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Record W3189225536 · doi:10.3389/fmed.2021.702776

A Review of the Efficacy and Safety for Biologic Agents Targeting IL-23 in Treating Psoriasis With the Focus on Tildrakizumab

2021· review· en· W3189225536 on OpenAlexafffund
Feras M. Ghazawi, Farhan Mahmood, Leon Kircik, Yves Poulin, Marc Bourcier, Ronald Vender, Marni Wiseman, Charles Lynde, Ivan V. Litvinov

Bibliographic record

VenueFrontiers in Medicine · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsMcGill UniversityUniversity of TorontoUniversity of ManitobaMcMaster UniversityUniversité de SherbrookeUniversité LavalUniversity of Ottawa
FundersFonds de Recherche du Québec - SantéSun Pharma
KeywordsPsoriasisMedicineInterleukin 23PathogenesisImmunologyDiseaseImmune systemTumor necrosis factor alphaInterleukin 17Internal medicine

Abstract

fetched live from OpenAlex

Psoriasis is a chronic and debilitating inflammatory immune-mediated skin disorder. Several cytokines including interleukin (IL)-23 were demonstrated to play a central role in the pathogenesis of this disease. Treatment options for psoriasis range from topical to systemic modalities, depending on the extent, anatomical locations involved and functional impairment level. Targeting cytokines or their cognate receptors that are involved in disease pathogenesis such as IL-12/23 (i.e., targeting the IL-12p40 subunit shared by these cytokines), IL-17A, IL-17F, IL-17RA, and TNF-α using biologic agents emerged in recent years as a highly effective therapeutic option for patients with moderate-to-severe disease. This review provides an overview of the important role of IL-23 signaling in the pathogenesis of psoriasis. We describe in detail the available IL-23 inhibitors for chronic plaque psoriasis. The efficacy, pharmacokinetic properties, and the safety profile of one of the most recent IL-23 biologic agents (tildrakizumab) are evaluated and reviewed in depth.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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 designOther design
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

Citations23
Published2021
Admission routes2
Has abstractyes

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