Deciphering the chemical instability of sphaeropsidin A under physiological conditions – degradation studies and structural elucidation of the major metabolite
Bibliographic record
Abstract
The fungal metabolite sphaeropsidin A (SphA) has been recognised for its promising cytotoxicity, particularly towards apoptosis- and multidrug-resistant cancers. Owing to its intriguing activity, the development of SphA as a potential anticancer agent has been pursued. However, this endeavour is compromised since SphA exhibits poor physicochemical stability under physiological conditions. Herein, SphA's instability in biological media was explored utilizing LC-MS. Notably, the degradation tendency was found to be markedly enhanced in the presence of amino acids in the cell medium utilized. Furthermore, the study investigated the presence of degradation adducts, including the identification, isolation and structural elucidation of a major degradation metabolite, (4R)-4,4',4'-trimethyl-3'-oxo-4-vinyl-4',5',6',7'-tetrahydro-3'H-spiro[cyclohexane-1,1'-isobenzofuran]-2-ene-2-carboxylic acid. Considering the reduced cytotoxic potency of aged SphA solutions, as well as that of the isolated degradation metabolite, the reported antiproliferative activity has been attributed primarily to the parent compound (SphA) and not its degradation species. The fact that SphA continues to exhibit remarkable bioactivity, despite being susceptible to degradation, motivates future research efforts to address the challenges associated with this instability impediment.
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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 | 0.000 |
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".