Partial agonists in schizophrenia – why some work and others do not: insights from preclinical animal models
Why this work is in the frame
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Bibliographic record
Abstract
While dopamine D2 receptor partial agonists (PAs) have been long considered for treating schizophrenia, only one, aripiprazole, is clinically available for therapeutic use. This raises critically important questions as to what is unique about aripiprazole and to what extent animal models can predict therapeutic success. A number of PAs whose clinical fate is known: aripiprazole, preclamol, terguride, OPC-4392 and bifeprunox were compared to haloperidol (a reference antipsychotic) in several convergent preclinical animal models; i.e. amphetamine-induced locomotion (AIL) and conditioned avoidance response (CAR), predictive of antipsychotic effects; unilateral nigrostriatal lesioned rats, a model of hypo-dopaminergia; striatal Fos induction, a molecular marker for antipsychotic activity; and side-effects common to this class of drugs: catalepsy (motor side-effects) and prolactaemia. The results were compared across drugs with reference to their measured striatal D2 receptor occupancy. All the PAs occupied striatal D2 receptors in a dose dependent manner, inhibited AIL and CAR, and lacked motor side-effects or prolactinaemia despite D2 receptor occupancy exceeding 80%. At comparative doses, aripiprazole distinguished itself from the other PAs by causing the least rotation in the hypo-dopaminergic model (indicating the least intrinsic activity) and showed the highest Fos expression in the nucleus accumbens (indicating functional D2 antagonism). Although a number of PAs are active in antipsychotic animal models, not all of them succeed. Given that only aripiprazole is clinically available, it can be inferred that low functional intrinsic activity coupled with sufficient functional antagonism as reflected in the animal models may be a marker of success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it