The Three Rs in the pharmaceutical industry: perspectives of scientists and regulators
Bibliographic record
Abstract
Abstract Six drug regulatory reviewers and 11 pharmaceutical industry scientists were interviewed to explore their perspectives on the obstacles and opportunities for greater implementation of the Three Rs (replacement, reduction, refinement) in drug research and development. Participants generally supported the current level of animal use in the pharmaceutical industry and viewed in vitro methods as supporting, but not replacing, the use of animals. Obstacles to greater use of the Three Rs cited by participants included the lack of non-animal alternatives; requirements for statistical validity; reluctance by industry and regulators to depart from established patterns of animal use; the priority of commercial objectives ahead of the Three Rs; and concern that less animal testing could jeopardise human safety. Opportunities identified for the Three Rs included the development of better animal models including genetically modified (GM) animals; pursuit of more basic knowledge, notably drug action on gene expression; re-use of animals; greater use of pilot studies; using sufficient numbers of animals per test to avoid repeating inconclusive studies; regular review of animal data in regulatory requirements; and following the regulatory option of combining segments of reproductive toxicology studies into one study. In some areas, greater implementation of the Three Rs seemed well aligned with industry priorities, for example, phenotypic characterisation of GM animals and validation of alternative methods. In other areas, wider use of the Three Rs may require building consensus on areas of disagreement including the usefulness of death as an endpoint; the suitability of re-using animals; and whether GM animals and the use of pilot studies contribute to reduction.
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 | 0.289 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.047 | 0.019 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.052 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".