An Overview of Phenserine Tartrate, A Novel Acetylcholinesterase Inhibitor for the Treatment of Alzheimers Disease
Bibliographic record
Abstract
Existing cholinesterase (ChE) inhibitor therapies for Alzheimer's disease (AD), while effective in improving cognitive, behavioral and functional impairments, do not alter disease progression. Novel drug design studies have focused on the classical ChE inhibitor, (-)-physostigmine, producing alterations in chemical composition and three-dimensional structure, which may offer an improved therapeutic index. The phenylcarbamate derivative, (-)-phenserine, is a selective, non-competitive inhibitor of acetylcholinesterase (AChE). In vivo, (-)-phenserine produces rapid, potent, and long-lasting AChE inhibition. As a possible result of its preferential brain selectivity, (-)-phenserine is significantly less toxic than (-)-physostigmine. In studies using the Stone maze paradigm, (-)-phenserine has been shown to improve cognitive performance in both young learning-impaired and elderly rats. In addition to reducing inactivation of acetylcholine in the brain, (-)-phenserine appears to have a second mode of action. Reduced secretion of beta-amyloid (Abeta) has been observed in cell lines exposed to (-)-phenserine, occurring through translational regulation of beta-amyloid precursor protein (beta-APP) mRNA via a non-cholinergic mechanism. These in vitro findings appear to translate in vivo into animal models and humans. In a small study of patients with AD, (-)-phenserine treatment tended to reduce beta-APP and Abeta levels in plasma samples. Clinical studies also reveal that (-)-phenserine (5-10 mg b.i.d.) had a favorable safety and pharmacological profile, produced significant improvements in cognitive function and was well tolerated in patients with AD treated for 12 weeks. Further randomized, double-blind, placebo-controlled Phase III studies assessing the efficacy, safety/tolerability and potential disease-modifying effects of (-)-phenserine in patients with AD are currently ongoing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".