Bibliographic record
Abstract
The present study discusses the expense associated with development, by narrowing the cost of development (raw materials) and workforce for generic drugs. During the last decade, the generic landscape has considerably evolved. The last two waves of several MM$ of off-patented drugs may be held responsible for this change. For example, the amount of money generated by statins, the proton pump inhibitors (PPIs), according to several experts, may now be difficult to reproduce. However, things cannot be seen from a monolithic way of thinking but should be foreseen with a holistic approach. The cost of generic medications is continuously declining. It is paradoxical because government organizations are raising their expectations for quality at the same time, driving up the cost of development. Furthermore, in December 2016, the FDA released revision 2 [1] of their Refuse to Receive Standards, for ANDA Submissions. After discussion with several experts who have attended numerous meetings on the topic with the FDA, data quality and regulatory operations were at the heart of this new guidance. Additionally, the generic market is facing the challenge that it will have to change its vision if it wants to survive, biologics getting more and more popular and cannot be “genericized” per se, but will become biosimilar, or second-entry biologics. This assumption is confirmed by looking at the contract research organization where the number of bioequivalence studies has been going down over the last decade. Therefore, generics will have to think outside of the box, and to date, the best compromise seems to develop new routes of administration and or new formulations of an already marketed drug substance. This short communication will illustrate a way of thinking and a summary of the generic landscape that is evolving.
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 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".