The development of a small molecule neuroserpin inhibitor towards the restoration of NGF metabolism in Alzheimer's disease
Bibliographic record
Abstract
In Alzheimer’s Disease (AD) the atrophy and degeneration of the cholinergic neurotransmitter system, beginning with the basal forebrain cholinergic neurons (BFCNs), is central to the onset of cognitive symptoms. These BFCNs rely solely on mature nerve growth factor (mNGF) for their trophic support. Our McGill lab has discovered a metabolic system controlling the availability of mNGF and found this brain metabolic pathway compromised in AD at clinical as well as preclinical stages, resulting in a substantial loss of mNGF. Thus, a dysregulation of this NGF metabolic cascade leads to the deprivation of adequate trophic support of BFCNs, therefore resulting in the well-established atrophy of these neurons and loss of their synaptic terminations in the cerebral cortex and hippocampus. Our lab hypothesizes that a pharmacological correction of this metabolism, through neuroserpin inhibition, could serve as a therapeutic route for normalizing the rate of endogenous mNGF production. Thus, restoring trophic support to the BFCNs and ameliorating the downstream cognitive decline incited by their atrophy. This Thesis covers the in silico identification, and in vitro testing (applying an in-house neuroserpin activity assay) of, small molecule candidate neuroserpin inhibitors. It also covers the initial exploration of the structural activity relationships between successful neuroserpin inhibitors and their respective binding pocket.In my thesis I will go over the rationale of a neuroserpin inhibitory therapy, the identification of target pockets within the protein neuroserpin, the in-silico identification of small molecules which can bind to these pockets, and the in-vitro testing of these small molecules for neuroserpin-specific inhibitory capacity
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".