The use of PD-1 functional knockout rats to study idiosyncratic adverse reactions to nevirapine
Bibliographic record
Abstract
Idiosyncratic drug reactions (IDRs) are associated with significant patient morbidity/mortality and lead to considerable drug candidate attrition in drug development. Their idiosyncratic nature makes the study of IDRs difficult. In particular, nevirapine is associated with a relatively high risk of serious skin rash and liver injury. We previously found that nevirapine causes a similar skin rash in female Brown Norway rats, but these animals do not develop significant liver injury. Programmed cell death protein-1 (PD-1) is an immune checkpoint involved in immune tolerance, and anti-PD-1 antibodies have been used to treat cancer. However, they increase the risk of liver injury caused by co-administered drugs. We found that PD-1-/- mice are more susceptible to drug-induced liver injury, but PD-1-/- mice are not a good model for all drugs. In particular, they do not develop a skin rash when treated with nevirapine, at least in part because they lack the sulfotransferase in their skin that forms the reactive metabolite responsible for the rash. Therefore, we developed a PD-1 mutant (PD-1m/m) rat, with an excision in the ligand-binding domain of PD-1, to test whether nevirapine would cause a more serious skin rash in these animals. The PD-1m/m rat was based on a Sprague Dawley background, which has a lower incidence of skin rash than Brown Norway rats. The treated PD-1m/m rats developed more severe liver injury than PD-1-/- mice, but in contrast to expectations, they did not develop a skin rash. Functional knockouts provide a unique tool to study the mechanisms of IDRs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".