O3‐06‐07: GSI‐953 is a potent APP‐selective gamma‐secretase inhibitor for the treatment of Alzheimer's disease
Bibliographic record
Abstract
Gamma secretase is responsible for the intramembraneous cleavage of the Alzheimer's Precursor Protein (APP), the Notch receptor, and several other substrates. While inhibition of this protease results in potentially therapeutic reductions in the neurotoxic Abeta peptide, severe side effects might result from inhibiting Notch processing. We report a novel thiophene sulfonamide gamma-secretase inhibitor, GSI-953, that selectively inhibits cleavage of APP while sparing Notch processing. In vitro assays of Abeta production and Notch function, measurements of Abeta levels in plasma and brain of Tg2576 mouse, and assessments of cognitive function using the contextual fear conditioning model are described, as well as human plasma Abeta levels and initial biomarker data. This compound inhibits Abeta production with low nM potency in vitro in cellular and cell-free assays. Cellular assays of Notch cleavage reveal that this compound is >15-fold selective for the inhibition of APP cleavage. In the Tg2576 transgenic mouse, this compound causes a robust reduction in brain and plasma Abeta levels and reverses memory deficits that are correlated with Abeta load. A lowering of plasma Abeta levels in human demonstrates target engagement. These data demonstrate that GSI-953 is a potent and selective gamma-secretase inhibitor with potential for therapeutic utility in Alzheimer's Disease. For these reasons, GSI-953 has been advanced into human clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".