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Record W2973451510 · doi:10.22270/jddt.v9i4-s.3334

Challenges Faced During eCTD and CTD Filling Procedures for USFDA and Canada

2019· article· en· W2973451510 on OpenAlexaboutno aff
Nisar Ahammad, Nagarjuna Reddy, M. Nagabhushanam, Brahmaiah Ramakrishna

Bibliographic record

VenueJournal of Drug Delivery and Therapeutics · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)CTDAuthorizationBusinessRegulatory affairsPolitical scienceComputer sciencePublic administrationComputer securitySociology

Abstract

fetched live from OpenAlex

Electronic Common Technical Document (eCTD) is a topic of increasing interest in the pharmaceutical Industry as it become compulsory for filing procedures. The Common Technical Document (CTD) is a set of specification for application dossier, for the registration of Medicines and designed to be used across Europe, Japan and the United States.Quality, Safety and Efficacy information is assembled in a common format through CTD .The CTD is maintained by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). Electronic common technical documentis an interface used by applicants of marketing authorisation for medical products to submit regulatory affairs document to the agency concerned. The purpose of this article is to present a concise overview of challenges faced during eCTD & CTD submissions in United States and Canada. A regulatory process, by which a person/organization/ sponsor/innovator gets authorization to launch a drug in the market, is known as registration process. The registration process will be done by submitting technical information to the authority
 Keywords: electronic common technical document (ECTD)/ (CTD), International conference on hormonisation (ICH), Drug registration process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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