Targeting the CaVα-β interaction yields a selective antagonist of the N-type CaV2.2 channel with broad antinociceptive efficacy
Bibliographic record
Abstract
Abstract Inhibition of voltage-gated calcium (CaV) channels is a potential therapy for many neurological diseases including chronic pain. Neuronal CaV1/CaV2 channels are composed of α, β and α2δ subunits. The β-subunits of CaV channels are cytoplasmic proteins that increase the surface expression of the pore-forming α subunit of CaV. We targeted the high-affinity protein-protein interface of CaVβ’s pocket within the CaVα-subunit. Structure-based virtual screening of 50,000 small molecule library docked to the β-subunit led to the identification of 2-(3,5-dimethylisoxazol-4-yl)-N-((4-((3-phenylpropyl)amino)quinazolin-2-yl)methyl)acetamide (compound 45 ). This small molecule bound to CaVβ and inhibited its coupling with N-type voltage-gated calcium (CaV2.2) channels, leading to a reduction in CaV2.2 currents in rat dorsal root ganglion (DRG) sensory neurons, decreased pre-synaptic localization of CaV2.2 in vivo , decreased frequency of spontaneous excitatory post-synaptic potentials (sEPSC), and inhibited release of the nociceptive neurotransmitter calcitonin gene related peptide (CGRP) from spinal cord. 45 was antinociceptive in naïve animals and reversed allodynia and hyperalgesia in models of acute (post-surgical) and neuropathic (spinal nerve ligation, chemotherapy- and gp120-induced peripheral neuropathy, and genome-edited neuropathy) pain. 45 did not cause akinesia or motor impairment, a common adverse effect of CaV2.2 targeting drugs, when injected into the brain. 45 , a quinazoline analog, represents a novel class of CaV2.2-targeting compounds that may serve as probes to interrogate CaVα-β function and ultimately be developed as a non-opioid therapeutic for chronic pain.
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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) | 0.002 | 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".