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Record W4388341603 · doi:10.1007/s13555-023-01054-3

Next Generation PDE4 Inhibitors that Selectively Target PDE4B/D Subtypes: A Narrative Review

2023· review· en· W4388341603 on OpenAlexaff
Andrew Blauvelt, Richard G. Langley, Kenneth B. Gordon, Jonathan I. Silverberg, Kilian Eyerich, Morten Otto Alexander Sommer, Jakob Felding, Richard B. Warren

Bibliographic record

VenueDermatology and Therapy · 2023
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersNovo Nordisk FondenLundbeckfonden
KeywordsAtopic dermatitisMedicinePsoriasisNarrative reviewDermatologyImmunologyPharmacologyIntensive care medicine

Abstract

fetched live from OpenAlex

For decades, topical corticosteroids have been the mainstay of treatment for mild-to-moderate inflammatory skin diseases, even though only short-term use is approved for these agents and systemic inflammation is not addressed. Increased understanding of the immunopathogenesis of these conditions, especially for psoriasis and atopic dermatitis, has facilitated the development of antibody-based drugs that neutralize single key cytokines or their associated receptors, such as interleukin (IL)-17A/F, IL-23, and IL-17RA in psoriasis and IL-13 and IL-4Rα in atopic dermatitis. However, oral therapy is still preferred by many patients owing to the ease of use and needle-free administration. Phosphodiesterase 4 (PDE4) inhibitors have been approved for both oral and topical use for inflammatory skin diseases. In this review, we present a summary of an emerging class of selective PDE4B/D inhibitors under clinical development and compare the differences in selectivity of this new generation of PDE4 inhibitors with the less selective currently approved PDE4 inhibitors.

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)0.000
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.100
GPT teacher head0.363
Teacher spread0.263 · 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

Citations43
Published2023
Admission routes1
Has abstractyes

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