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Record W4390744213 · doi:10.1080/14728214.2024.2303116

Novel and emerging drugs for the treatment of Crohn’s disease: a review of phase II and III trials

2024· review· en· W4390744213 on OpenAlexaff
Panu Wetwittayakhlang, Talat Bessissow, Péter L. Lakatos

Bibliographic record

VenueExpert Opinion on Emerging Drugs · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCrohn's diseaseJanus kinaseClinical trialDiseaseVedolizumabNarrative reviewInflammatory bowel diseaseIntensive care medicineBioinformaticsPharmacologyImmunologyInternal medicineCytokine

Abstract

fetched live from OpenAlex

INTRODUCTION: Crohn's disease (CD) is a chronic inflammatory bowel disease characterized by unpredictable flare-ups and periods of remission. While several therapeutic options, such as anti-tumor necrosis factor (TNF), anti-integrin, and interleukin (IL) 12/23 inhibitors, as well as IL-23 and Janus kinase (JAK) inhibitors, have been approved for CD treatment, a substantial number of patients fail to respond adequately or experience a loss of response over time. In recent years, the scientific community has been actively investigating novel agents to address these challenges and improve the management of CD. AREAS COVERED: This comprehensive narrative review provides an overview of recent developments in CD treatment, summarizing phase 2 and phase 3 clinical trial data. We delve into the clinical efficacy and safety profiles of emerging therapies, encompassing JAK inhibitors, IL-23 inhibitors, anti-adhesion molecules, S1P1 receptor modulators, and combined targeted treatments. EXPERT OPINION: The armamentarium of CD therapeutic agents is constantly expanding. We analyze pivotal findings from phase 2 and phase 3 CD treatment trials. We also underscore the existing gaps in therapy and the paramount role of ongoing research and innovation in CD management.

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)0.000
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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.053
GPT teacher head0.407
Teacher spread0.354 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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