In Vitro and Clinical Evaluation of Potential Interactions of Bemnifosbuvir with Drug Transporters
Bibliographic record
Abstract
Bemnifosbuvir is a novel oral guanosine nucleotide prodrug candidate for the treatment of chronic hepatitis C virus infection. Potential drug-drug interactions (DDIs) of bemnifosbuvir as a substrate or perpetrator with regard to ATP-binding cassette (ABC) and solute carrier (SLC) transporters were evaluated in vitro and in clinical studies. Bemnifosbuvir was demonstrated in vitro as a substrate and inhibitor of the ABC transporters' P-glycoprotein (P-gp), as an inhibitor of the breast cancer resistance protein (BCRP), as well as a weak inhibitor of SLC transporters, including organic anion transporting polypeptide 1B1 (OATP1B1). Phase 1 studies in healthy participants were subsequently conducted to assess the clinical significance of transporter-mediated DDI potentials of bemnifosbuvir as a precipitant using digoxin and rosuvastatin as P-gp and BCRP/OATP1B1 index substrates, respectively. A single dose of 0.25 mg digoxin or 10 mg rosuvastatin was administered alone and with 1100 mg bemnifosbuvir, either simultaneously or staggered. Simultaneous administration of a single dose of 1100 mg bemnifosbuvir increased total plasma exposure of both drugs by less than 20%, and transiently increased the peak plasma exposure of digoxin and rosuvastatin by 78% and 40%, respectively. Staggered dosing reduced the magnitude of changes in peak exposure to digoxin and rosuvastatin. No serious adverse events or drug discontinuations were observed. Dose adjustments are therefore unlikely for drugs that are substrates of P-gp or BCRP/OAT1B1 when coadministered with bemnifosbuvir, and staggered dosing may further reduce any DDI risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".