Lumateperone Safety and Tolerability in Schizophrenia: A Narrative Review
Bibliographic record
Abstract
Schizophrenia remains challenging to manage, as most available antipsychotic medications alleviate symptoms but are associated with significant adverse effects such as weight gain, sedation, and extrapyramidal symptoms (EPS). Lumateperone, a recently approved antipsychotic, has attracted attention due to its unique pharmacological profile. In addition to modulating dopamine receptors, it also influences serotonin and glutamate neurotransmission, potentially addressing a broader spectrum of symptoms, including cognitive and social deficits. This narrative review synthesizes recent clinical evidence on lumateperone, evaluating study design, outcomes, and consistency across trials. Current findings suggest that lumateperone reduces core symptoms of schizophrenia while demonstrating a more favorable safety profile than many established antipsychotics. In particular, it appears to carry a lower risk of metabolic and motor side effects, which may support improved long-term adherence. Overall, this review aims to contextualize the emerging body of evidence and to evaluate the potential role of lumateperone, particularly for patients with inadequate response to conventional antipsychotics.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".