Tau pathology in neurodegenerative disease: disease mechanisms and therapeutic avenues
Bibliographic record
Abstract
Tauopathies are disorders associated with tau protein dysfunction and insoluble tau accumulation in the brain at autopsy. Multiple lines of evidence from human disease, as well as nonclinical translational models, suggest that tau has a central pathologic role in these disorders, historically thought to be primarily related to tau gain of toxic function. However, a number of tau-targeting therapies with various mechanisms of action have shown little promise in clinical trials in different tauopathies. We review what is known about tau biology, genetics, and therapeutic mechanisms that have been tested in clinical trials to date. We discuss possible reasons for failures of these therapies, such as use of imperfect nonclinical models that do not predict human effects for drug development; heterogeneity of human tau pathologies which may lead to variable responses to therapy; and ineffective therapeutic mechanisms, such as targeting of the wrong tau species or protein epitope. Innovative approaches to human clinical trials can help address some of the difficulties that have plagued our field's development of tau-targeting therapies thus far. Despite limited clinical success to date, as we continue to refine our understanding of tau's pathogenic mechanism(s) in different neurodegenerative diseases, we remain optimistic that tau-targeting therapies will eventually play a central role in the treatment of tauopathies.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".