Bibliographic record
Abstract
Different regulatory authorities regulate the drug development in various countries of the world. Various Regulatory authority for generic drug application Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceutical and Medical Devices Agency (PMDA), Health Product and Food Branch (HPFB) Central Drug Standard of Organization (CDSO). Generic manufacturers may file an abbreviated New Drug Application (ANDA) that incorporates the safety/effectiveness data submitted by original innovator drug manufacturer and adds only bioequivalence studies. Therefore it is very difficult and challenging task to approve a drug by the manufacturing companies, simultaneously submitted in all the regulatory authorities. Regulatory authorities are responsible to ensure the quality, safety, and efficacy including manufacturing, distribution of the drug product. There is lot of challenges for the pharmaceutical industry to development and filling of generic drug application, which can be overcoming by the common format of submission. This is due to the different regulatory procedure of the various countries. Through the international conference on harmonization (ICH) process common technical documents (CTD) has been developed for USA, EU, JAPAN, INDIA AND CANADA. There are few differences in the dossier submission for among these five regions. To development of any generic drug product still we need strategic planning by these regulatory authorities. Keywords: Generic drug, Regulatory authority, Drug development, FDA, ANDA, ICH
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".