Selective Effects of D- and L-Govadine in Preclinical Tests of Positive, Negative, and Cognitive Symptoms of Schizophrenia
Bibliographic record
Abstract
There is a critical need to develop novel pharmacotherapeutics capable of addressing the positive, negative, and cognitive symptoms of schizophrenia. Building on recent studies with a racemic mixture of the synthetic tetrahydroprotoberberine, D,L-Govadine, we isolated the D- and L-stereoisomers and employed a battery of behavioral, neurochemical, and electrophysiological procedures to assess their individual therapeutic potential. Rodent models predictive of antipsychotic efficacy and those that model positive symptoms were employed and we found that L-Govadine, but not D-Govadine, improved these measures. Pretreatment with either stereoisomer during CS pre-exposure prevented the disruption of latent inhibition by amphetamine. Moreover, pretreatment with either stereoisomer also improved deficits in social interaction in the neonatal ventral hippocampal lesioned rat. Improved cognitive performance in two different prefrontal cortex-dependent tasks was observed with D-, but not L-Govadine, which strongly suggests that the D-steroisomer may be an effective cognitive enhancer. Alterations in dopamine efflux were also assessed and L-Govadine increased dopamine efflux in both the prefrontal cortex and nucleus accumbens. However, D-Govadine only increased dopamine efflux in the prefrontal cortex and not in the nucleus accumbens. Electrophysiological studies confirmed that L-Govadine is a DA-D2 antagonist, whereas D-Govadine shows no appreciable physiological effects at this receptor. Collectively these data show that L-Govadine performs well on measures predictive of antipsychotic efficacy and rodent models of positive symptoms through antagonism of DA-D2 receptors, whereas D-Govadine improves impairments in compromised memory function in delayed response tasks possibly through selective increases in DA efflux in the frontal cortex.
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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 | 0.001 |
| 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".