Interaction between riboflavin and BCRP‐mediated drug transport in the mammary gland (844.2)
Bibliographic record
Abstract
Breastfeeding provides numerous benefits to a child’s growth and development. However, in a lactating mother, expression of Breast Cancer Resistance Protein (BCRP) is significantly increased in the mammary gland. BCRP is an efflux transporter and is responsible for the transport of many different substrates from natural carcinogens (ex. PhIP found in charred meats) to chemotherapeutic drugs. As a result, this poses a risk of exposure to potentially harmful BCRP substrates from a mother’s milk to her child. We hypothesized that riboflavin, a vitamin substrate for BCRP, can act as a potential competitor for BCRP‐mediated excretion of drugs/toxins into milk. We used intravenous topotecan and cimetidine at a dose of 1 and 5 mg/kg as model BCRP substrates in lactating mice. In mice pretreated with 5 mg/kg riboflavin, excretion of topotecan and cimetidine into milk at 30 min postdose was significantly reduced. Mean topotecan levels in the milk were 883 ng/ml (SD: ±465; n=10) in the control and 682 ng/ml (SD: ±316; n=10; p<0.01) in the riboflavin treatment group. For cimetidine, the mean milk levels were 1941 ng/ml (SD: ±387; n=7) in the control, compared to 1555 ng/ml (SD: ±192; n=7; p<0.05) in the riboflavin‐pretreated mice. The milk‐to‐plasma ratios of topotecan and cimetidine in riboflavin‐pretreated mice also showed a significant 35% and 26% decrease respectively. In conclusion, our findings indicate that riboflavin administration does reduce the excretion of other BCRP substrates into a mother’s milk. Grant Funding Source : Supported by CIHR
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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 | 0.000 |
| 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".