Drug-Induced Taste Disorders In Clinical Practice And Preclinical Safety Evaluation
Bibliographic record
Abstract
More than 200 medications can induce taste disorders in patients. They not only reduce quality of life for those affected, but can lead to malnutrition, severe dehydration and difficulty in maintaining a therapeutic regimen. Nevertheless, the impact of drug candidates on taste is rarely evaluated in preclinical toxicology studies during the early stage of drug development. Moreover, knowledge about how to investigate these adverse effects is scarce in the toxicology field. Here, we discuss the clinical status of drug-induced taste disorders in patients, with the goal of providing toxicologists with a broad understanding of its prevalence, and how stressful and even dangerous it can be to affected patients. Because taste, smell, and oral trigeminal sensation are highly interdependent, we also address drug-induced changes in olfactory and oral somatosensory perceptions. We then review the biology of the gustatory system (including anatomy and histology), and the latest developments about how taste contributes to flavor perception. Finally, we feature recently optimized preclinical approaches to investigate drug-induced taste change in animal models, including morphological evaluation of taste buds and taste cells, gustatory nerve recording, and behavioral testing. Our goals are to raise awareness of drug-induced taste disorders among toxicologists, share an overview of new approaches and key studies that can be used to identify drug-induced gustatory system toxicity early in the drug development process, and to stimulate further research at this emerging interface of chemosensory disorders with toxicology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".