The danger hypothesis applied to idiosyncratic drug reactions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Idiosyncratic drug reactions pose a significant clinical threat and hamper drug development. The idiosyncratic nature of these reactions has made mechanistic studies exceedingly difficult, and yet without a better understanding of the mechanisms involved it is unlikely that much progress can be made in dealing with the problem. Several working hypotheses have been used to study these reactions, but none fits all of the characteristics that are observed. Borrowed from immunology, the danger hypothesis has most recently been used to explain several characteristics of these reactions. The present review describes the danger hypothesis and compares it with previous hypotheses to determine how well each fits with the observed characteristics of the reactions. RECENT FINDINGS: Slow progress in the field continues and it is important to use new observations, such as identifying T cells that recognize drugs in the absence of reactive metabolite formation, to test and refine the working hypotheses. However, the development of animal models of idiosyncratic drug reactions as well as progress in basic immunology and genomics are likely to accelerate progress in this area in the near future. SUMMARY: No one model fits the characteristics of all idiosyncratic drug reactions; however, the danger model provides a new perspective and suggests avenues of research that have the potential to increase our ability to predict and prevent such reactions significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".