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Record W2157932545 · doi:10.1177/00912700022009558

Who Needs Individual Bioequivalence Studies for Narrow Therapeutic Index Drugs? A Case for Warfarin

2000· article· en· W2157932545 on OpenAlexaff
Avraham Yacobi, Eric Masson, Daniel A. Moros, Derek Ganes, Claude Lapointe MSc, Zohreh Abolfathi, Marc Lebel, Yechiel Golander, Darlene Doepner, Tamar Blumberg, Yoram Cohen, Barrie Levitt

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2000
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsCegep de Sainte Foy
Fundersnot available
KeywordsBioequivalenceWarfarinMedicinePharmacokineticsCrossover studyPharmacologyTherapeutic indexReplicateCYP2C9Internal medicineDrugStatisticsMathematicsPlacebo

Abstract

fetched live from OpenAlex

Warfarin is, among drugs, considered to have a narrow therapeutic index for which individual bioequivalence has been suggested. To establish the propriety of "switching," an individual bioequivalence study involving a replicate-design study and three "switchings" in healthy subjects was undertaken using the U.S.-brand warfarin sodium tablet and a generic product. A randomized, single-center, open-label, single-dose, four-way crossover replicate bioequivalence study was performed in 24 healthy male volunteers in which each subject received the same 5 mg warfarin test and reference tablets twice on different occasions under fasting conditions. Concentrations of warfarin in plasma were measured by a validated specific HPLC method. The individual pharmacokinetic parameters obtained with test and reference products were compared using pooled data and Liu's method. Bioequivalence was shown with both average and individual bioequivalence methods. The individual bioequivalence assessment did not show a subject-by-formulation interaction, nor did it add value to the bioequivalence assessment of warfarin.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.421
GPT teacher head0.588
Teacher spread0.167 · 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.

Study designNot applicable
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

Citations21
Published2000
Admission routes1
Has abstractyes

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