Synergistic cytotoxicity of fludarabine, clofarabine, busulfan, vorinostat and olaparib in AML cells
Bibliographic record
Abstract
Combinations of nucleoside analog(s) and DNA alkylating agent(s) are used for cancer treatment as components of pre-transplant regimens used in hematopoietic stem cell transplantation. Their efficacies are enhanced by combining drugs with different mechanisms of action, which also allows a reduction in the individual drug dosages and thus potentially in toxicity to the patient. We hypothesized that addition of SAHA and olaparib, an HDAC- and a PARP-inhibitor, respectively, to the established combination of fludarabine, clofarabine and busulfan would enhance AML cell cytotoxicity. Exposure of the AML cell lines KBM3/Bu2506, MV4-11, MOLM14 and OCI-AML3 to the 5-drug combination resulted in synergistic cytotoxicity with combination indexes < 1. Increased protein acetylation and decreased poly(ADP-ribosyl)ation were observed, as expected. Activation of apoptosis was suggested by cleavage of Caspase 3 and PARP1, DNA fragmentation, increased reactive oxygen species, and decreased mitochondrial membrane potential. The reduction in poly(ADP-ribosyl)ation was independent of caspase activation. Several proteins involved in DNA damage response and repair were downregulated, which may be contributing factors for the observed synergism. The increased phosphorylation of DNAPKcs suggests inhibition of its kinase activity and diminution of its role in DNA repair. A similar synergism was observed in patient-derived cell samples. These findings will be important in designing clinical trials using these drug combinations as pre-transplant conditioning regimens for AML patients.
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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) | 0.001 | 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".