Interactions Between Nutraceutical Supplements and Standard Acute Myeloid Leukemia Chemotherapeutics
Bibliographic record
Abstract
PURPOSE: Concomitant use of nutraceuticals with chemotherapy is very common. Cancer patients self-medicate to relieve the side effects associated with chemotherapy, improve disease outcome and to regain control of their medical care. However, there is limited empirical evidence on potential drug-nutraceutical interactions and their resulting effect on chemotherapy efficacy. METHOD: To investigate drug-nutraceutical interactions we created and screened a library of commonly used nutraceuticals for their modulatory effects on the activity of cytarabine and daunorubicin, two primary chemotherapeutics used to treat acute myeloid leukemia (AML). Combination screening was performed in 3 AML cell lines (OCI-AML2, KG1a and U937) using the MTS viability assay. Lead compounds were validated using with the Annexin V/ Propidium iodide assay and CalcuSyn drug combination software. RESULTS: We identified zinc as a nutraceutical that enhanced AML chemotherapy efficacy with combination index (CI) values of 0.649, 0.632 and 0.615 at EC 25, 50 and 75, respectively; CI values <0.9, >1.1 or between 0.9-1.1 denote statistical synergy, antagonism or additivity, respectively. In contrast, we show that echinacea hindered AML chemotherapy efficacy by significantly reducing the ability of cytarabine to induce cell death. CONCLUSION: Given the positive and negative effects of nutraceuticals, patients undergoing chemotherapy must consult with their oncologist before consuming over-the-counter supplements. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.001 | 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 | 0.000 |
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".