Bile Acids Increase Doxorubicin Sensitivity in ABCC1-expressing Tumour Cells
Bibliographic record
Abstract
Tumour cells possess or acquire various mechanisms to circumvent the cytotoxic effects of chemotherapy drugs. One such mechanism involves the overexpression of ABC transporters that facilitate the extrusion of a variety of structurally distinct chemotherapy drugs from the cytoplasm into the extracellular space. While specific ABC transporter inhibitors have been developed, many affect other ABC transporters, particularly at elevated concentrations. It is also unclear whether they show clear efficacy for combatting drug resistance in cancer patients with minimal host toxicity. In this study, we demonstrate the ability of two bile acids [β-cholanic acid (urso-cholanic acid) and deoxycholic acid] to specifically inhibit ABCC1-mediated drug transport, augmenting doxorubicin accumulation in breast and lung tumour cells selected for doxorubicin resistance through overexpression of the ABCC1 (but not ABCB1) drug transporter. The bile acids could also restore uptake and sensitivity to doxorubicin in human endothelial kidney cells genetically engineered to overexpress the ABCC1 drug transporter. These observations suggest a previously unreported role for bile acids as ABCC1 inhibitors or regulators. Given its additional properties of minimal clinical toxicity in humans and its ability to inhibit aldo-keto reductases involved in anthracycline resistance and anthracycline-induced cardiotoxicity, β-cholanic acid merits further in vivo and clinical investigation.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".