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Record W2531923638 · doi:10.1021/acs.jmedchem.6b01194

Design and Discovery of Functionally Selective Serotonin 2C (5-HT<sub>2C</sub>) Receptor Agonists

2016· article· en· W2531923638 on OpenAlexaff
Jianjun Cheng, John D. McCorvy, Patrick M. Giguère, Hu Zhu, Terry Kenakin, Bryan L. Roth, Alan P. Kozikowski

Bibliographic record

VenueJournal of Medicinal Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Mental Health
KeywordsChemistryFunctional selectivityAgonistArrestinSerotonin5-HT2C receptorSerotonergic5-HT receptorReceptorPharmacologyDesensitization (medicine)G protein-coupled receptorPartial agonistIntrinsic activitySignal transductionBiochemistryBiology

Abstract

fetched live from OpenAlex

On the basis of the structural similarity of our previous 5-HT 2C agonists with the melatonin receptor agonist tasimelteon and the putative biological cross-talk between serotonergic and melatonergic systems, a series of new (2,3-dihydro)benzofuran-based compounds were designed and synthesized. The compounds were evaluated for their selectivity toward 5-HT 2A, 5-HT 2B, and 5-HT 2C receptors in the calcium flux assay with the ultimate goal to generate selective 5-HT 2C agonists. Selected compounds were studied for their functional selectivity by comparing their transduction efficiency at the G protein signaling pathway versus β-arrestin recruitment. The most functionally selective compound (+)- 7e produced weak β-arrestin recruitment and also demonstrated less receptor desensitization compared to serotonin in both calcium flux and phosphoinositide (PI) hydrolysis assays. We report for the first time that selective 5-HT 2C agonists possessing weak β-arrestin recruitment can produce distinct receptor desensitization properties.

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)0.000
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 score0.046
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.008
GPT teacher head0.217
Teacher spread0.210 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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