An evaluation of the anti‐hyperalgesic effects of cannabidiolic acid‐methyl ester in a preclinical model of peripheral neuropathic pain
Bibliographic record
Abstract
Background and Purpose Chronic neuropathic pain (NEP) is associated with growing therapeutic cannabis use. To promote quality of life without psychotropic effects, cannabinoids other than Δ9‐tetrahydrocannabidiol, including cannabidiol and its precursor cannabidiolic acid (CBDA), are being evaluated. Due to its instability, CBDA has been understudied, particularly as an anti‐nociceptive agent. Adding a methyl ester group (CBDA‐ME) significantly enhances its stability, facilitating analyses of its analgesic effects in vivo. This study examines early treatment efficacy of CBDA‐ME in a rat model of peripherally induced NEP and evaluates sex as a biological variable. Experimental Approach After 14 consecutive days of intraperitoneal CBDA‐ME administration at 0.01, 0.1 and 1 μg·kg−1, commencing 1 day after surgically implanting a sciatic nerve‐constricting cuff to induce NEP, the anti‐nociceptive efficacy of this cannabinoid was assessed in male and female Sprague–Dawley rats relative to vehicle‐treated counterparts. In females, 2 and 4 μg·kg−1 daily doses of CBDA‐ME were also evaluated. Behavioural tests were performed for hind paw mechanical and thermal withdrawal thresholds once a week for 8 weeks. At endpoint, in vivo electrophysiological recordings were obtained to characterize soma threshold changes in primary sensory neurons. Key Results In males, CBDA‐ME elicited a significant concentration‐dependent chronic anti‐hyperalgesic effect, also influencing both nociceptive and non‐nociceptive mechanoreceptors, which were not observed in females at any of the concentrations tested. Conclusion and Implications Initiating treatment of a peripheral nerve injury with CBDA‐ME at an early stage post‐surgery provides anti‐nociception in males, warranting further investigation into potential sexual dimorphisms underlying this response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".