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Drug Master File Filing in US, Europe, Canada and Australia

2017· article· en· W2738563879 on OpenAlexaboutno aff
Indu Gurram, M. V. S. Kavitha, Nagarjuna Reddy, M. Nagabhushanam

Bibliographic record

VenueJournal of Pharmaceutical Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsDrugBusinessMedicinePharmacology

Abstract

fetched live from OpenAlex

Purpose : A Drug Master File (DMF) is a submission to the Food and Drug Administration (FDA) that may be used to provide confidential detailed information about facilities, processes, or articles used in the manufacturing, processing, packaging, and storing of one or more human drugs. The submission of a DMF is not required by law or FDA regulation, it is submitted solely at the discretion of the DMF holder. The DMF contains factual and complete information on a drug product's chemistry, manufacture, stability, purity, impurity profile, packaging, and the cGMP status of any human drug product. The present work gives the Detailed idea on how to file Drug Master File in US, EUROPE, CANADA, AUSTRALIA. Approach : A Drug Master File is a submission of information to the FDA to permit the FDA to review this information in support of a third party's submission without revealing the information to the third party. In US, DMF filing was done through NDA for drugs, ANDA for generics and BLA for Biologics. In Europe, DMF filing was done through MAA via centralized procedure for eligible products and for other products via decentralized procedure was used. In CANADA, DMF filing was done through NDS for both drugs and biologic products, where as in AUSTRALIA different application processes and regulatory requirements apply depending on the type of therapeutic goods that is applied. Findings : This gives you clear vision on how to file Drug master file in US, EUROPE, CANADA and AUSTRALIA. This paper also gives you the comparison of DMF fling procedure in the above-mentioned countries so that reader can have clear idea on how to file DMF and different concerns on DMF among the above counties. Conclusion : The DMF contains factual and complete information on a drug product's chemistry, manufacture, stability, purity, impurity profile, Packaging and the cGMP status of any Drug product for humans. The content and the format for Drug Master File is used to obtain marketing Authorization. The main objective of the DMF is to support regulatory requirements of a medicinal product to prove its quality, safety and efficacy. This helps to obtain a marketing authorization grant. Now from 2016 onwards most of the regulated countries will use eCTD or their electronic format for their DMF submission.

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)0.003
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.383
GPT teacher head0.460
Teacher spread0.076 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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