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Record W2931930663 · doi:10.1021/acs.jmedchem.9b00141

Structure- and Ligand-Based Discovery of Chromane Arylsulfonamide Na<sub>v</sub>1.7 Inhibitors for the Treatment of Chronic Pain

2019· article· en· W2931930663 on OpenAlexaff
Steven J. McKerrall, Teresa T. Nguyen, Kwong Wah Lai, Philippe Bergeron, Lunbin Deng, Antonio G. DiPasquale, Jae H. Chang, Jun Chen, Tania Chernov-Rogan, David H. Hackos, Jonathan Maher, Daniel F. Ortwine, Jodie Pang, Jian Payandeh, William R. Proctor, Shannon D. Shields, Jennifer Vogt, Pengfei Ji, Wenfeng Liu, Elisa Ballini, Lilia Schumann, Glauco Tarozzo, Girish Bankar, Sultan Chowdhury, Abid Hasan, J. P. Johnson, Kuldip Khakh, Sophia Lin, Charles J. Cohen, Christoph M. Dehnhardt, Brian S. Safina, Daniel P. Sutherlin

Bibliographic record

VenueJournal of Medicinal Chemistry · 2019
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsXenon Pharmaceuticals (Canada)
FundersGenentech
KeywordsChemistryLigand efficiencyLipophilicityLigand (biochemistry)In vivoStereochemistryPotencyMutagenesisStructure–activity relationshipIn vitroSelectivityLead compoundPharmacologyCombinatorial chemistryBiochemistryReceptorMutation

Abstract

fetched live from OpenAlex

Using structure- and ligand-based design principles, a novel series of piperidyl chromane arylsulfonamide Nav1.7 inhibitors was discovered. Early optimization focused on improvement of potency through refinement of the low energy ligand conformation and mitigation of high in vivo clearance. An in vitro hepatotoxicity hazard was identified and resolved through optimization of lipophilicity and lipophilic ligand efficiency to arrive at GNE-616 (24), a highly potent, metabolically stable, subtype selective inhibitor of Nav1.7. Compound 24 showed a robust PK/PD response in a Nav1.7-dependent mouse model, and site-directed mutagenesis was used to identify residues critical for the isoform selectivity profile of 24.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.251
Teacher spread0.243 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

Explore more

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