Assessment of CYP3A‐mediated drug–drug interaction potential for victim drugs using an <i>in vivo</i> rat model
Bibliographic record
Abstract
The present study aims to determine if an in vivo rat model of drug-drug interaction (DDI) could be useful to discriminate a sensitive (buspirone) from a 'non-sensitive' (verapamil) CYP3A substrate, using ketoconazole and ritonavir as perpetrator drugs. Prior to in vivo studies, ketoconazole and ritonavir were shown to inhibit midazolam hydroxylation with IC50 values of 350 ± 60 nm and 11 ± 3 nm, respectively, in rat liver microsomes (RLM). Buspirone and verapamil were also shown to be substrates of recombinant rat CYP3A1/3A2. In the rat model, the mean plasma AUC0-inf of buspirone (10 mg/kg, p.o.) was increased by 7.4-fold and 12.8-fold after co-administration with ketoconazole and ritonavir (20 mg/kg, p.o.), respectively. The mean plasma AUC0-inf of verapamil (10 mg/kg, p.o.) was increased by 3.0-fold and 4.8-fold after co-administration with ketoconazole and ritonavir (20 mg/kg, p.o.), respectively. Thus, the rat DDI model correctly identified buspirone as a sensitive CYP3A substrate (>5-fold AUC change) in contrast to verapamil. In addition, for both victim drugs, the extent of DDI when co-administered was greater with ritonavir compared with ketoconazole, in line with their in vitro CYP3A inhibition potency in RLM. In conclusion, our study extended the rat DDI model applicability to two additional victim/perpetrator pairs. In addition, we suggest that use of this model would increase our confidence in estimation of the DDI potential for victim drugs in early discovery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".