Advancements in Non-Dopaminergic Treatments for Schizophrenia: A Systematic Review of Pipeline Developments
Bibliographic record
Abstract
INTRODUCTION: Conventional antipsychotic drugs that attenuate dopaminergic neural transmission are ineffective in approximately one-third of patients with schizophrenia. This necessitates the development of non-dopaminergic agents. METHODS: A systematic search was conducted for completed phase II and III trials of compounds for schizophrenia treatment using the US Clinical Trials Registry and the EU Clinical Trials Register. Compounds demonstrating significant superiority over placebo in the primary outcome measure in the latest phase II and III trials were identified. Collateral information on the included compounds was gathered through manual searches in PubMed and press releases. RESULTS: Sixteen compounds were identified; four compounds (ulotaront, xanomeline/trospium chloride, vabicaserin, and roluperidone) were investigated as monotherapy and the remaining 12 (pimavanserin, bitopertin, BI 425809, encenicline, tropisetron, pregnenolone, D-serine, estradiol, tolcapone, valacyclovir, cannabidiol, and rimonabant) were examined as add-on therapy. Compared to the placebo, ulotaront, xanomeline/trospium chloride, vabicaserin, bitopertin, estradiol, cannabidiol, rimonabant, and D-serine showed efficacy for positive symptoms; roluperidone and pimavanserin were effective for negative symptoms; and encenicline, tropisetron, pregnenolone, tolcapone, BI 425809, and valacyclovir improved cognitive function. DISCUSSION: Compounds that function differently from existing antipsychotics may offer novel symptom-specific therapeutic strategies for patients with 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".