Betulinic acid, derived from the desert lavender Hyptis emoryi, attenuates paclitaxel-, HIV-, and nerve injury–associated peripheral sensory neuropathy via block of N- and T-type calcium channels
Bibliographic record
Abstract
The Federal Pain Research Strategy recommended development of nonopioid analgesics as a top priority in its strategic plan to address the significant public health crisis and individual burden of chronic pain faced by >100 million Americans. Motivated by this challenge, a natural product extracts library was screened and identified a plant extract that targets activity of voltage-gated calcium channels. This profile is of interest as a potential treatment for neuropathic pain. The active extract derived from the desert lavender plant native to southwestern United States, when subjected to bioassay-guided fractionation, afforded 3 compounds identified as pentacyclic triterpenoids, betulinic acid (BA), oleanolic acid, and ursolic acid. Betulinic acid inhibited depolarization-evoked calcium influx in dorsal root ganglion (DRG) neurons predominantly through targeting low-voltage-gated (Cav3 or T-type) and CaV2.2 (N-type) calcium channels. Voltage-clamp electrophysiology experiments revealed a reduction of Ca, but not Na, currents in sensory neurons after BA exposure. Betulinic acid inhibited spontaneous excitatory postsynaptic currents and depolarization-evoked release of calcitonin gene-related peptide from lumbar spinal cord slices. Notably, BA did not engage human mu, delta, or kappa opioid receptors. Intrathecal administration of BA reversed mechanical allodynia in rat models of chemotherapy-induced peripheral neuropathy and HIV-associated peripheral sensory neuropathy as well as a mouse model of partial sciatic nerve ligation without effects on locomotion. The broad-spectrum biological and medicinal properties reported, including anti-HIV and anticancer activities of BA and its derivatives, position this plant-derived small molecule natural product as a potential nonopioid therapy for management of 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) | 0.001 | 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".