Bioequivalent Drugs: Towards A Needed Holistic Paradigm Shift?
Bibliographic record
Abstract
The author of this short communication has been working in and teaching drug development for the last 20 years.He has been involved in more than 100 drug development projects, including drug delivery, medical device, biologics, innovators and generic drugs.He has also been involved in all the steps that are needed to file properly to different governmental agencies investigational new drug applications (IND), clinical trial applications (CTA), abbreviated and new drug applications/submissions (ANDA\S; NDA/S), 505b2, 510k and biologics legal applications (BLA).After several other interactions with all the other actors, such as Health Canada (HC), the Food and Drug Agency (FDA), the European Medicines Agency (EMA), pharmacovigilance companies and consultants, public relation companies, insurance companies and especially patients, the author has decided to gather all the comments in order to initiate a kind of a new debate on the innovators and generic drugs.However, the goal of this current expert opinion is not to generate conflicts, or to compare generic versus innovator drugs in the sense that one is better than the other.The author has already been involved in several bioequivalence studies, comparing two innovator products where the results showed lack of bioequivalence….or in bioequivalence studies evaluated with clinical endpoint where generic drug products were more potent than the innovators…The goal of this paper should be more based on the following question: are the current methods used to assess bioequivalence are suitable and reliable enough to "stamp" that generic drugs are bioequivalent, are as stable, reliable
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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 | 0.060 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.021 | 0.042 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.018 | 0.036 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".