Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice
Bibliographic record
Abstract
BACKGROUND: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor. METHODS: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin. RESULTS: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4- or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9- or 3.0-fold increase in exposure in older subjects. CONCLUSIONS: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".