Combining semi-synthesis with plant and microbial biocatalysis: new frontiers in producing a chemical arsenal against cancer
Bibliographic record
Abstract
Natural products (NPs) that exhibit anticancer activities are frequently not potent enough to be used clinically as therapeutics. Semi-synthesis and metabolic engineering are promising approaches for producing more efficacious derivatives of anticancer NPs (ACNPs), but each technique alone can be inefficient at obtaining specific ACNP derivatives that may be suspected to have enhanced anticancer activity. Here, we demonstrate that the methods of semi-synthesis and biocatalysis can be used as modules in succession and in different combinations to produce 6,8-dibromogenkwanin, a derivative of the ACNP apigenin. Further, we demonstrated that soybean seed coats can be used as a biocatalyst to convert brominated flavonoids into multiple derivatives. A strength of the combinatorial (bio)synthesis approach was that the order of the modules could be rearranged to increase the yield of the desired product. At lower treatment concentration (5 μM), 6,8-dibromogenkwanin exhibited enhanced antiproliferative activities against HT-29 colorectal adenocarcinoma cancer cells under normoxic and hypoxic conditions compared to its ACNP precursors, but not at higher concentrations. Dose-response analyses suggested that dibromogenkwanin had a distinct mode-of-action compared to apigenin. Thus, this proof-of-concept paper demonstrates combinatorial (bio)synthesis as an approach that can be used to produce novel chemistries for anticancer research.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".