Human Stakeholders and the Use of Animals in Drug Development
Bibliographic record
Abstract
Abstract Pharmaceutical firms seek to fulfill their responsibilities to stakeholders by developing drugs that treat diseases. We evaluate the social and financial costs of developing new drugs relative to the realized benefits and find the industry falls short of its potential. This is primarily due to legislation‐mandated reliance on animal test results in early stages of the drug development process, leading to a mere 10 percent success rate for new drugs entering human clinical trials. We cite hundreds of biomedical studies from journals including Nature , Science , and the Journal of the American Medical Association to show animal modeling is ineffective, misleading to scientists, unable to prevent the development of dangerous drugs, and prone to prevent the development of useful drugs. Legislation still requires animal testing prior to human testing even though the pharmaceutical sector has better options that were unavailable when animal modeling was first mandated. We propose that the U.S. Food and Drug Administration (FDA) and Congress should work together to abolish regulations and policies that require animal use. Doing so will benefit pharmaceutical industry stakeholders, including patients whose health depends on drugs and the many people who rely on the financial well‐being of pharmaceutical firms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".