A peptidomimetic modulator of the Ca <sub>V</sub> 2.2 N-type calcium channel for chronic pain
Bibliographic record
Abstract
Transmembrane Ca v 2.2 (N-type) voltage-gated calcium channels are genetically and pharmacologically validated, clinically relevant pain targets. Clinical block of Ca v 2.2 (e.g., with Prialt/Ziconotide) or indirect modulation [e.g., with gabapentinoids such as Gabapentin (GBP)] mitigates chronic pain but is encumbered by side effects and abuse liability. The cytosolic auxiliary subunit collapsin response mediator protein 2 (CRMP2) targets Ca v 2.2 to the sensory neuron membrane and regulates their function via an intrinsically disordered motif. A CRMP2-derived peptide (CBD3) uncouples the Ca v 2.2–CRMP2 interaction to inhibit calcium influx, transmitter release, and pain. We developed and applied a molecular dynamics approach to identify the A 1 R 2 dipeptide in CBD3 as the anchoring Ca v 2.2 motif and designed pharmacophore models to screen 27 million compounds on the open-access server ZincPharmer. Of 200 curated hits, 77 compounds were assessed using depolarization‐evoked calcium influx in rat dorsal root ganglion neurons. Nine small molecules were tested electrophysiologically, while one (CBD3063) was also evaluated biochemically and behaviorally. CBD3063 uncoupled Ca v 2.2 from CRMP2, reduced membrane Ca v 2.2 expression and Ca 2+ currents, decreased neurotransmission, reduced fiber photometry-based calcium responses in response to mechanical stimulation, and reversed neuropathic and inflammatory pain across sexes in two different species without changes in sensory, sedative, depressive, and cognitive behaviors. CBD3063 is a selective, first-in-class, CRMP2-based peptidomimetic small molecule, which allosterically regulates Ca v 2.2 to achieve analgesia and pain relief without negative side effect profiles. In summary, CBD3063 could potentially be a more effective alternative to GBP for pain relief.
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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".