Anti-amyloid treatment is broadly effective in neuronopathic mucopolysaccharidoses and synergizes with gene therapy in MPS-IIIA
Bibliographic record
Abstract
Mucopolysaccharidoses (MPSs) are childhood diseases caused by inherited deficiencies in glycosaminoglycan degradation. Most MPSs involve neurodegeneration, which to date is untreatable. Currently, most therapeutic strategies aim at correcting the primary genetic defect. Among these strategies, gene therapy has shown great potential, although its clinical application is challenging. We have shown previously in an MPS-IIIA mouse model that the molecular tweezer (MT) CLR01, a potent, broad-spectrum anti-amyloid small molecule, inhibits secondary amyloid storage, facilitates amyloid clearance, and protects against neurodegeneration. Here, we demonstrate that combining CLR01 with adeno-associated virus (AAV)-mediated gene therapy, targeting both the primary and secondary pathologic storage in MPS-IIIA mice, results in a synergistic effect that improves multiple therapeutic outcomes compared to each monotherapy. Moreover, we demonstrate that CLR01 is effective therapeutically in mouse models of other forms of neuronopathic MPS, MPS-I, and MPS-IIIC. These strongly support developing MTs as an effective treatment option for neuronopathic MPSs, both on their own and in combination with gene therapy, to improve therapeutic efficacy and translation into clinical application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".