Cobicistat Versus Ritonavir: Similar Pharmacokinetic Enhancers But Some Important Differences
Bibliographic record
Abstract
OBJECTIVE: To describe properties of cobicistat and ritonavir; compare boosting data with atazanavir, darunavir, and elvitegravir; and summarize antiretroviral and comedication interaction studies, with a focus on similarities and differences between ritonavir and cobicistat. Considerations when switching from one booster to another are discussed. DATA SOURCES: A literature search of MEDLINE was performed (1985 to April 2017) using the following search terms: cobicistat, ritonavir, pharmacokinetic, drug interactions, booster, pharmacokinetic enhancer, HIV, antiretrovirals. Abstracts from conferences, article bibliographies, and product monographs were reviewed. STUDY SELECTION AND DATA EXTRACTION: Relevant English-language studies or those conducted in humans were considered. DATA SYNTHESIS: Similar exposures of elvitegravir, darunavir, and atazanavir are achieved when combined with cobicistat or ritonavir. Cobicistat may not be as potent a CYP3A4 inhibitor as ritonavir in the presence of a concomitant inducer. Ritonavir induces CYP1A2, 2B6, 2C9, 2C19, and uridine 5'-diphospho-glucuronosyltransferase, whereas cobicistat does not. Therefore, recommendations for cobicistat with comedications that are extrapolated from studies using ritonavir may not be valid. Pharmacokinetic properties of the boosted antiretroviral can also affect interaction outcome with comedications. Problems can arise when switching patients from ritonavir to cobicistat regimens, particularly with medications that have a narrow therapeutic index such as warfarin. CONCLUSIONS: When assessing and managing potential interactions with ritonavir- or cobicistat-based regimens, clinicians need to be aware of important differences and distinctions between these agents. This is especially important for patients with multiple comorbidities and concomitant medications. Additional monitoring or medication dose adjustments may be needed when switching from one booster to another.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".