Review of Clinically Relevant Drug Interactions with Next Generation Hepatitis C Direct-acting Antiviral Agents
Bibliographic record
Abstract
In this review, we examine the pharmacokinetics and clinically relevant drug interactions of the newer generation direct-acting antivirals (DAAs) for the treatment of chronic hepatitis C, specifically sofosbuvir/velpatasvir (Epclusa®), sofosbuvir/velpatasvir/voxilaprevir (Vosevi®), glecaprevir/pibrentasvir (Maviret®), and elbasvir/grazoprevir (Zepatier®). We searched MEDLINE (1948-January 2020), Embase (1964-January 2020), Google, and GoogleScholar using the terms pharmacokinetics, drug interaction, drug metabolism, sofosbuvir, velpatasvir, Epclusa, voxilaprevir, Vosevi, glecaprevir, pibrentasvir, Maviret, elbasvir, grazoprevir, and Zepatier, from inception to January 13, 2020. The search was limited to randomized controlled trials, in vitro studies, prospective and retrospective human studies, drug monographs, abstracts, and conference proceedings. All relevant published literature on pharmacokinetic and pharmacodynamic interactions involving DAAs were reviewed and the data extracted. Numerous clinically relevant drug-drug interactions (DDIs) were identified with the newer generation DAAs and commonly prescribed drugs. NS3/4A protease inhibitors are more likely to be involved in DDIs, followed by NS5A inhibitors and NS5B polymerase inhibitor. The majority of clinically relevant DDIs are predictable, according to known pharmacokinetic, pharmacodynamics, and physicochemical properties of DAAs; however, in select cases, unpredictable DDIs do occur. As expected, many drug interactions exist between newer generation DAAs and commonly prescribed medications. While the majority of clinically relevant interactions are predictable, many require therapeutic dose adjustment or careful selection of non-interacting drugs. In select cases, severe and unpredictable drug interactions can occur. Clinicians should consult hepatitis C virus pharmacotherapy experts and tertiary drug interaction resources when initiating DAA therapy in patients taking other medications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.002 |
| 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".