Drug Interactions Between Antiplatelet or Novel Oral Anticoagulant Medications and Antiretroviral Medications
Bibliographic record
Abstract
OBJECTIVE: To review potential drug interactions between antiretroviral (ARV) medications and antiplatelets or novel oral anticoagulants (NOACs). DATA SOURCES: A literature search of MEDLINE, PubMed, EMBASE, International Pharmaceutical Abstracts, and Google Scholar was performed using the search terms (1) clopidogrel or ticagrelor or prasugrel, (2) dabigatran or rivaroxaban or apixaban, and (3) antiretrovirals. STUDY SELECTION AND DATA EXTRACTION: Any English language study or case report describing a drug interaction between an ARV and an antiplatelet or NOAC was included. Additional information was taken from pharmacokinetic studies of individual agents alone or information from similar drug interactions. RESULTS: Two studies were identified through the literature search: one reporting an in vivo interaction between ritonavir and prasugrel and the other an in vitro interaction between efavirenz and clopidogrel. A case report describing a drug interaction between nevirapine and rivaroxaban was also located. Information from pharmacokinetic studies and from similar drug interactions allowed for a comprehensive review of potential drug interactions. CONCLUSIONS: There are potential drug interactions between ARVs, antiplatelet agents or NOACs. Management of these interactions may include selecting ARVs with a lower potential for drug interactions or choosing antiplatelet agents or NOACs least likely to interact with ARVs. With protease inhibitors or cobicistat, clopidogrel and dabigatran do not appear to have clinically significant interactions. Nonnucleoside reverse transcriptase inhibitors have a low potential for interactions with prasugrel and dabigatran. Clinically significant drug interactions are unlikely to occur between antiplatelet agents or NOACs and nucleoside reverse transcriptase inhibitors raltegravir, dolutegravir, or maraviroc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".