A preclinical assessment of d.l-govadine as a potential antipsychotic and cognitive enhancer
Bibliographic record
Abstract
Tetrahydroprotoberberines (THPBs) are compounds derived from traditional Chinese medicine and increasing preclinical evidence suggests efficacy in treatment of a wide range of symptoms observed in schizophrenia. A receptor-binding profile of the THPB, d.l-govadine (d.l-Gov), reveals high affinity for dopamine and noradrenaline receptors, efficacy as a D2 receptor antagonist, brain penetrance in the 10-300 ng/g range, and thus motivated an assessment of the antipsychotic and pro-cognitive properties of this compound in the rat. Increased dopamine efflux in the prefrontal cortex and nucleus accumbens, measured by microdialysis, is observed following subcutaneous injection of the drug. d.l-Gov inhibits both conditioned avoidance responding (CAR) and amphetamine-induced locomotion (AIL) at lower doses than clozapine (CAR ED50: d.l-Gov 0.72 vs. clozapine 7.70 mg/kg; AIL ED50: d.l-Gov 1.70 vs. clozapine 4.27 mg/kg). Catalepsy is not detectable at low biologically relevant doses, but is observed at higher doses. Consistent with previous reports, acute d-amphetamine disrupts latent inhibition (LI) while a novel finding of enhanced LI is observed in sensitized animals. Treatment with d.l-Gov prior to conditioned stimulus (CS) pre-exposure restores LI to levels observed in controls in both sensitized animals and those treated acutely with d-amphetamine. Finally, possible pro-cognitive properties of d.l-Gov are assessed with the spatial delayed win-shift task. Subcutaneous injection of 1.0 mg/kg d.l-Gov failed to affect errors at a 30-min delay, but decreased errors observed at a 12-h delay. Collectively, these data provide the first evidence that d.l-Gov may have antipsychotic properties in conjunction with pro-cognitive effects, lending further support to the hypothesis that THPBs are a class of compounds which merit serious consideration as novel treatments for schizophrenia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".