Potential Therapeutic Applications of Plant-Derived Alkaloids against Inflammatory and Neurodegenerative Diseases
Bibliographic record
Abstract
Alkaloids are a type of natural compound possessing different pharmacological activities. Natural products, including alkaloids, which originate from plants, have emerged as potential protective agents against neurodegenerative disorders (NDDs) and chronic inflammations. A wide array of prescription drugs are used against these conditions, however, not free of limitations of potency, side effects, and intolerability. In the context of personalized medicine, further research on alkaloids to unravel novel therapeutic approaches in reducing complications is critical. In this review, a systematic survey was executed to collect the literature on alkaloids and their health complications, from which we found that majority of alkaloids exhibit anti-inflammatory action via nuclear factor-κB and cyclooxygenase-2 (COX-2), and neuroprotective interaction through acetylcholinesterase (AChE), COX, and β-site amyloid precursor protein activity. In silico ADMET and ProTox-II-related descriptors were calculated to predict the pharmacological properties of 280 alkaloids isolated from traditional medicinal plants towards drug development. Out of which, eight alkaloids such as tetrahydropalmatine, berberine, tetrandrine, aloperine, sinomenine, oxymatrine, harmine, and galantamine are found to be optimal within the categorical range when compared to nicotine. These alkaloids could be exploited as starting materials for novel drug synthesis or, to a lesser extent, manage inflammation and neurodegenerative-related complications.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".