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Record W2785965975 · doi:10.18433/j3x93k

A Survey of the Regulatory Requirements for BCS-Based Biowaivers for Solid Oral Dosage Forms by Participating Regulators and Organisations of the International Generic Drug Regulators Programme

2018· review· en· W2785965975 on OpenAlexaffvenue
Joy Elizabeth Van Oudtshoorn, Alfredo García‐Arieta, Gustavo Mendes Lima Santos, Christopher Crane, Clare Rodrigues, Craig Simon, Ji Myoung Kim, Sang Aeh Park, Yusuke Okada, Ryosuke Kuribayashi, Chantal Pfäffli, Arno Nolting, Iván Omar Calderón Lojero, Zulema Rodríguez Martínez, Wen-Yi Hung, April C. Braddy, Nancy Arciniegas Leal, Diego Gutierrez Triana, Mitch Clarke, Peter Bachmann

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2018
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsHealth Canada
Fundersnot available
KeywordsBioequivalenceBiopharmaceutics Classification SystemPharmacologyEquivalence (formal languages)Regulatory scienceBiopharmaceuticsDrugMedicineComputer scienceMathematicsChemistryPharmacokineticsPathology

Abstract

fetched live from OpenAlex

PURPOSE: The Biopharmaceutics Classification System (BCS) based biowaiver is a scientific model which enables the substitution of in vivo bioequivalence studies with in vitro data as evidence of therapeutic equivalence subject to certain conditions. Despite being based on the same principles, BCS-based biowaivers are interpreted and regulated differently among international regulatory agencies. In this survey, the Bioequivalence Working Group (BEWG) of the International Generic Drug Regulators Programme (IGDRP) compared the criteria for BCS-based biowaivers applied by the participating regulators and organisations. METHODS: Differences and similarities regarding solubility, permeability, dissolution, excipients and fixed-dose combination products, were identified and compared in a detailed survey of each participant's criteria for BCS-based biowaivers. These criteria were determined based upon the participants' respective regulatory guidance documents, policies and practices. RESULTS: This review has, with the exception of two participants who do not accept BCS-based biowaivers, revealed that most IGDRP participants interpret the BCS principles and conditions similarly but notable differences exist in the application of these principles. Conclusion: Although many similarities exist, this review identifies several opportunities for greater convergence of regulatory requirements amongst the surveyed jurisdictions. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.411
GPT teacher head0.526
Teacher spread0.116 · 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
Published2018
Admission routes2
Has abstractyes

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