Establishment of tandem mass spectrometric fingerprint of novel antineoplastic curcumin analogues using electrospray ionization
Bibliographic record
Abstract
RATIONALE: Curcumin analogues are antineoplastic agents, designed based on the structure of the spice turmeric with structural modifications aiming at enhancing potency. The goal is to identify the common tandem mass spectrometric (MS/MS) behavior of 13 novel curcumin analogues. Such knowledge is critical for their biological assessment, including metabolite identification and pharmacokinetic evaluation. METHODS: Both detection of the protonated molecules [M + H](+) of the synthesized compounds and determination of their exact molecular masses were achieved with hybrid quadrupole orthogonal time-of-flight mass spectrometry (QqTOF-MS). Low-energy collision-induced dissociation (CID)-MS/MS analysis was performed using triple quadrupole linear ion trap mass spectrometry (QqLIT-MS). Both instruments were equipped with an electrospray ionization (ESI) source. MS(3) and neutral loss experiments were performed using QqLIT-MS to confirm the genesis of the observed product ions. RESULTS: Abundant [M + H](+) molecules were formed using the QqTOF-MS hybrid instrument with mass accuracies below 6 ppm. CID-MS/MS dissociation studies were centered on the piperidone ring of curcumin analogues; twelve common product ions have been identified from the fission of the various bonds within the piperidone moiety. There was a tendency for the formation of highly conjugated product ions, stabilized via resonance. The variety of the side-chain substituents at the nitrogen atom resulted in side-chain-specific product ions. CONCLUSIONS: The ESI-CID-MS/MS analysis of curcumin analogues revealed a common fragmentation behavior of all tested compounds, which gave diagnostic product ions identified for each molecule. The established MS/MS behavior will be applied to determine metabolic by-products of curcumin analogues as well as to develop targeted identification/quantification methods within biological extracts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".