Is it Possible to More Accurately Predict which Drug Candidates will cause Idiosyncratic Drug Reactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The unexpected occurrence of idiosyncratic drug reactions during late clinical trials or after a drug has been released can lead to a severe restriction in its use or failure to release/withdrawal. This leads to considerable uncertainty in drug development and has led to attempts to try to predict a drug's potential to cause such reactions. It appears that most idiosyncratic drug reactions are due to reactive metabolites; however, many drugs that form reactive metabolites are associated with a very low incidence of idiosyncratic drug reactions. Therefore. screening drug for their ability to generate reactive metabolites is likely to cause the rejection of many good drug candidates. There is evidence to suggest that an idiosyncratic drug reaction is more likely if there is some "danger signal'. Thus drugs that cause some degree of cell stress or damage may be more likely to lead to a high incidence of idiosyncratic drug reactions. The exact nature of the putative danger signals is unknown. However, a screen of the effects of drugs known to be associated with a high incidence of idiosyncatic reactions using expression genomics and proteomics may reveal a pattern or patterns of mRNA and protein expression that predict which drugs will cause a high incidence of idiosyncratic drug reactions. Although idiosyncratic drug reactions are not usually detected in animal tests because they are just as idiosyncratic in animals as they are in humans, it is likely that drug reactive metabolites would also cause similar cell stress in animals. It is more likely that in most cases it is differences in the immune response to the reactive metabolites that determine which individuals will develop an idiosyncratic reaction. If the expression of certain proteins in animals treated with a drug candidate could be used as a screening method to predict a drug's potential to cause a high incidence of idiosyncratic drug reactions, it would greatly facilitate the development of safer drugs.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it