Drug tendering: drug supply and shortage implications for the uptake of biosimilars
Bibliographic record
Abstract
Due to the continued increase in global spending on health care, payers have introduced a number of programs, policies, and agreements on pharmaceutical pricing in order to control costs. While incentives to increase generic drug use have achieved significant savings, other cost-containment measures are required. Tendering is a formal procedure to purchase medications using competitive bidding for a particular contract. Although useful for cost containment, tendering can lead to decreased competition in a given market. Consequently, drug shortages can occur, resulting in changes to treatment plans to products that may have lower efficacy and/or an increased risk of adverse effects. Therefore, care must be taken to ensure that tendering does not negatively impact patient care or the health care system. A large and expanding portion of total pharmaceutical expenditure is for biologic therapies. These agents have revolutionized the treatment of many diseases, including cancer and inflammatory conditions; however, patient access to biologic drugs can be limited due to availability, insurance coverage, and cost. As branded biologic therapies reach the end of patent- and data-protection periods, biosimilars are being approved as lower-cost alternatives. Biosimilars are products that are highly similar to the originator product with no clinically meaningful differences in terms of safety, purity, or potency. As more biosimilars receive regulatory approval and adoption increases, these therapies are expected to have an impact on global health care spending and should result in overall savings. However, the use of tendering to maximize the potential benefits of biosimilars has varied across the world. Therefore, the objectives of this review are to examine the drug-tendering process and its implications on drug supply and drug shortages, as well as to describe biosimilars and how tendering may influence their uptake.
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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 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".