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Record W4396697650 · doi:10.1080/17425255.2024.2349711

Evaluating the pharmacokinetics of upadacitinib for the treatment of moderate-to-severe Crohn’s disease

2024· article· en· W4396697650 on OpenAlexaff
Ilaria Faggiani, Ferdinando D’Amico, Francesca Futura Bernardi, Sarah Bencardino, Mariangela Allocca, Federica Furfaro, Tommaso Lorenzo Parigi, Alessandra Zilli, Gionata Fiorino, Laurent Peyrin‐Biroulet, Silvio Danese

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPharmacokineticsMedicineCrohn's diseaseTherapeutic drug monitoringDiseasePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Janus kinases (JAK) are enzymes involved in signaling pathways that activate the immune system. Upadacitinib, an oral small molecule, is the first JAK inhibitor approved by FDA and EMA for the treatment of moderately to severely active Crohn's disease (CD), following successful phase II and III trials. Compared to other JAK inhibitors, upadacitinib has a high selectivity toward JAK1. This characteristic could improve its efficacy and safety. AREAS COVERED: This review provides an overview of the available knowledge on the pharmacokinetics of upadacitinib as induction and maintenance therapy for CD. EXPERT OPINION: The approval of newer targeted small molecules drug, including JAK inhibitors, marked a significant advancement in terms of effectiveness. In fact, the oral administration, the rapid absorption, the excellent bioavailability and the short serum time of maximum concentration are some of the advantages compared to biologics. The selective inhibition of JAK1 by upadacitinib allows for high efficacy while maintaining a reliable safety profile.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.380
Teacher spread0.335 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Drug Metabolism & ToxicologySame topicInflammatory Bowel DiseaseFrench-language works237,207