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Record W2625685960 · doi:10.1111/bph.13906

Recent advances in the development of T‐type calcium channel blockers for pain intervention

2017· review· en· W2625685960 on OpenAlexafffund
Terrance P. Snutch, Gerald W. Zamponi

Bibliographic record

VenueBritish Journal of Pharmacology · 2017
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsAlberta Children's HospitalCanada's Michael Smith Genome Sciences CentreUniversity of CalgaryMichael Smith Health Research BCUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsCalcium channelMedicineCalciumIntervention (counseling)NeurosciencePharmacologyPsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Ca v 3.2 T‐type calcium channels are important regulators of pain signals in the afferent pain pathway, and their activities are dysregulated during various chronic pain states. Therefore, it is reasonable to predict that inhibiting T‐type calcium channels in dorsal root ganglion neurons and in the spinal dorsal horn can be targeted for pain relief. This is supported by early pharmacological studies with T‐type channel blockers, such as ethosuximide, and by analgesic effects of siRNA depletion of Ca v 3.2 channels. In the past 5 years, considerable effort has been applied towards identifying novel classes of T‐type calcium channel blockers. Here, we review recent developments in the discovery of novel classes of T‐type calcium channel blockers, and their analgesic effects in animal models of pain and in clinical trials. Linked Articles This article is part of a themed section on Recent Advances in Targeting Ion Channels to Treat Chronic Pain. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.12/issuetoc

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.140
GPT teacher head0.464
Teacher spread0.324 · 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

Citations122
Published2017
Admission routes2
Has abstractyes

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