Disparity of outcomes: the limits of modeling amyotrophic lateral sclerosis in murine models and translating results clinically
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastatingly progressive neurodegenerative disorder with multiple underlying etiological factors contributing to disease pathogenesis. Despite intensive research efforts and therapeutic development, disease presentation in ALS remains largely intractable to intervention. To date, the most common rodent model used in pre-clinical drug development accounts for a small proportion of the affected patient population and is predicated upon the significant overexpression of a mutant form of the human antioxidant protein, superoxide dismutase 1 (mSOD1). After more than 50 clinical trials, there is an alarming paucity of positive outcomes at the clinical level of ALS therapeutics with strong supporting pre-clinical data in mSOD1 models. Potential reasons for the negative clinical results are multifactorial in nature and include an overly reductionist model system that is heavily influenced by individual transgene level variation, as well as attempting to widely apply findings derived from a model of specific genetic causality to a patient population where the majority of cases are of unknown etiology. With such a tremendous disease burden and a lack of therapeutic options, it is critical that the research community re-evaluate the dependence on mSOD1 pre-clinical models as the gold standard prior to translating findings at the clinical level. Here we briefly review both the clinical and pre-clinical findings of select therapeutics, discuss the limitations of pre-clinical mSOD1 models, and suggest future stratagems that could aid in the clinical translation of efficacious therapeutic agents.
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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 | 0.067 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".