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Record W3200483736 · doi:10.3390/pharmaceutics13091456

A Novel Approach to Assess the Potency of Topical Corticosteroids

2021· article· en· W3200483736 on OpenAlexaff
Michael Zvidzayi, Seeprarani Rath, Charles Bon, Sagaran Abboo, Isadore Kanfer

Bibliographic record

VenuePharmaceutics · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Toronto
FundersRhodes University
KeywordsFluocinolone acetonideClobetasol propionateMometasone furoateCorticosteroidMedicineDermatologyPotencyBlanchingPharmacologyBudesonideChemistrySurgery

Abstract

fetched live from OpenAlex

The potencies of topical corticosteroid products have mainly been classified using clinical data but in some instances, the US Food and Drug Administration’s (FDA’s) vasoconstrictor assay (VCA) to assess the skin blanching response has also been used. However, the reported skin blanching response data were often based on a single visual reading and lack information on the dose (amount/quantity) or dose duration. Although several lists classifying potencies of various topical corticosteroid products have been published, the inherent potencies of topical corticosteroid raw materials used as active pharmaceutical ingredients (APIs) have not been investigated. The objective was to rank the inherent potencies of topical corticosteroid APIs and to standardize dosing such that the relevant compounds could be compared on a normalized molar basis. The potencies of clobetasol propionate, halcinonide, mometasone furoate, and fluocinolone acetonide were compared using the resulting Emax data following the fitting of the relevant response data to the Emax model where mometasone furoate > fluocinolone acetonide = clobetasol propionate > halcinonide. This ranking lists the respective inherent potencies of the APIs, which will facilitate the choice of a suitable candidate for incorporation into an appropriate topical corticosteroid product for a specific clinical indication.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.330
GPT teacher head0.485
Teacher spread0.156 · 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 designBench or experimental
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

Citations15
Published2021
Admission routes1
Has abstractyes

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