Reversible, Time-Dependent Inhibition of CYP3A-Mediated Metabolism of Midazolam and Tacrolimus by Telaprevir in Human Liver Microsomes
Bibliographic record
Abstract
PURPOSE: Telaprevir inhibits CYP3A resulting in drug-drug interactions (DDI) of unprecedented magnitude. We investigated the mechanisms by which telaprevir inhibits the oxidation of midazolam and tacrolimus in human liver microsomes (HLM). METHODS: We performed a static mechanistic DDI prediction to evaluate whether previously reported competitive inhibition of CYP3A by telaprevir and its diastereomeric metabolite - VRT-127394 is sufficient to explain the remarkable reduction in oral clearance observed with oral midazolam and tacrolimus. To further explore the inhibitory mechanisms of telaprevir, we assessed whether telaprevir-mediated inhibition of the oxidation of midazolam and tacrolimus is time-dependent in human liver microsomes, and whether any observed time-dependency was irreversible or reversible in nature. RESULTS: The competitive inhibition model failed to account for the magnitude of telaprevir interactions in human subjects. In comparing HLM incubations with and without a prior 30-min exposure to telaprevir, a respective 4- and 11-fold reduction in IC50 was observed with midazolam and tacrolimus as substrates. This time-dependent inhibition was shown to be NADPH-dependent. Upon dilution of microsomes following pre-incubation with telaprevir, time-dependent inhibition of midazolam metabolism was completely reversed, whereas partial reversal occurred with tacrolimus. CONCLUSIONS: The interaction between telaprevir and midazolam or tacrolimus involves both competitive and time-dependent inhibition. The time-dependent component is not explained by irreversible inactivation of CYP3A. Formation of potent inhibitory metabolites may contribute to the remarkable in vivo inhibitory potency of telaprevir.
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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) | 0.000 | 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) | 0.002 | 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".