LC-based targeted metabolomics analysis of nucleotides and identification of biomarkers associated with chemotherapeutic drugs in cultured cell models
Bibliographic record
Abstract
Treatment of mammalian cells with chemotherapeutic drugs can result in perturbations of nucleotide pools. Monitoring these perturbations in cultured tumor cells from human sources is useful for assessment of the effect of drug therapy and a better understanding of the mechanism of action of these drugs. In this study, three classes of chemotherapeutic drugs with different mechanisms of action were used in the development of drug-treated cell models. The LC-based targeted metabolomics analysis of nucleotides in cells of the control group and the drug-treated group was carried out. Several data processing methods were combined for the identification of potential biomarkers associated with the action of drugs, including one-way analysis of variance, principal component analysis, and receiver operating characteristic curves. Intriguingly, tumor cells of both the control group and the drug-treated groups can be distinguished from each other, and several variables were recognized as potential biomarkers, such as ATP, GMP, and UDP for antimetabolite agents, ATP, GMP, and CTP for DNA-damaging agents, as well as GMP, ATP, UDP, and GDP for the mitotic spindle agents. Further validation of the potential biomarkers was performed using the receiver operating characteristic curve. Considering their corresponding area under the curve, which was larger than 0.9, it can be concluded that GMP and ATP are the best potential biomarkers for DNA-damaging drugs, as well as GMP, ATP, and UDP for the other two classes of drugs. This limited nucleotide approach cannot completely distinguish the mechanisms of the nine drugs, but it provides preliminary evidence for the role of pharmacometabolomics in the preclinical development of drugs at least.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".