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Record W2943789407 · doi:10.1021/acs.jmedchem.8b01709

Design and Synthesis of Poly(ADP-ribose) Polymerase Inhibitors: Impact of Adenosine Pocket-Binding Motif Appendage to the 3-Oxo-2,3-dihydrobenzofuran-7-carboxamide on Potency and Selectivity

2019· article· en· W2943789407 on OpenAlexafffund
Uday Kiran Velagapudi, Marie-France Langelier, Cristina Delgado-Martín, Morgan E. Diolaiti, Sietske T. Bakker, Alan Ashworth, Bhargav A. Patel, Xuwei Shao, John M. Pascal, Tanaji T. Talele

Bibliographic record

VenueJournal of Medicinal Chemistry · 2019
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversité de Montréal
FundersNational Cancer InstituteCanadian Institutes of Health ResearchSt. John's UniversityBreast Cancer Research FoundationSusan G. Komen for the Cure
KeywordsChemistryPoly ADP ribose polymeraseStereochemistryPolymeraseCarboxamideIC50RiboseEnzymeBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Poly(adenosine 5′-diphosphate-ribose) polymerase (PARP) inhibitors are a class of anticancer drugs that block the catalytic activity of PARP proteins. Optimization of our lead compound 1 ((Z)-2-benzylidene-3-oxo-2,3-dihydrobenzofuran-7-carboxamide; PARP-1 IC50 = 434 nM) led to a tetrazolyl analogue (51, IC50 = 35 nM) with improved inhibition. Isosteric replacement of the tetrazole ring with a carboxyl group (60, IC50 = 68 nM) gave a promising new lead, which was subsequently optimized to obtain analogues with potent PARP-1 IC50 values (4–197 nM). PARP enzyme profiling revealed that the majority of compounds are selective toward PARP-2 with IC50 values comparable to clinical inhibitors. X-ray crystal structures of the key inhibitors bound to PARP-1 illustrated the mode of interaction with analogue appendages extending toward the PARP-1 adenosine-binding pocket. Compound 81, an isoform-selective PARP-1/-2 (IC50 = 30 nM/2 nM) inhibitor, demonstrated selective cytotoxic effect toward breast cancer gene 1 (BRCA1)-deficient cells compared to isogenic BRCA1-proficient cells.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.286
Teacher spread0.274 · 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 teacher head, 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

Citations49
Published2019
Admission routes2
Has abstractyes

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