Adverse Events and Drug Interactions Associated with Elexacaftor/Tezacaftor/Ivacaftor Treatment: A Descriptive Study Across Australian, Canadian, and American Adverse Event Databases
Bibliographic record
Abstract
People with cystic fibrosis may experience polypharmacy, which can increase the risk of drug induced complications such as adverse events and drug-drug interactions. This study aimed to examine the prevalence of adverse events and to identify potential drug-drug interactions associated with elexacaftor/tezacaftor/ivacaftor (ETI). Three databases, the Australian Therapeutic Goods Administration Database of Adverse Event Notification (TGA DAEN), the Canada Vigilance Adverse Reaction Online Database (CVAROD), and the USA Food and Drug Administration Adverse Event Reporting System (FAERS) Database were searched for spontaneous ETI adverse events between 2019 and 2024. Descriptive analysis of the data was undertaken. The FAERS database was analysed to identify adverse events of interest such as anxiety and depression and concomitant drugs prescribed with ETI. A total of 10,628 ETI associated adverse events were identified in all system organ classes. The incidence of psychiatric adverse events ranged from 7 to 15% across the three databases. Potential drug-drug interactions with CYP 3A4/5 strong inhibitors and strong inducers were identified from the FAERS database and azole antifungals were implicated in several ETI dose modifications. The prevalence and types of ETI adverse events were varied and use of concomitant drugs with potential drug interactions was significant, requiring more research to manage them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".