Promising animal models for amyotrophic lateral sclerosis drug discovery: a comprehensive update
Bibliographic record
Abstract
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons. Several animal models have been generated to understand ALS pathogenesis. They have provided valuable insight into disease mechanisms and the development of therapeutic strategies. AREAS COVERED: In this review, the authors provide a concise overview of simple genetic model organisms, including C. elegans, Drosophila, zebrafish, and mouse genetic models that have been generated to study ALS. They emphasize the benefits of each model and their application in translational research for discovering new chemicals, gene therapy approaches, and antibody-based strategies for treating ALS. EXPERT OPINION: Significant progress is being made in identifying new therapeutic targets for ALS. This progress is being enabled by promising animal models of the disease using increasingly effective genetic and pharmacological strategies. There are still challenges to be overcome in order to achieve improved success rates for translating drugs from animal models to clinics for treating ALS. Several promising future directions include the establishment of novel preclinical protocol standards, as well as the combination of animal models with human induced pluripotent stem cells (iPSCs).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".