<i>In Silico</i> and <i>Ex Vivo</i> Analyses of the Inhibitory Action of the Alzheimer Drug Posiphen and Primary Metabolites with Human Acetyl- and Butyrylcholinesterase Enzymes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide. Ongoing research to develop AD treatments has characterized multiple drug targets including the cholinergic system, amyloid-β peptide, phosphorylated tau, and neuroinflammation. These systems have the potential to interact to either drive or slow AD progression. Promising agents that simultaneously impact many of these drug targets are the AD experimental drug Posiphen and its enantiomer phenserine that, currently, are separately being evaluated in clinical trials. To define the cholinergic component of these agents, the anticholinesterase activities of a ligand dataset comprising Posiphen and primary metabolites ((+)-N1-norPosiphen, (+)-N8-norPosiphen, and (+)-N1,N8-bisnorPosiphen) were characterized and compared to those of the enantiomer phenserine. The “target” dataset involved the human cholinesterase enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Binding interactions between the ligands and targets were analyzed using Autodock 4.2. The computationally determined inhibitory action of these ligands was then compared to ex vivo laboratory-measured values versus human AChE and BChE. While Posiphen lacked AChE inhibitory action, its major and minor metabolites (+)-N1-norPosiphen and (+)-N1,N8-bisnorPosiphen, respectively, possessed modest AChE inhibitory activity, and Posiphen and all metabolites lacked BChE action. Phenserine, as a positive control, demonstrated AChE-selective inhibitory action. In light of AChE inhibitory action deriving from a major and minor Posiphen metabolite, current Posiphen clinical trials in AD and related disorders should additionally evaluate AChE inhibition; particularly if Posiphen should be combined with a known anticholinesterase, since this drug class is clinically approved and the standard of care for AD subjects, and excessive AChE inhibition may impact drug tolerability.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".