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Record W7034774204

Vias alternativas de registro de medicamentos: análise de dados da ANVISA

2021· article· en· W7034774204 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrioritizationDiseaseDrug approvalDrugRegulatory authorityMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Since the late 1980s and early 1990s, Alternative Drug Registration Regulatory Routes (ADRR) have been regulated by the Reference Regulatory Agencies, with the aim of accelerating the evaluation of new drugs with a positive risk-benefit ratio, usually for serious illnesses, when the disease morbidity is significant or when the disease is potentially fatal or where there is an unmet medical need, causing patients and sponsors to tolerate greater risks, including not fully knowing the drug. ADRRs can increase the level of communication and commitment between the registrant and the regulatory agency, can give a greater role to surrogate drug efficacy outcomes, and shift part of the burden of proof of clinical benefit and of the generation of safety evidence from pre to post registration. For this paper we conducted a descriptive retrospective bibliographic research on the alternative regulatory pathways developed in several countries, mainly in the US, Canadian and European Reference Regulatory Agencies as well as in the Singapore Regulatory Agency. At ANVISA, an analytical study was carried out in the ANVISA database (DATAVISA), in the registration and post-registration submissions of drugs prioritized in 2018, with the purpose of preparing a newsletter with the data obtained from the research. In Brazil two alternative ways of drug registration are regulated: the prioritization of analysis, regulated by CBR (Collegiate Board Resolution) N° 204/2017 and the special procedure for treatment, diagnosis and prevention of rare diseases, regulated by CBR N° 205/2017. Of the 16 registration petitions prioritized by CBR N° 204/2017, which were granted, 7 concern drugs that are object of Productive Development Partnership (PDP), among which are antineoplastic, antipsychotic, anti-inflammatory and recombinant human growth hormone, 7 petitions concern generic unprecedented drugs and 2 petitions concern drugs for neglected, serious debilitating disease or public health emergency, being an antineoplastic and a protein kinase inhibitor. Of the post-registration petitions prioritized by CBR N° 204/2017, which were granted, 8 concern antineoplastic drug applications, among which 6 are characterized for treating serious debilitating diseases, one is a PDP drug and one is a drug with risk of shortage with an impact on Public Health. Among the 11 petitions classified for special procedure for rare diseases by CBR N° 205/2017, which were granted, there are antineoplastic drugs, a respiratory system drug, a replacement enzyme, a Duchenne muscular dystrophy drug, a protein kinase inhibitor and a drug for X-linked hypophosphatemia. The importance of Alternative Registration Routes in patients’ access to life-saving medicines is unquestionable, however proactive action by the three stakeholders is important as the Regulatory Authority works to shorten the time needed for marketing authorization, registrants expedite studies, production and marketing, and patient associations support rapid access to drugs and monitor their availability. This joint effort to make timely medications available that increase life expectancy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.243
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.243
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0310.047
Science and technology studies0.0020.004
Scholarly communication0.0060.004
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.254
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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