A brain-shuttled antibody targeting alpha synuclein aggregates for the treatment of synucleinopathies
Bibliographic record
Abstract
Abstract Parkinson’s disease and multiple system atrophy are members of a class of devastating neurodegenerative diseases called synucleinopathies, which are characterized by the presence of alpha-synuclein (α-Syn) rich aggregates in the brains of patients. Passive immunotherapy targeting these aggregates is an attractive disease-modifying strategy. Such an approach must not only demonstrate target selectivity towards α-Syn aggregates, but also achieve appropriate brain exposure to have the desired therapeutic effect. Here we present preclinical data for a next-generation antibody for the treatment of synucleinopathies. SAR446159 (ABL301) is a bispecific antibody composed of an α-Syn-binding immunoglobulin (IgG) and an engineered insulin-like growth factor receptor 1 (IGF1R) binding single-chain variable fragment (scFv), acting as a shuttle to transport an antibody across the blood-brain barrier (BBB). SAR446159 binds tightly and preferentially to α-Syn aggregates and prevents their seeding capacity in vitro and in vivo . Incubation with SAR446159 reduced α-Syn preformed fibrils (PFFs) uptake in neurons and facilitated uptake and clearance by microglia. In wild type mice injected in the striatum with α-Syn PFFs, treatment with SAR446159 reduced the spread of aSyn pathology as measured by phosphorylated α-Syn staining and lessened the severity of motor phenotypes. Additionally, in 9-month-old transgenic mice overexpressing α-Syn (mThy1-α-Syn, Line 61), repeated treatment with SAR446159 reduced markers of α-Syn aggregation in the brain. SAR446159 had significantly higher brain and CSF penetration over a sustained period than its monospecific counterpart (1E4) in rats and monkeys. The binding properties of SAR446159 combined with its brain-shuttle technology make it a potent, next-generation immunotherapeutic for treating synucleinopathies.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".