Trilostane, an inhibitor of 3β-hydroxysteroid dehydrogenase, has an agonistic activity on androgen receptor in human prostate cancer cells

Takizawa, Itsuhiro; Nishiyama, Tsutomu; Hara, Noboru; Hoshii, Tatsuhiko; Ishizaki, Fumio; Miyashiro, Yoshimichi; Takahashi, Kota

doi:10.1016/j.canlet.2010.05.015

Cited by 15 publications

(12 citation statements)

References 21 publications

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“…Moreover, according to our prediction results, trilostane, an inhibitor of 3β-hydroxysteroid dehydrogenase for treating the Cushing's syndrome, can bind to the androgen receptor, an important therapeutic target in prostate cancer. Such a predicted interaction agreed with a previous experimental observation [64] that trilostane can induce agonistic activity on the androgen receptor in human prostate cancer cells. Our predictions also showed that benzphetamine can act on amine oxidases, which can be supported by the known facts that benzphetamine is a substituted amphetamine and amphetamine can be used an effective inhibitor of copper-containing amine oxidases [65].…”

Section: Novel Drug-target Interaction Predictionssupporting

confidence: 90%

Deep learning with feature embedding for compound-protein interaction prediction

Wan

Zeng

2016

Preprint

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Accurately identifying compound-protein interactions in silico can deepen our understanding of the mechanisms of drug action and significantly facilitate the drug discovery and development process. Traditional similarity-based computational models for compound-protein interaction prediction rarely exploit the latent features from current available large-scale unlabelled compound and protein data, and often limit their usage on relatively small-scale datasets. We propose a new scheme that combines feature embedding (a technique of representation learning) with deep learning for predicting compound-protein interactions. Our method automatically learns the low-dimensional implicit but expressive features for compounds and proteins from the massive amount of unlabelled data. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline for accurate compound-protein interaction prediction, even when the interaction knowledge of compounds and proteins is entirely unknown. Evaluations on current large-scale databases of the measured compound-protein affinities, such as ChEMBL and BindingDB, as well as known drug-target interactions from DrugBank have demonstrated the superior prediction performance of our method, and suggested that it can offer a useful tool for drug development and drug repositioning.

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Section: Novel Drug-target Interaction Predictionssupporting

confidence: 90%

Deep learning with feature embedding for compound-protein interaction prediction

Wan

Zeng

2016

Preprint

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“…LNCaP cells were used between passages 20–40 to ensure androgen sensitivity [van Bokhoven et al, 2003]. The AR is wild type in LAPC4 and VCaP cells, but mutant in LNCaP and 22Rv1 cells [Arnold et al, 2008; Attardi et al, 2004; Denmeade et al, 2003; Furr et al, 1987; Takizawa et al, 2010]. Unless otherwise noted, all studies examining the effects of supplementation with R1881 or CXCL12 utilize serum-free, phenol red-free media.…”

Section: Methodsmentioning

confidence: 99%

The CXCL12/CXCR4 axis promotes ligand-independent activation of the androgen receptor

Kasina

Macoska

2012

Molecular and Cellular Endocrinology

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The molecular mechanisms responsible for the transition of some prostate cancers from androgen ligand-dependent to androgen ligand -independent are incompletely established. Molecules that are ligands for G protein coupled receptors (GPCRs) have been implicated in ligand-independent androgen receptor (AR) activation. The purpose of this study was to examine whether CXCL12, the ligand for the GPCR, CXCR4, might mediate prostate cancer cell proliferation through AR-dependent mechanisms involving functional transactivation of the AR in the absence of androgen. The results of these studies showed that activation of the CXCL12/CXCR4 axis promoted: The nuclear accumulation of both wild-type and mutant AR in several prostate epithelial cell lines; AR-dependent proliferative responses; nuclear accumulation of the AR co-regulator SRC-1 protein; SRC-1:AR protein:protein association; co-localization of AR and SRC-1 on the promoters of AR-regulated genes; AR- and SRC-1 dependent transcription of AR-regulated genes; AR-dependent secretion of the AR-regulated PSA protein; P13K-dependent phosphorylation of AR; MAPK-dependent phosphorylation of SRC-1, and both MAPK- and P13K-dependent secretion of the PSA protein, in the absence of androgen. Taken together, these studies identify CXCL12 as a novel, non-steroidal growth factor that promotes the growth of prostate epithelial cells through AR-dependent mechanisms in the absence of steroid hormones. These findings support the development of novel therapeutics targeting the CXCL12/CXCR4 axis as an ancillary to those targeting the androgen/AR axis to effectively treat castration resistant/recurrent prostate tumors.

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“…et al 51. In brief, the culture medium was extracted with ethyl acetate, and then the extracts were purified using a solid-phase extraction cartridge.…”

Section: Methodsmentioning

confidence: 99%

Dihydrotestosterone synthesis pathways from inactive androgen 5α-androstane-3β,17β-diol in prostate cancer cells: Inhibition of intratumoural 3β-hydroxysteroid dehydrogenase activities by abiraterone

Ando

Nishiyama

Takizawa

et al. 2016

Sci Rep

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Intratumoural dihydrotestosterone (DHT) synthesis could be an explanation for castration resistance in prostate cancer (PC). By using liquid chromatography-mass spectrometry, we evaluated the intratumoral DHT synthesis from 5α-androstane-3β,17β-diol (3β-diol), which is inactive androgen metabolized from DHT. 3β-diol had biochemical potential to be converted to DHT via three metabolic pathways and could stimulate PC cell growth. Especially, 3β-diol was not only converted back to upstream androgens such as dehydroepiandrosterone (DHEA) or Δ5-androstenediol but also converted directly to DHT which is the main pathway from 3β-diol to DHT. Abiraterone had a significant influence on the metabolism of DHEA, epiandrosterone and 3β-diol, by the inhibition of the intratumoural 3β-hydroxysteroid dehydrogenase (3β-HSD) activities which is one of key catalysts in androgen metabolic pathway. The direct-conversion of 3β-diol to DHT was catalysed by 3β-HSD and abiraterone could inhibit this activity of 3β-HSD. These results suggest that PC had a mechanism of intratumoural androgen metabolism to return inactive androgen to active androgen and intratumoural DHT synthesis from 3β-diol is important as one of the mechanisms of castration resistance in PC. Additionally, the inhibition of intratumoural 3β-HSD activity could be a new approach to castration-resistant prostate cancer treatment.

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Trilostane, an inhibitor of 3β-hydroxysteroid dehydrogenase, has an agonistic activity on androgen receptor in human prostate cancer cells

Cited by 15 publications

References 21 publications

Deep learning with feature embedding for compound-protein interaction prediction

Deep learning with feature embedding for compound-protein interaction prediction

The CXCL12/CXCR4 axis promotes ligand-independent activation of the androgen receptor

Dihydrotestosterone synthesis pathways from inactive androgen 5α-androstane-3β,17β-diol in prostate cancer cells: Inhibition of intratumoural 3β-hydroxysteroid dehydrogenase activities by abiraterone

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