The development and characterization of a CRISPR/Cas9-mediated PD-1 functional knockout rat as a tool to study idiosyncratic drug reactions
Bibliographic record
Abstract
Idiosyncratic drug reactions are rare but serious adverse drug reactions unrelated to the known therapeutic properties of the drug and manifest in only a small percentage of the treated population. Animal models play an important role in advancing mechanistic studies examining idiosyncratic drug reactions. However, to be useful, they must possess similarities to those seen clinically. Although mice currently represent the dominant mammalian genetic model, rats are advantageous in many areas of pharmacologic study where their physiology can be examined in greater detail and is more akin to that seen in humans. In the area of immunology, this includes autoimmune responses and susceptibility to diabetes, in which rats more accurately mimic disease states in humans compared with mice. For example, oral nevirapine treatment can induce an immune-mediated skin rash in humans and rats, but not in mice due to the absence of the sulfotransferase required to form reactive metabolites of nevirapine within the skin. Using CRISPR-mediated gene editing, we developed a modified line of transgenic rats in which a segment of IgG-like ectodomain containing the core PD-1 interaction motif containing the native ligand and therapeutic antibody domain in exon 2 was deleted. Removal of this region critical for mediating PD-1/PD-L1 interactions resulted in animals with an increased immune response resulting in liver injury when treated with amodiaquine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".