C2230, a preferential use- and state-dependent CaV2.2 channel blocker, mitigates pain behaviors across multiple pain models
Bibliographic record
Abstract
Antagonists - such as Ziconotide and Gabapentin - of the CaV2.2 (N-type) calcium channels are used clinically as analgesics for chronic pain. However, their use is limited by narrow therapeutic windows, difficult dosing routes (Ziconotide), misuse, and overdoses (Gabapentin), as well as a litany of adverse effects. Expansion of novel pain therapeutics may emerge from mechanism-based interrogation of CaV2.2. Here, we report the identification of C2230, an aryloxy-hydroxypropylamine, as a CaV2.2 blocker. C2230 trapped and stabilized inactivated CaV2.2 in a slow-recovering state and accelerated the open-state inactivation of the channel, conferring an advantageous use-dependent inhibition profile. C2230 inhibited CaV2.2 during high-frequency stimulation, while sparing other voltage-gated ion channels. C2230 inhibited CaV2.2 in dorsal root and trigeminal ganglia neurons from rats, marmosets, and humans in a G-protein-coupled-receptor-independent manner. Further, C2230 reduced evoked excitatory postsynaptic currents and excitatory neurotransmitter release in the spinal cord, leading to relief of neuropathic, orofacial, and osteoarthritic pain-like behaviors via 3 different routes of administration. C2230 also decreased fiber photometry-based calcium responses in the parabrachial nucleus, mitigated aversive behavioral responses to mechanical stimuli after neuropathic injury, and preserved protective pain responses, all without affecting motor or cardiovascular function. Finally, site-directed mutation analysis demonstrated that C2230 binds differently than other known CaV2.2 blockers, making it a promising lead compound for analgesic development.
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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 | 0.001 |
| 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".