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Record W4404580222 · doi:10.1021/acs.jmedchem.4c02019

Covalent Inhibitors of KEAP1 with Exquisite Selectivity

2024· article· en· W4404580222 on OpenAlexfundno aff
Imre Fejes, Piroska Markacz, János Tatai, Mónika Rudas, Petra Dunkel, Márió Gyuris, Miklós Nyerges, Nicolas Provost, Valérie Duvivier, Philippe Delerive, Virginie Martiny, Alexandra Bristiel, Brice Vidal, William Richardson, Elisabeth M. Rothweiler, Jeppe Tranberg-Jensen, Charlotte E. Manning, Melissa N. Sweeney, R. Chalk, K. Huber, Alex N. Bullock, Andras Herner, Klaus Seedorf, Cédric Vinson, Csaba Weber, András Kotschy

Bibliographic record

VenueJournal of Medicinal Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsnot available
FundersEuropean Federation of Pharmaceutical Industries and AssociationsHorizon 2020 Framework ProgrammeInnovative Medicines InitiativeKungliga Tekniska HögskolanMcGill University
KeywordsChemistrySelectivityKEAP1In vivoCovalent bondStructure–activity relationshipBiochemistryIn vitroPharmacologyStereochemistryGeneOrganic chemistryTranscription factorGeneticsBiology

Abstract

fetched live from OpenAlex

The NRF2-KEAP1 interaction is central for cytoprotection against stresses, giving it high clinical significance. Covalent modification of KEAP1 is an efficient approach, but the covalent inhibitors used in the clinic carry undesired side effects originating in their moderate selectivity. Starting with a phenotypic screen, we identified a new covalent inhibitor chemotype that was optimized to deliver a series of potent and highly selective KEAP1 binders. While the developed compounds showed both cellular and in vivo activity, upregulating antioxidant response element-dependent target genes, they showed no genotoxicity in vitro. The lead compound exhibited broad selectivity in activity-based protein profiling and showed no significant interaction with a panel of commonly studied receptors nor with a broad panel of kinases. The nature of its interaction with KEAP1 and the origin of its selectivity were revealed by X-ray crystallography.

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 head0.004
GPT teacher head0.232
Teacher spread0.228 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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Same venueJournal of Medicinal ChemistrySame topicGenomics, phytochemicals, and oxidative stressFrench-language works237,207