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Record W2945204410 · doi:10.1186/s10194-019-0992-1

Effects of monoclonal antagonist antibodies on calcitonin gene-related peptide receptor function and trafficking

2019· article· en· W2945204410 on OpenAlexaff
Raffi Manoukian, Hong Sun, Silke Miller, Di Shi, Brian Chan, Cen Xu

Bibliographic record

VenueThe Journal of Headache and Pain · 2019
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsAmgen (Canada)
FundersAmgen
KeywordsCalcitonin gene-related peptideInternalizationReceptorMonoclonal antibodyCalcitoninAntagonistCell biologyMedicineEndocrinologyPharmacologyInternal medicineAntibodyBiologyImmunologyNeuropeptide

Abstract

fetched live from OpenAlex

BACKGROUND: Monoclonal antibodies against calcitonin gene-related peptide (CGRP) or its receptor are efficacious for the prevention of migraine headaches. The downstream molecular mechanisms following ligand-receptor blockade by which these antibodies prevent CGRP signaling through CGRP receptors have not been demonstrated. METHODS: Here we produced tool monoclonal functional antagonist antibodies against CGRP and its canonical receptor and developed a novel cellular model using fluorogen-activated protein technology that allows detection of CGRP receptor internalization by flow cytometry and, for an extended time course, visualization by confocal microscopy. RESULTS: Using this cell model we showed that these antagonist antibodies block both CGRP-induced cAMP signaling and CGRP receptor internalization. At least 10-fold higher concentrations of either antibody are necessary to block CGRP receptor internalization compared with cAMP accumulation in our cell model. CONCLUSION: These data reinforce our understanding of how monoclonal functional antagonist antibodies interfere with CGRP signaling.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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 designObservational
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

Citations27
Published2019
Admission routes1
Has abstractyes

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