Bibliographic record
Abstract
Drug hypersensitivity reactions (DHRs) are type B adverse drug reactions (ADRs) traditionally defined as unpredictable, dose independent and not related to the drug pharmacology. DHRs, also called drug allergy if the immune system involvement is confirmed, represent around one-sixth of all ADRs and can cause major clinical problems due to their vague clinical presentation and irregular time course. Understanding the underlying pathophysiology of DHRs is very important for their diagnosis and management. Multiple layers of evidence exist pointing to the involvement of the immune system in DHRs. Recent data have led to a paradigm shift in our understanding of the exact pathophysiology of these reactions. Numerous hypotheses proposing explanation on how a low molecular weight drug molecule can elicit an immune reaction have been proposed. In addition to the classical "hapten" hypothesis, the reactive metabolite hypothesis, the pharmacological interaction with the immune system (p-i) concept, the danger/injury hypothesis and the altered peptide repertoire hypothesis have been proposed. We here introduce the inflammasome activation hypothesis and the cross-reactivity hypothesis as additional models explaining the pathophysiology of DHRs. Available data supporting these hypotheses are briefly summarized and discussed. We also introduced the cross-reactivity model, which may provide a platform to appreciate the potential role played by other factors leading to the activation of the immune system. We believe that although the drug in question could be the trigger of the reaction, the components of the immune system mediating the reaction do not act in isolation but rather are affected by the proinflammatory milieu occurring at the time of the reaction. This review attempts to summarize the available evidence to further illustrate the pathophysiology of DHRs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".