BIOSIMILARS: OPPORTUNITIES, CHALLENGES, AND THE GENERAL PRINCIPLES GOVERNING THEIR DEVELOPMENT AROUND THE GLOBE
Bibliographic record
Abstract
Biologic drugs have revolutionized the treatment of many life-threatening and rare illnesses such as cancer and autoimmune diseases. Biologics are broadly referred as substances that are produced by living cells and are used in the treatment, prevention, or diagnosis of diseases. They include a wide range of substances, such as genetic material, antibodies, vaccines, or processes which act by influencing cellular processes that block disease or affect diseased cells. Biologics have become striking treatment options and the size of the market has grown hastily. It is expected that by 2023, most of the patents will expire in the European Union opening a large potential market. Keeping this in mind, the ability to launch substitutes to original biologics, also known as biosimilars, presents many opportunities to generic companies. The field of biosimilars seems to be “breaking” the traditional division between the creations of innovative NCE-based medicines by research-based companies, on the one hand, and, on the other hand, mapping of these medicines by the generic companies. The field of biosimilars so far presents some considerable challenges, namely, regulatory, safety, economic, and legal which are still being debated and discussed in different forums. In this article, we have tried to summarize the general principles and regulations governing the development of biosimilars by regulatory authorities such as the World Health Organization, European Medicines Agency, US Food and Drug Administration, and Health Canada. Furthermore, we have tried to throw some light on the opportunities, challenges, and current scenarios pertaining to biosimilars.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".