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Incretin Signaling Neighborhoods and Adverse Drug Reactions

2025· review· en· W4414109894 on OpenAlexfundno aff
Shane C. Wright, Peter Lindquist, Mette M. Rosenkilde, Volker M. Lauschke

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsnot available
FundersScience for Life LaboratoryVetenskapsrådetNovo Nordisk FondenSvenska Sällskapet för Medicinsk ForskningCancerfondenEli Lilly and CompanyEuropean Foundation for the Study of DiabetesRobert Bosch StiftungEuropean CommissionNovo NordiskDiamond Light SourceMcGill UniversityEuropean Federation of Pharmaceutical Industries and AssociationsKungliga Tekniska Högskolan
KeywordsIncretinDrugType 2 diabetesReceptorDrug reactionFunctional selectivityAdverse effectAction (physics)

Abstract

fetched live from OpenAlex

In light of the success of blockbuster drugs for type 2 diabetes and obesity based on the GLP-1 hormone, drugmakers have concentrated their efforts on developing new and improved variations that address the route of administration, dosing, pathway selectivity, or polypharmacology. While some of these modifications have demonstrated improved efficacy in clinical studies and offered exciting opportunities for treating other diseases, drug-induced shifts to the conformational landscape of target receptors may have consequences for side effects. Our review summarizes advances in the understanding of the biochemistry, pharmacogenomics, and molecular pharmacology of incretins and their cognate receptors. We further highlight the current landscape of incretin mimetics and discuss how differences in compartmentalized pathway selectivity affect drug action and outcomes.

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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.012
GPT teacher head0.341
Teacher spread0.328 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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