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Dual Alleviation of Acute and Neuropathic Pain by Fused Opioid Agonist-Neurokinin 1 Antagonist Peptidomimetics

2015· article· en· W2290385391 on OpenAlexafffund
Cecilia Betti, Joanna Starnowska-Sokół, Joanna Mika, Jolanta Dyniewicz, Łukasz Frankiewicz, Alexandre Novoa, Marta Bochyńska, Attila Keresztes, Piotr Kossoń, Wioletta Makuch, Joost Van Duppen, Nga N. Chung, Jozef Vanden Broeck, Andrzej W. Lipkowski, Peter W. Schiller, Frans Janssens, Marc Ceusters, François Sommen, Theo Meert, Barbara Przewłocka, Dirk Tourwé, Steven Ballet

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsMontreal Clinical Research Institute
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of HealthVlaamse regeringNarodowe Centrum NaukiMinisterstwo Edukacji i NaukiFonds Wetenschappelijk Onderzoek
KeywordsPharmacologyOpioidBuprenorphineAntagonistAnalgesicMorphineAgonistNeuropathic painPharmacophoreChemistryMedicineNociceptionReceptorStereochemistryBiochemistry

Abstract

fetched live from OpenAlex

Herein, the synthesis and biological evaluation of dual opioid agonists-neurokinin 1 receptor (NK1R) antagonists is described. In these multitarget ligands, the two pharmacophores do not overlap, and this allowed maintaining high NK1R affinity and antagonist potency in compounds 12 and 13. Although the fusion of the two ligands resulted in slightly diminished opioid agonism at the μ- and δ-opioid receptors (MOR and DOR, respectively), as compared to the opioid parent peptide, balanced MOR/DOR activities were obtained. Compared to morphine, compounds 12 and 13 produced more potent antinociceptive effects in both acute (tail-flick) and neuropathic pain models (von Frey and cold plate). Similarly to morphine, analgesic tolerance developed after repetitive administration of these compounds. To our delight, compound 12 did not produce cross-tolerance with morphine and high antihyperalgesic and antiallodynic effects could be reinstated after chronic administration of each of the two compounds.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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Same venueACS Medicinal Chemistry LettersSame topicNeuropeptides and Animal PhysiologyFrench-language works237,207