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Record W4323538374 · doi:10.1007/s00406-023-01571-4

Esmethadone-HCl (REL-1017): a promising rapid antidepressant

2023· review· en· W4323538374 on OpenAlexaff
Maurizio Fava, Stephen M. Stahl, Sara De Martin, Andrea Mattarei, Ezio Bettini, Stefano Comai, Andrea Alimonti, Francesco Bifari, Luca Pani, Franco Folli, Clotilde Guidetti, Alberto Furlan, Jacopo Sgrignani, Patrizia Locatelli, Andrea Cavalli, Cedric O’Gorman, Sergio Traversa, Charles E. Inturrisi, M. Pappagallo, Paolo L. Manfredi

Bibliographic record

VenueEuropean Archives of Psychiatry and Clinical Neuroscience · 2023
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsMcGill University
Fundersnot available
KeywordsMemantineMajor depressive disorderAntidepressantNMDA receptorNeuroscienceMedicinePharmacologyDementiaPsychologyDiseasePsychiatryHippocampusReceptorInternal medicine

Abstract

fetched live from OpenAlex

This review article presents select recent studies that form the basis for the development of esmethadone into a potential new drug. Esmethadone is a promising member of the pharmacological class of uncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonists that have shown efficacy for major depressive disorder (MDD) and other diseases and disorders, such as Alzheimer's dementia and pseudobulbar affect. The other drugs in the novel class of NMDAR antagonists with therapeutic uses that are discussed for comparative purposes in this review are esketamine, ketamine, dextromethorphan, and memantine. We present in silico, in vitro, in vivo, and clinical data for esmethadone and other uncompetitive NMDAR antagonists that may advance our understanding of the role of these receptors in neural plasticity in health and disease. The efficacy of NMDAR antagonists as rapid antidepressants may advance our understanding of the neurobiology of MDD and other neuropsychiatric diseases and disorders.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.132
GPT teacher head0.425
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2023
Admission routes1
Has abstractyes

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Same venueEuropean Archives of Psychiatry and Clinical NeuroscienceSame topicTreatment of Major DepressionFrench-language works237,207