Evaluation of Sources of Drug Interaction Information for Nirmatrelvir/ritonavir
Bibliographic record
Abstract
The Japanese package insert (J-PI) for nirmatrelvir/ritonavir (N/r) (specially approved pharmaceutical) includes numerous warnings about drug interactions. However, discrepancies in the information on drug interaction are reported between J-PI and foreign databases. This study aimed to evaluate various information sources on N/r drug interactions. We categorized and compared information on N/r drug interactions from the J-PI, prescribing information from foreign regulatory agencies, guidance from the National Institutes of Health and University Health Network, the Ontario coronavirus disease 2019 (COVID-19) Science Advisory Table, University of Liverpool, Lexicomp, and the Japanese Society of Pharmaceutical Health Care and Sciences (JSPHCS). We assessed information quantity, missing data in J-PI, predicted change of the area under the blood concentration-time curve (AUC) for nirmatrelvir or co-administered drugs, and the information source consistency. From these information sources, we compiled a dataset with 115 contraindications and 203 precautions for N/r co-administration, and 51 contraindications are missing in J-PI. Among them, at least 12 drugs have large predicted AUC changes with N/r (AUC ≥5-fold or <1/5 of the baseline value). Nine of these 12 drugs are included as contraindications in Lexicomp and the JSPHCS. The consistency among the information sources is low. Information in the J-PI alone may be insufficient and Lexicomp or the JSPHCS guidelines should be useful because of their large amounts of information and wide coverage of drugs with large AUC changes. Due to low source consistency, multiple sources are needed for clinical management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".