Chemical Synthesis and Biological Activities of Amaryllidaceae Alkaloid Norbelladine Derivatives and Precursors
Bibliographic record
Abstract
Amaryllidaceae alkaloids (AAs) are a structurally diverse family of alkaloids recognized for their many therapeutic properties, such as antiviral, anti-cholinesterase, and anticancer properties. Norbelladine and its derivatives, whose biological properties are poorly studied, are key intermediates required for the biosynthesis of all ~650 reported AAs. To gain insight into their therapeutic potential, we synthesized a series of O-methylated norbelladine-type alkaloids and evaluated their cytotoxic effects on two types of cancer cell lines, their antiviral effects against the dengue virus (DENV) and the human immunodeficiency virus 1 (HIV-1), and their anti-Alzheimer’s disease (anti-cholinesterase and -prolyl oligopeptidase) properties. In monocytic leukemia cells, norcraugsodine was highly cytotoxic (CC50 = 27.0 μM), while norbelladine was the most cytotoxic to hepatocarcinoma cells (CC50 = 72.6 μM). HIV-1 infection was impaired only at cytotoxic concentrations of the compounds. The 3,4-dihydroxybenzaldehyde (selectivity index (SI) = 7.2), 3′,4′-O-dimethylnorbelladine (SI = 4.8), 4′-O-methylnorbelladine (SI > 4.9), 3′-O-methylnorbelladine (SI > 4.5), and norcraugsodine (SI = 3.2) reduced the number of DENV-infected cells with EC50 values ranging from 24.1 to 44.9 μM. The O-methylation of norcraugsodine abolished its anti-DENV potential. Norbelladine and its O-methylated forms also displayed butyrylcholinesterase-inhibition properties (IC50 values ranging from 26.1 to 91.6 μM). Altogether, the results provided hints of the structure−activity relationship of norbelladine-type alkaloids, which is important knowledge for the development of new inhibitors of DENV and butyrylcholinesterase.
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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 | 0.000 |
| 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) | 0.001 | 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".