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Record W4413105979 · doi:10.1093/ijnp/pyaf059

Trajectory of efficacy and safety across ulotaront dose levels in schizophrenia: a systematic review and dose–response meta-analysis

2025· review· en· W4413105979 on OpenAlexaff
Yang‐Chieh Brian Chen, Kuo‐Chuan Hung, Chih‐Sung Liang, Ping‐Tao Tseng, Yu‐Kang Tu, Christoph U. Correll, Chih‐Wei Hsu, Marco Solmi

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Science and Technology CouncilChang Gung Medical Foundation
KeywordsAdverse effectPositive and Negative Syndrome ScaleMeta-analysisInternal medicineMedicineSchizophrenia (object-oriented programming)Relative riskRandomized controlled trialStrictly standardized mean differenceConfidence intervalPlaceboAntipsychoticPsychologyPsychiatryPsychosis

Abstract

fetched live from OpenAlex

BACKGROUND: Ulotaront is an experimental antipsychotic for schizophrenia, but its optimal dose is unclear. This study aimed to evaluate dose-response relationships for efficacy and safety in people with schizophrenia. METHODS: A systematic review of four databases (until January 22, 2025; INPLASY202510091) identified randomized clinical trials assessing ulotaront. Outcomes included efficacy, measured by changes in the Positive and Negative Syndrome Scale (PANSS) total score (primary outcome), positive and negative subdomains, and the Clinical Global Impression Scale-Severity, and safety, assessed by all-cause dropout (co-primary outcome, dropout due to adverse event, serious, non-serious, and specific adverse events). We employed one-stage dose-response meta-analysis (random-effects model) calculating standardized mean differences (SMDs) and risk ratios (RRs) with 95% confidence intervals (CIs). RESULTS: Analysis of three randomized clinical trials (n = 1144) indicated that the 100 mg dose of ulotaront provided the greatest improvement in PANSS total score (standardized mean difference = -0.23 [95% CI: -0.43, -0.02]), PANSS positive symptom score (-0.30 [-0.70, 0.10]), and PANSS negative symptom score (-0.28 [-0.48, -0.08]). However, Clinical Global Impression Scale-Severity scores did not exhibit a clear dose-response relationship. Regarding safety, all-cause dropout (RR at 100 mg = 1.10 [95% CI: 0.57, 2.12]), adverse event-related dropout, serious, non-serious, and most specific adverse events showed no significant dose-response relationship. The risk of anxiety-related adverse events was significantly higher than placebo at 50 and 75 mg doses (RR at 75 mg = 2.06 [95% CI: 1.11, 3.80]). CONCLUSION: Ulotaront 100 mg appears greatest efficacy with favorable safety for acute schizophrenia. However, effect sizes were small, and higher ulotaront doses should be tested. Significance Statement Ulotaront is a new medication being tested for treating schizophrenia. Unlike most existing antipsychotic drugs that block dopamine receptors in the brain, ulotaront works through a different mechanism by activating trace amine-associated receptor 1 and serotonin 1A receptors. These novel targets may help reduce both hallucinations and negative symptoms like social withdrawal and lack of motivation, with fewer side effects. In this study, we analyzed data from several clinical trials to understand how different doses of ulotaront affect patients. We found that higher doses-especially around 100 mg-can improve schizophrenia symptoms without increasing safety concerns. These findings are important because they suggest that ulotaront may offer a new and safer treatment option for people with schizophrenia, and they help guide doctors toward the most effective dose.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.035
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.046
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.092
GPT teacher head0.449
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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