Interactions of doxycycline with chemotherapeutic agents in human breast adenocarcinoma MDA-MB-231 cells
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Bibliographic record
Abstract
Commonly used chemotherapeutic agents for breast cancer treatment include cisplatin, doxorubicin, and paclitaxel. Unfortunately, these effective antiproliferative agents are limited by their toxicities. Previously, we have shown that doxycycline can substantially reduce tumor burden in an animal model of breast cancer bone metastasis. The purpose of this study was to examine the effect of doxycycline in combination with chemotherapy. Human breast adenocarcinoma MDA-MB-231 cells were treated in vitro with each drug individually and in combination with doxycycline. Cell survival was determined using the clonogenic survival assay. Doxycycline in combination with doxorubicin or paclitaxel yielded therapeutic antagonism at all effect levels. Combinatory treatment with cisplatin, however, yielded a biphasic interaction, at low combinatorial doses, the effect was quantified as nearly additive, whereas higher doxycycline-cisplatin doses yielding greater than 50% cell inhibition resulted in synergistic effects. Cell cycle profiles were determined and showed that treatment with doxycycline, cisplatin, and doxorubicin resulted in G1-phase, S-phase, and G2/M-phase arrests, respectively. Upon addition of doxycycline to doxorubicin, the G2/M-arrest characteristic of doxorubicin-only treatment was abrogated, which may account for the observed antagonism. Cells treated with doxycycline and cisplatin showed a further increase in S-phase arrest, also observed with cisplatin alone, which may be responsible for the additive and synergistic effects on cell survival. We clearly show that doxycycline in combination with paclitaxel or doxorubicin treatment resulted in antagonism; however, combining doxycycline with cisplatin led to synergistic interactions at higher effect levels. The increased potency of cisplatin may warrant dose reduction and thus decrease toxicity in vivo.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it