Probing simple structural modification of α‐mangostin on its cholinesterase inhibition and cytotoxicity
Bibliographic record
Abstract
Abstract α‐Mangostin has been reported to possess a broad range of pharmacological effects including potent cholinesterase inhibition, but the development of α‐mangostin as a potential lead compound is impeded by its toxicity. The present study investigated the impact of simple structural modification of α‐mangostin on its cholinesterase inhibitory activities and toxicity toward neuroblastoma and liver cancer cells. The dialkylated derivatives retained good acetylcholinesterase (AChE) inhibitory activities with IC 50 values between 4.15 and 6.73 µM, but not butyrylcholinesterase (BChE) inhibitory activities, compared with α‐mangostin, a dual inhibitor (IC 50 : AChE, 2.48 µM; BChE, 5.87 µM). Dialkylation of α‐mangostin produced AChE selective inhibitors that formed hydrophobic interactions at the active site of AChE. Interestingly, all four dialkylated derivatives of α‐mangostin showed much lower cytotoxicity, being 6.4‐ to 9.0‐fold and 3.8‐ to 5.5‐fold less toxic than their parent compound on neuroblastoma and liver cancer cells, respectively. Likewise, their selectivity index was higher by 1.9‐ to 4.4‐fold; in particular, A2 and A4 showed improved selectivity index compared with α‐mangostin. Taken together, modification of the hydroxyl groups of α‐mangostin at positions C‐3 and C‐6 greatly influenced its BChE inhibitory and cytotoxic but not its AChE inhibitory activities. These dialkylated derivatives are viable candidates for further structural modification and refinement, worthy in the search of new AChE inhibitors with higher safety margins.
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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 | 0.000 |
| 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) | 0.000 | 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".