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Record W4400095907 · doi:10.1016/j.yrtph.2024.105666

The need for guidance in antidepressant drug development: Revisiting the role of the forced swim test and tail suspension test

2024· review· en· W4400095907 on OpenAlexaff
Emily R. Trunnell, Stephen Farghali, Tara A. Jackson, Kimberley Jayne, Rachel Vickers‐Smith, Tina Stibbe

Bibliographic record

VenueRegulatory Toxicology and Pharmacology · 2024
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsTail suspension testTest (biology)Behavioural despair testAntidepressantPsychologyDrugDrug developmentSuspension (topology)PharmacologyMedicinePsychiatryMathematicsBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.335
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations33
Published2024
Admission routes1
Has abstractyes

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