Interaction of Erdosteine with TrkA Signaling Pathways: Implications for Analgesia
Bibliographic record
Abstract
Thiol-containing drugs may interact with a region of tropomyosin receptor kinase A (TrkA), potentially inhibiting its activation by nerve growth factor (NGF). This action has been linked to potential analgesic activities. Here, we describe the ability of erdosteine, a thiolic compound classified as a mucolytic agent, to bind to the TrkA receptor sequence in silico and its in vitro effects on TrkA activation induced by NGF in cultured human neuroblastoma cells. Our results show that erdosteine and its metabolite, Met-1, bind to the TrkA receptor pocket, involving the primary TrkA residues Glu331, Arg347, His298, and His297. Furthermore, Met-1 has the ability to reduce the disulfide bridge between Cys300 and Cys345 of TrkA. In vitro measurement of TrkA autophosphorylation following NGF activation confirmed that erdosteine and Met-1 interfere with NGF-induced TrkA activation, leading to a consequent loss of the molecular recognition and spatial reorganization necessary for the induction of the autophosphorylation process. This effect was inhibited by low millimolar concentrations of the two compounds, reaching a maximal inhibition (around 40%) after 24 h of exposure to 1 mM erdosteine, and then plateauing. These findings suggest that erdosteine can act as a TrkA antagonist, thus indicating that this drug may have potential as an analgesic via a novel non-opioid mechanism of action operating through NGF signaling inhibition at the level of TrkA.
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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 | 0.000 | 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".