Jadomycins: A potential chemotherapy for multi‐drug resistant metastatic breast cancer
Bibliographic record
Abstract
Breast cancer causes the most cancer fatalities in women worldwide. Approximately one-third of breast cancers metastasize, or spread from primary tumors to other tissues, and have a 70% 5-year mortality rate. Current breast cancer treatments like doxorubicin and paclitaxel become ineffective when breast cancer cells develop multi-drug resistance and overexpress ATP-binding cassette transporters, as the transporters cause a substantial efflux of the chemotherapies. Jadomycins, a group of molecules isolated from Streptomyces venezuelae ISP5230, are shown to be cytotoxic against a variety of cancers, especially breast cancer. Furthermore, jadomycins retain their cytotoxic properties in multi-drug resistant breast cancer cells, as they are not expelled through ATP-binding cassette transporters. Here, we describe the research that supports the potential use of jadomycins as a novel chemotherapy in the treatment of multi-drug resistant, metastatic breast cancer. We present the supportive findings, as well as the mechanisms of action investigated thus far. These include copper-mediated reactive oxygen species generation, aurora B kinase inhibition, and topoisomerase IIα and IIβ inhibition. We also suggest future directions of jadomycin research, which will help to determine if jadomycins can be used as a breast cancer chemotherapy in clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".