Efficacy and safety of the muscarinic receptor agonist KarXT (Xanomeline-Trospium) in schizophrenia: a systematic review, meta-analysis and Bayesian sensitivity analysis
Bibliographic record
Abstract
BACKGROUND: Schizophrenia significantly impacts global health, with existing treatments primarily focusing on positive symptoms and often causing considerable side effects. KarXT (Xanomeline-Trospium), a novel treatment combining xanomeline, a muscarinic M1/M4 receptor agonist, and trospium, targets a broader range of symptoms including negative and cognitive deficits, potentially with fewer side effects. This systematic review and meta-analysis evaluates the efficacy and safety of KarXT in treating schizophrenia, assessing symptom reduction and safety profiles compared to placebo. METHODS: We searched PubMed, Embase, and Web of Science up to November 10, 2024, for randomized controlled trials (RCTs) assessing the efficacy and safety of KarXT in schizophrenia. Data were pooled using a random-effects model, assessing outcomes like Positive and Negative Syndrome Scale (PANSS) scores and incidence of treatment-emergent adverse events (TEAEs). R software (Version 4.4.) was used for meta-analysis. RESULTS: Three RCTs involving 674 participants were included. KarXT significantly reduced total PANSS scores of mean difference (MD) -9.707 (95% CI: -12.329 to -7.085), with notable improvements in both negative (MD = -1.623; 95% CI: -2.461 to -0.785) and positive symptom subscales (MD = -3.213; 95% CI: -4.033 to -2.393). The treatment was associated with a higher incidence of TEAEs, predominantly constipation [risk ratio (RR): 2.77; 95% CI: 1.72-4.45] and nausea (RR: 4.87; 95% CI: 2.73-8.68) compared to placebo. Bayesian meta-analysis confirmed the results. CONCLUSION: KarXT offers significant improvements in both negative and positive symptoms of schizophrenia with a manageable safety profile. Its potential as a transformative treatment for schizophrenia highlights the need for further research to confirm these findings and to fully understand its long-term efficacy and safety.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.055 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".