Bioenergetic deficits in peripheral nerve sensory axons during chemotherapy-induced neuropathic pain resulting from peroxynitrite-mediated post-translational nitration of mitochondrial superoxide dismutase
Bibliographic record
Abstract
Many of the widely used anticancer drugs induce dose-limiting peripheral neuropathies that undermine their therapeutic efficacy. Animal models of chemotherapy-induced painful peripheral neuropathy (CIPN) evoked by a variety of drug classes, including taxanes, vinca alkaloids, platinum-complexes, and proteasome-inhibitors, suggest that the common underlying mechanism in the development of these neuropathies is mitotoxicity in primary nerve sensory axons (PNSAs) arising from reduced mitochondrial bioenergetics [eg adenosine triphosphate (ATP) production deficits due to compromised respiratory complex I and II activity]. The causative mechanisms of this mitotoxicity remain poorly defined. However, peroxynitrite, an important pro-nociceptive agent, has been linked to mitotoxicity in several disease states and may also drive the mitotoxicity associated with CIPN. Our findings reveal that the development of mechano-hypersensitivity induced by paclitaxel, oxaliplatin, and bortezomib was prevented by administration of the peroxynitrite decomposition catalyst Mn(III) 5,10,15,20-tetrakis(N-n-hexylpyridinium-2-yl)porphyrin (MnTE-2-PyP(5+)) without interfering with their anti-tumor effects. Peak CIPN was associated with the nitration and inactivation of superoxide dismutase in the mitochondria, but not in the cytosol, as well as a significant decrease in ATP production within the PNSAs; all of these events were attenuated by MnTE-2-PyP(5+). Our results provide continued support for the role of mitotoxicity in the development of CIPN across chemotherapeutic drug classes, and identify peroxynitrite as a key mediator in these processes, thereby providing the rationale towards development of "peroxynitrite-targeted" therapeutics for CIPN.
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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".