From Bench to Bedside: Utilization of an In Vitro Model to Predict Potential Drug‐Drug Interactions in the Kidney: The Digoxin‐Mifepristone Example
Bibliographic record
Abstract
Drug interactions are a common source of drug-induced toxicity. For drugs with narrow therapeutic windows, such as digoxin, an understanding of the potential mechanisms by which drugs might interact is essential to clinical practice. This article describes the utility of a renal tubular cell culture model in the prediction of drug interactions involving P-glycoprotein. Digoxin is a cardiac glycoside that undergoes active secretion in the renal tubules by the MDR1 (P-glycoprotein) drug efflux pump. Mifepristone (RU486) is a recently introduced abortifacient that is largely unstudied in terms of drug-drug interactions. The authors used an in vitro model to study the effects of mifepristone on the renal tubular secretion and cellular uptake of digoxin by Madin-Darby canine kidney (MDCK) cells. Mifepristone significantly inhibited the renal tubular secretion of digoxin (p = 0.0005), without interfering with its ability to enter the renal tubular cell. Similar results were found with the P-glycoprotein substrate vinblastine. The findings suggest that drug interactions may result if mifepristone is administered with P-glycoprotein substrates, highlighting the usefulness of this model in the study of not only common but also rare combinations of drugs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".