereoselective manner using low catalyst loadings. Besides, the same catalyst selectively affords homodimerization products from the starting allene, a process that may be conveniently fine tuned by addition of norbornene.

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(+)-(2R,5S)-4-[4-Cyano-3-(trifluoromethyl)phenyl]-2,5-dimethyl-N-[6-(trifluoromethyl)pyridin-3- yl]piperazine-1-carboxamide (YM580) as an Orally Potent and Peripherally Selective Nonsteroidal Androgen Receptor Antagonist

Cited by 21 publications

References 50 publications

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Astellas' Drug Discovery Strategy: Focus on Oncology

Developments in Nonsteroidal Antiandrogens Targeting the Androgen Receptor

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