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Record W2046632683 · doi:10.1158/1538-7445.am2014-2508

Abstract 2508: The development of anti-androgens with a novel mechanism of action for treatment of castration-resistant prostate cancer

2014· article· en· W2046632683 on OpenAlexaff
Ravi S. N. Munuganti, Mohamed D.H. Hassona, Eric Leblanc, Fuqiang Ban, Emma Tomlinson Guns, Paul S. Rennie, Artem Cherkasov

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAndrogen receptorProstate cancerEnzalutamideLNCaPCoactivatorCancer researchDocking (animal)Androgen Receptor AntagonistsIn silicoMechanism of actionBinding siteChemistryCancerComputational biologyPharmacologyMedicineBiologyTranscription factorBiochemistryInternal medicineIn vitroGene

Abstract

fetched live from OpenAlex

Abstract The androgen receptor (AR) is one of the most validated therapeutic targets in prostate cancer (PCa). Conventional anti-androgens lose effectiveness as cancer therapeutics because anti-androgen resistance usually develops after long term treatment. The challenge is that the current therapeutics bind to the same site of the AR (hormone binding pocket) and act via the same mode, to which the receptor has already developed effective resistance mechanisms. Hence, there is a pressing need for novel therapeutics that inhibit the AR through novel, alternative modes of action. Recent studies have identified a novel binding pocket on the surface of AR called binding function 3 (BF3) that is important for the AR transcriptional activity. In order to identify compounds that specifically bind to BF3 site and inhibit the AR, we conducted a systematic in silico screen (that included large-scale docking, in-site rescoring, and consensus voting procedures) followed by experimental validation of the identified hit molecules. As a result, we have discovered a novel chemical series of indoles as lead BF3 inhibitors. One of the most potent inhibitors identified, VPC-13163, demonstrated an IC50 of 0.31µM in AR eGFP transcriptional assay. Confirming it as a true BF3 binder, VPC-13163 neither displaced the co-activator from an alternative coactivator binding site, activation function 2 site, nor androgen from the hormone binding pocket. Additionally, the Biolayer Interferometry assay detected direct reversible interactions between the AR ligand binding domain and the inhibitor. VPC-13163 demonstrated strong anti-proliferative activity against LNCaP and Enzalutamide-resistant prostate cancer cell lines (MR49F) whereas it did not affect the growth of AR independent PC3 cell line. It also inhibits prostate specific antigen (PSA) in both LNCaP and MR49F and reduces expression of AR target genes, PSA and TMPRSS2. These findings suggest that VPC-13163 exhibits AR BF3 specific mechanism of action. Furthermore, VPC-13163 reduces AR-dependent growth of xenograft tumors in vivo. Based on these outcomes, it can be anticipated that such drug prototypes will lay a foundation for the development of alternative or supplementary small-molecule therapies capable of combating PCa even in its drug resistant forms. Because the emergence of castration resistance is the lethal end stage of the disease, we anticipate that the proposed research will eventually have a substantial impact on patient survival. Citation Format: Ravi Shashi Nayana Munuganti, Mohamed DH Hassona, Eric Leblanc, Fuqiang Ban, Emma T. Guns, Paul S. Rennie, Artem Cherkasov. The development of anti-androgens with a novel mechanism of action for treatment of castration-resistant prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2508. doi:10.1158/1538-7445.AM2014-2508

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.166
GPT teacher head0.438
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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