The need for guidance in antidepressant drug development: Revisiting the role of the forced swim test and tail suspension test
Bibliographic record
Abstract
Depressive disorders are one of the most common mental disorders globally and progress in treating these disorders has been hampered, in part, by a lack of suitable nonclinical efficacy tests. Two common tests used in nonclinical efficacy studies of antidepressants-the forced swim test (FST) and tail suspension test (TST)-have come under criticism in recent years for their inconsistency and lack of validity, yet they continue to be used in the pharmaceutical industry. In this review, we provide a rationale for why international pharmaceutical regulatory and guidance agencies should begin issuing direction on methods for non-clinical efficacy testing that traditionally use the FST and TST, particularly considering that some regulators, such as those in the U.S. and E.U., allow the authorization of clinical trials to proceed without requiring tests in animals. The area of antidepressant drug discovery represents an important opportunity for reducing the attrition of psychiatric drugs, harmonizing regulatory requirements, and reducing animal use. Specific recommendations for the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) have been provided.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".