Bibliographic record
Abstract
Abstract There is a large amount of circumstantial evidence that supports the hypothesis that most idiosyncratic drug reactions (IDRs) are caused by reactive metabolites rather than the parent drug. When the total daily dose is included in the calculation, there is a rough correlation between the amount of covalent binding and the risk that a drug will be associated with a significant incidence of IDRs, especially those affecting the liver. However, there are drugs that form a large amount of reactive metabolites and rarely cause IDRs, and there are drugs such as ximelagatran that do not appear to form reactive metabolites and yet had to be withdrawn from the market because of an unacceptable risk of IDRs. There are a wide variety of proteins that are targets of covalent binding, and clearly, not all covalent binding is associated with the same risk of causing an IDR. It is likely that most IDRs are immune mediated, although this is not without controversy, especially with respect to idiosyncratic liver toxicity. There are several hypotheses that address the issue of how drugs initiate an immune response, but in the absence of valid animal models, they are very difficult to rigorously test. These hypotheses include the hapten hypothesis, the danger hypothesis, the p‐I (pharmacological interaction) hypothesis, epigenetic effects leading to activation of the immune system, direct activation of antigen‐presenting cells, and some of the newer biological agents simply appear to alter the balance in the immune system and do not involve reactive metabolites. It is likely that the mechanism by which different drugs induce an immune response is different, at least in detail. There are also hypotheses that do not involve the immune system such as mitochondrial toxicity and the inflammagen hypothesis. In the absence of valid animal models, it is important to use the clinical characteristics of IDRs to evaluate the hypotheses. A very important characteristic is the usual delay between starting a drug and the onset of the IDR. Another important characteristic is that different drugs can cause different types of IDRs in different people and even the same drug can cause different types of IDRs in different people. This is most easily explained by an immune mechanism in which this variability is due to variability in T‐cell‐receptor specificity, which is different even in identical twins. However, until we have a much better understanding of the detailed mechanisms of IDRs, it will be difficult to predict which drug candidates are likely to cause a relatively high incidence of IDRs and which patients are at high risk of such adverse reactions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".