Identification of a Reactive Metabolite of Terbinafine: Insights into Terbinafine-Induced Hepatotoxicity
Bibliographic record
Abstract
Oral terbinafine treatment for superficial fungal infections of toe and fingernails is associated with a low incidence (1:45000) of hepatobiliary dysfunction. Due to the rare and unpredictable nature of this adverse drug reaction, the mechanism of toxicity has been hypothesized to be either an uncommon immunological or metabolically mediated effect. However, there is little evidence to support either mechanism, and toxic metabolites of terbinafine have not been identified. We incubated terbinafine with both rat and human liver microsomal protein in the presence of GSH and were able to trap an allylic aldehyde, 7,7-dimethylhept-2-ene-4-ynal (TBF-A), which corresponds to the N-dealkylation product of terbinafine. TBF-A was also prepared synthetically and reacted with excess GSH to yield conjugates with HPLC retention times and mass spectra identical to those generated in the microsomal incubations. The major GSH conjugate, characterized by (1)H NMR, corresponds to addition of GSH in a 1,6-Michael fashion. There remains a second electrophilic site on this metabolite, which can bind either to a second molecule of GSH or to cellular proteins via a 1,4-Michael addition mechanism. Moreover, we demonstrated that the formation of the GSH conjugates was reversible. We speculate that this allylic aldehyde metabolite, formed by liver enzymes and conjugated with GSH, would be transported across the canalicular membrane of hepatocytes and concentrated in the bile. The mono-GSH conjugate, which is still reactive, could bind to hepatobiliary proteins and lead to direct toxicity. Alternatively, it could modify canalicular proteins and lead to an immune-mediated reaction causing cholestatic dysfunction.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".