Induction Effects of Ritonavir: Implications for Drug Interactions
Bibliographic record
Abstract
OBJECTIVE: To review the literature on the induction effects of ritonavir on the cytochrome P450 enzyme system and glucuronyl transferase and identify resultant established and potential drug interactions. DATA SOURCES: Primary literature was identified from MEDLINE (1950-April 2008), EMBASE (1988-April 2008) and International Pharmaceutical Abstracts (1970-April 2008) using the search terms ritonavir, cytochrome P450 enzyme system, enzyme induction, glucuronyl transferase, and drug interactions. Additionally, relevant conference abstracts and references of relevant articles were reviewed. STUDY SELECTION AND DATA ABSTRACTION: All English-language articles and abstracts identified were reviewed. DATA SYNTHESIS: Ritonavir is a well-known inhibitor of the metabolism of numerous medications that are substrates of the CYP3A and CYP2D6 pathways. It also exhibits a biphasic, time-dependent effect on P-glycoprotein of inhibition followed by induction. Numerous pharmacokinetic studies suggested that ritonavir induces cytochrome P450 enzymes 3A, 1A2, 2B6, 2C9, and 2C19, as well as glucuronyl transferase. Additionally, several case reports described clinically significant subtherapeutic effects of drugs metabolized by these isoenzymes when coadministered with ritonavir. Both therapeutic and boosting doses of ritonavir appear to induce these enzymes; however, most of the studies of low-dose ritonavir involved a second protease inhibitor such as lopinavir, darunavir, or tipranavir. It is, therefore, difficult to distinguish the relative effects of additional medications unless well-designed, 3-way studies are conducted. CONCLUSIONS: At both therapeutic and boosting doses, ritonavir exhibits a clinically relevant induction effect on numerous drug-metabolizing enzymes. A decrease or loss of therapeutic effect may be observed when ritonavir is coadministered with medications that are substrates for these enzymes. It is important for clinicians to be aware of drugs potentially impacted by ritonavir therapy to identify and manage these interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".