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Record W2325404207 · doi:10.1158/1538-7445.am2013-1070

Abstract 1070: New potent inhibitors of the androgen receptor that target its BF3 surface binding site.

2013· article· en· W2325404207 on OpenAlexaboutno aff
Ravi S. N. Munuganti, Fuqiang Ban, Huifang Li, Eric Leblanc, Peter Axerio, Kate Frewin, Emma Tomlinson Guns, Artem Cherkasov, Paul S. Rennie

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsTransactivationAndrogen receptorBicalutamideProstate cancerLNCaPAntiandrogensChemistryCancer researchBinding siteIn silicoDihydrotestosteronePharmacologyCancerAndrogenBiochemistryBiologyInternal medicineMedicineTranscription factorHormone

Abstract

fetched live from OpenAlex

Abstract In the development and progression of prostate cancer, the androgen receptor (AR) plays a critical role and its hormone/ligand-binding site is the primary therapeutic target for all antiandrogens (eg Bicalutamide, MDV3100) currently used to treat advanced, metastatic forms of this disease. While treatment with these AR inhibitors initially suppresses prostate tumor growth, resistance to these drugs invariably emerges. Accordingly, there exists a clinical need for the development of new AR antagonists with different chemical structures and different mechanisms of action for targeting and inhibiting AR transactivation. To this end, we have focused on a surface pocket on the AR called Binding Function 3 (BF3). Applying iterative in silico screening to millions of chemicals in the ZINC database, coupled with biological screening for AR binding and activity, we identified several compounds that can bind directly to the BF3 pocket and inhibit AR transactivation. Cell-based screening assays for inhibition of a fluorescent ARE-reporter and PSA expression, together with Biolayer Interferometry analysis for measuring binding to the AR, revealed several chemically distinct BF-3 binders that showed significant IC50 inhibitions of the AR in the nano-molar to low micro-molar range. Validation for binding to the BF3 site was performed by x-ray crystallography. Furthermore, none of these compounds were able to displace DHT from the hormone binding site even at concentrations more than 10-fold higher than their IC50 values. In addition to wild type LNCaP prostate cancer cells, these BF3 binders effectively inhibited cell proliferation (MTS assay) and PSA expression in derived MDV3100-resistant prostate cancer cell lines, but had no effect on cell viability of AR-negative prostate cancer cell lines. In summary, we have identified an entirely new class of anti-androgens that bind to the BF3 surface site of the AR and inhibit its transactivation by a mechanism which does not involve binding to its hormone/ligand binding site and thereby bypasses the treatment resistance seen with conventional antiandrogens. (Supported by funds from the PCF, Canada Safeway, and CIHR). Citation Format: Ravi Munuganti, Fuqiang Ban, Huifang Li, Eric LeBlanc, Peter Axerio, Kate Frewin, Emma Tomlinson Guns, Artem Cherkasov, Paul S. Rennie. New potent inhibitors of the androgen receptor that target its BF3 surface binding site. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1070. doi:10.1158/1538-7445.AM2013-1070

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: Bench or experimental · Consensus signal: Bench or experimental
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)0.0010.000
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 head0.104
GPT teacher head0.384
Teacher spread0.280 · 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 designBench or experimental
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

Citations1
Published2013
Admission routes1
Has abstractyes

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