Towards Application of Bioactive Natural Products Containing Isoprenoids for the Regulation of HMG-CoA Reductase—A Review
Bibliographic record
Abstract
Recognition of the biological properties of numerous “natural products” has fueled the current focus of this field, namely, the search for new drugs, antibiotics, insecticides, and herbicides. Based on their biosynthetic origins, natural products can be divided into three major groups: the isoprenoids, alkaloids, and phenolic compounds. Isoprenoids are structurally the most diverse group of secondary natural metabolites with different roles in the growth, development, and reproduction of a diverse range of prokaryotic and eukaryotes cells. Mevalonate and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways are known to be responsible for biosynthesis of numerous isoprenoids. HMG-CoA reductase is a rate-determining enzyme in mevalonate pathway, producing intermediates such as farnesyl and geranylgeranyl pyrophosphates, which lead to by-products such as cholesterol. Earlier studies have demonstrated that the inhibition of HMG-CoA reductase is one of the most effective approaches for treating hypercholesterolemia and eventually cardiovascular disease (CVD). Statins are HMG-CoA reductase inhibitors and the most prescribed group of drugs worldwide in treating hypercholesterolemia; however the application of this group of drugs may be expensive and has side effects including rashes and gastrointestinal symptoms. For these reasons, there is an important need to examine the viability of natural products as an alternative to statin treatment. This article is a review of different aforementioned areas with a focus on isoprenoids that can be used for the regulation of HMG-CoA reductase.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".