Partial agonists in schizophrenia – why some work and others do not: insights from preclinical animal models
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".