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Record W2821537775 · doi:10.22270/ijdra.v6i2.236

Regulatory requirements for Drug master file in context to Canada and Australia

2018· article· en· W2821537775 on OpenAlexfundaboutno aff
Meghna Danej, Ronak Dedania, Juhi Randeria, Kankrej Gaurav

Bibliographic record

VenueInternational Journal of Drug Regulatory Affairs · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
FundersHealth Canada
KeywordsLegislationContext (archaeology)TerminologyDrugBusinessQuality (philosophy)Product (mathematics)Computer sciencePolitical sciencePharmacologyMedicineLawBiology

Abstract

fetched live from OpenAlex

Drug Master Files are required in most countries as supporting documents for the registration of drug products. DMFs generally contain information pertaining to the chemistry, manufacturing and controls (CMC) sections of the drug submission and reflect the drug’s identity, strength, purity and quality. Canada and Australia which are consider as highly regulated markets (HRMs). In CANADA, DMF filing was done through New Drug Submission (NDS) for both drugs and biologic products. They use MF terminology for DMF which contain four types of MASTER FILE- ASMFs, CCS MFs, Excipient MFs, Drug product MFs. In AUSTRALIA different application processes and regulatory requirements apply depending on the type of therapeutic goods that is applied. They consist of eight phase for DMF registration. Where EU guidelines adopted in Australia include references to EU legislation. Now from 2016 onwards most of the regulated countries will use eCTD or their electronic format for their DMF submission. Compare DMF regulatory requirements in the above-mentioned countries so that reader can have clear idea on how to file DMF.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.299
Teacher spread0.242 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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