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Demonstrating bioequivalence using clinical endpoint studies

2012· article· en· W1498026393 on OpenAlexaff
Eden Bermingham, Jérôme R. E. del Castillo, Chantal Lainesse, Kirby Pasloske, Stephen E. Radecki

Bibliographic record

VenueJournal of Veterinary Pharmacology and Therapeutics · 2012
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsHealth CanadaUniversité de Montréal
Fundersnot available
KeywordsBioequivalenceClinical endpointClinical trialMedicineEndpoint DeterminationMedical physicsClinical study designClinical studyBioavailabilityPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

For drug products not amenable to blood level studies, clinical endpoint studies have been used as an indirect measure of formulation difference in bioavailability between test and reference products. However, clinical endpoint studies are not as sensitive in detecting formulation differences as blood level studies and offer numerous challenges to both regulatory authorities and sponsors. The objective of this article is not to suggest new regulatory policies, but to explore new methodologies and alternative solutions to clinical endpoint bioequivalence (BE) studies, which are used when a blood level study is not considered to be appropriate. To achieve this objective, this article identifies situations where a clinical endpoint study might be appropriate, lists the advantages and disadvantages of this type of study design, and discusses possible alternative solutions. It is concluded that future evidence-based research is needed to explore new methodologies such as clinical trial simulations of various study designs, new statistical methods, and new in vitro methods to demonstrate BE.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
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 head0.896
GPT teacher head0.707
Teacher spread0.188 · 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 designTheoretical or conceptual
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

Citations8
Published2012
Admission routes1
Has abstractyes

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