MétaCan
Menu
Back to cohort
Record W4316040033 · doi:10.3390/pharmaceutics15010268

In Vitro–In Vivo Correlations (IVIVC) for Predicting the Clinical Performance of Metronidazole Topical Creams Intended for Local Action

2023· article· en· W4316040033 on OpenAlexaff
Seeprarani Rath, Isadore Kanfer

Bibliographic record

VenuePharmaceutics · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Toronto
FundersRhodes University
KeywordsBioequivalenceIVIVCIn vivoPharmacologyDrugChemistryPharmacokineticsStratum corneumChromatographyMedicineDissolution testingPathology

Abstract

fetched live from OpenAlex

The safety and efficacy of a generic medicine can be confirmed by demonstrating bioequivalence (BE) between the generic product and its reference listed drug (RLD) by measuring drug concentrations in the blood following administration. However, for topical dermatological products that are not absorbed into the systemic circulation, clinical trials in patients are required. The objective of this investigation was to use an in vitro method to predict in vivo performance by correlating in vitro release testing (IVRT) data with tape stripping (TS) data following the application of metronidazole (MTZ) creams to the skin of healthy human participants. Whereas IVRT is generally used to characterize the release of a drug from topical products across a synthetic membrane into a suitable receptor medium, TS involves the sequential removal of layers of stratum corneum (SC) with an adhesive tape to determine the amount of the drug in the skin. The resulting IVRT and TS data were correlated using the IVRT parameter of the apparent release constant (ARC), which is the slope obtained from the release rate profile, with the TS parameter of the area under the curve (AUC) obtained from a plot of the amount of drug per tape strip vs. the relative SC depth. A rank order relationship for these parameters was established for the reference and test products. A graph of AUC vs. ARC was plotted to establish a Level C in vitro–in vivo correlation (IVIVC). Although the ARC for T1 was slightly lower than that for the reference, the rank order was essentially consistent. A linear relationship was observed between the AUCs and ARCs. The equation derived was used to predict the AUCs for all the tested products based on their respective ARCs. The predicted AUC values based on the observed ARCs were similar to the observed AUCs. The lower and upper limits for the in vitro and in vivo parameters for BE were computed based on regulatory acceptance criteria. In order to predict BE from the IVRT studies, the values of the ARC should be between 30.50 and 47.67 when comparing test and reference cream products containing MTZ.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.287
GPT teacher head0.531
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePharmaceuticsSame topicAdvancements in Transdermal Drug DeliveryFrench-language works237,207