MétaCan
Menu
Back to cohort
Record W4281745809 · doi:10.1111/bcpt.13759

Application of <i>E</i><sub>max</sub> model to assess the potency of topical corticosteroid products

2022· article· en· W4281745809 on OpenAlexaff
Seeprarani Rath, Michael Zvidzayi, Charles Bon, Isadore Kanfer

Bibliographic record

VenueBasic & Clinical Pharmacology & Toxicology · 2022
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsOntario Drug Policy Research NetworkUniversity of Toronto
Fundersnot available
KeywordsPotencyMometasone furoateClobetasol propionatePharmacologyBlanchingChemistryCorticosteroidMedicineInternal medicineIn vitroBiochemistryFood science

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to compare the potencies of two topical corticosteroid products (TCPs) using the E max model to fit the skin blanching responses obtained from the US FDA's vasoconstrictor assay (VCA) and to illustrate the influence of formulation on potency. The potencies of two marketed TCPs, Dermovate® cream containing clobetasol propionate (CP) and Elocon® cream containing mometasone furoate (MF), were assessed using healthy human subjects. In order to investigate the influence of formulation and associated vehicle properties, the creams were compared with their respective topical corticosteroids (TCs) from a previously published study wherein the inherent potencies of those TCs were assessed using a validated VCA method. Whereas the inherent potency of MF ( E max = −94.45 ± 0.21) was found to be greater than CP ( E max = −58.80 ± 15.65), when formulated as creams, the TCP containing CP had a higher potency ( E max = −86.15 ± 0.17) than that containing MF ( E max = −42.61 ± 26.04). This reversal of potency may be attributed to the effect of formulation factors. The comparison of the potencies of TCPs with inherent potencies of their corresponding TCs confirmed the influence of formulation parameters on the potency of those products.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.400
Teacher spread0.324 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBasic & Clinical Pharmacology & ToxicologySame topicDermatology and Skin DiseasesFrench-language works237,207