Pharmacokinetic and pharmacodynamic analysis of 5-aza-2’-deoxycytidine (decitabine) in the design of its dose-schedule for cancer therapy
Bibliographic record
Abstract
5-Aza-2'-deoxycytidine (5-AZA-CdR, decitabine), an epigenetic drug that inhibits DNA methylation, is currently used to treat myelodysplastic syndrome (MDS), and is under investigation for treating acute myeloid leukemia (AML) and other malignancies. 5-AZA-CdR can reactivate tumor suppressor genes silenced by aberrant DNA methylation, a frequent event in all types of cancer. Because this epigenetic change is reversible, it is a good target for 5-AZA-CdR therapy. We have reviewed the preclinical data of 5-AZA-CdR to analyze the concentrations and exposure times required to eradicate cancer stem cells. We analyzed the dose-schedules used in animal models that show potent antineoplastic activity of 5-AZA-CdR. We attempted to correlate the preclinical data with the responses obtained in clinical trials of 5-AZA-CdR in patients with cancer. The pharmacokinetics and drug distribution of 5-AZA-CdR are key parameters because adequate therapeutic drug levels are required to eliminate cancer stem cells in all anatomic compartments. The plasma half-life of 5-AZA-CdR in humans is approximately 20 minutes due to the high levels in the liver of cytidine deaminase, the enzyme that inactivates this analogue. This provides a rationale to use an inhibitor of cytidine deaminase in combination with 5-AZA-CdR. Low-dose 5-AZA-CdR is effective for MDS and AML and can induce complete remissions (CR). However, maintenance of CR with low-dose 5-AZA-CdR is difficult. Based on analyses of preclinical and clinical data, low dose 5-AZA-CdR has the potential to be an effective form of therapy in some patients with cancer. For patients who do not respond to low dose therapy we recommend dose-intensive treatment with 5-AZA-CdR. Patients who are candidates for intensive dose 5-AZA-CdR should have a good bone marrow status so as to permit adequate recovery from myelosuppression, the major toxicity of 5-AZA-CdR. Solid tumors are also interesting targets for therapy with 5-AZA-CdR. Both low dose and intensive therapy with 5-AZA-CdR can reduce the proliferative potential of tumor stem cells in animal models. We propose novel dose schedules of 5-AZA-CdR for investigation in patients with cancer. The full chemotherapeutic potential of 5-AZA-CdR to treat cancer merits further clinical investigation and can only be realized when its optimal dose-schedule is determined.
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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) | 0.000 | 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 | 0.001 |
| 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".