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Record W2169973428 · doi:10.2174/156802611795860997

A New Perspective of Cannabinoid 1 Receptor Antagonists: Approaches Toward Peripheral CB1R Blockers without Crossing the Blood-Brain Barrier

2011· review· en· W2169973428 on OpenAlexaff
Yen‐Ku Wu, Ching-Fang Yeh, Tai Wei Ly, Ming‐Shiu Hung

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2011
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
FundersNational Health Research Institutes
KeywordsRimonabantCannabinoid receptorMedicineEndocannabinoid systemType 2 diabetesAnxietyDepression (economics)Metabolic syndromeCannabinoidPharmacologyDiabetes mellitusReceptorPsychiatryInternal medicineEndocrinologyAntagonist

Abstract

fetched live from OpenAlex

Since Rimonabant was withdrawn in Europe in 2008 because of its substantial CNS risk factors including depression and anxiety, the development of anti-obesity drugs targeting CB1R in the brain has been suspended and/or terminated globally. Instead, developing peripherally restricted CB1R antagonists is actively pursued in the hope that not only could they eliminate any CNS adverse effects observed with Rimonabant, but also maintain therapeutic benefits in metabolic syndrome, including obesity, type 2 diabetes, and non-alcoholic fatty liver diseases. In this review, we summarized the most recent advances that have been made on this area, with particular emphasis on various synthetic approaches, whereby the increase in polarity, water solubility and polar surface area were centralized on, toward potential peripheralacting CB1 antagonists. Keywords: Crossing the Blood-Brain Barrier, Metabolic disorders, Cannabinoid 1 receptor, Peripheral antagonists, SR141716A, CNS risk factors, depression, anxiety, anti-obesity drugs, antagonists, CNS adverse effects, metabolic syndrome, type 2 diabetes, non-alcoholic fatty liver diseases, water solubility, peripheralacting CB1 antagonists

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.096
GPT teacher head0.361
Teacher spread0.264 · 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.

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

Citations22
Published2011
Admission routes1
Has abstractyes

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