International Collaboration to Develop and Harmonize Drug Interaction Guidance for Nirmatrelvir/Ritonavir During COVID-19: Lessons Learned for Future Pandemic Preparedness
Bibliographic record
Abstract
In December 2021 and January 2022, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral drug, nirmatrelvir/ritonavir, was authorized in the United States, Canada, and Europe. However, ritonavir has significant drug-drug interaction (DDI) potential, and information resources were incomplete or provided conflicting advice for certain DDIs. Within the challenging pandemic setting, nirmatrelvir/ritonavir was being prescribed largely by clinicians unfamiliar with ritonavir. Coronavirus disease 2019 (COVID-19) affects people with comorbid conditions and polypharmacy who could benefit from therapy, but prescriber uncertainty surrounding the appropriate management of DDIs was a barrier to the use of nirmatrelvir/ritonavir. To support clinicians, the National Institutes of Health (NIH) panel on COVID-19 guidelines, the Ontario COVID-19 Science Advisory Table, and the University of Liverpool independently developed prescribing guidelines. Ultimately, the groups united to establish the LiON-PK (Liverpool-Ontario-NIH Pharmacokinetics) collaboration. Here we describe how the team developed pragmatic and harmonized guidance for managing DDIs with nirmatrelvir/ritonavir. This framework may inform development of prescribing resources for other complex medications or for future pandemic preparedness.
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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 | 0.292 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".