Cariprazine’s efficacy in treating depressive symptoms – pooled data from schizophrenia, bipolar depression and major depression trials
Bibliographic record
Abstract
Introduction Depressive symptoms are a common feature of schizophrenia (SCH) and define bipolar disorder and major depressive disorder (MDD). Their emergence is related to altered neurotransmission at the serotonin receptors and potentially at dopamine D3 receptors. Objectives The aim of this analysis was to examine the efficacy of cariprazine (CAR) in treating depressive symptoms in SCH, bipolar depression (BD) and MDD. Methods Clinical trials with randomised, double-blind, placebo (PLB)-controlled designs were included in these analyses. Data from 3 SCH [NCT00694707, NCT01104766, NCT01104779; 1.5-9 mg/d] and 3 BD [NCT01396447, NCT02670538, NCT02670551; 1.5-3 mg/d] studies were pooled. In MDD, add-on CAR to antidepressant treatment was evaluated against PLB in two studies [NCT03738215: 1.5 and 3 mg/d; NCT01469377: 1-2 mg/d and 2-4.5 mg/d). Least square (LS) mean changes were analysed using Mixed Model Repeated Measures: from baseline (BL) to Week 6 in the Positive and Negative Syndrome Scale (PANSS)-derived Marder anxiety/depression factor items (schizophrenia); from BL to Week 6 in the Montgomery-Åsberg Depression Rating Scale (MADRS) total scores (bipolar depression); and from BL to Week 6 [NCT03738215] and Week 8 [NCT01469377] in MADRS total score (major depressive disorder). Results Altogether, 1466 SCH (PLB=442, CAR=1024) patients were included in the pooled analysis. In the BD analysis, data from 1383 (PLB=460, CAR=923) patients were pooled. In the MDD trials, there were 502 CAR (1.5mg/d=250, 3 mg/d=252) and 249 PLB-treated patients [NCT03738215], and 544 CAR (1-2 mg/d=273, 2-4.5 mg/d=271) and 264 PLB patients in the other study [NCT01469377]. In SCH, CAR achieved significantly greater reductions than PLB on the Marder anxiety/depression factor domain (LS mean change: PLB= -2.66, CAR= -3.26, p<0.01): the effect was driven by 3 out of 4 items. In BD, CAR yielded significantly greater improvement on the MADRS compared to PLB (LS mean change: PLB= -12.05, CAR= -14.69, p<0.001), which was driven by 9 out of 10 items. In MDD [NCT03738215], CAR 1.5 mg/d add-on significantly alleviated depressive symptoms compared to PLB (LS mean change: PLB= -11.5, CAR 1.5mg/d= -14.1, p<0.01), while in the other MDD trial [NCT01469377], CAR 2-4.5 mg/d add-on produced significantly greater reductions than PLB (LS mean change: PLB= -12.5, CAR 2-4.5 mg/d= -14.6, p<0.01). Conclusions These findings indicate that CAR is an effective treatment option for the treatment of depressive symptoms independent of disease (in SCH, BD and MDD), being a transdiagnostic broad-spectrum treatment option. Disclosure of Interest R. McIntyre Grant / Research support from: CIHR/GACD/National Natural Science Foundation of China (NSFC), the Milken Institute, Consultant of: Lundbeck, Janssen, Alkermes, Neumora Therapeutics, Boehringer Ingelheim, Sage, Biogen, Mitsubishi Tanabe, Purdue, Pfizer, Otsuka, Takeda, Neurocrine, Sunovion, Bausch Health, Axsome, Novo Nordisk, Kris, Sanofi, Eisai, Intra-Cellular, NewBridge Pharmaceuticals, Viatris, Abbvie, Gedeon Richter, Recordati, Atai Life Sciences, Speakers bureau of: Lundbeck, Janssen, Alkermes, Neumora Therapeutics, Boehringer Ingelheim, Sage, Biogen, Mitsubishi Tanabe, Purdue, Pfizer, Otsuka, Takeda, Neurocrine, Sunovion, Bausch Health, Axsome, Novo Nordisk, Kris, Sanofi, Eisai, Intra-Cellular, NewBridge Pharmaceuticals, Viatris, Abbvie, Gedeon Richter, Recordati, Atai Life Sciences, R. Csehi Employee of: Gedeon Richter Plc., G. Németh Employee of: Gedeon Richter Plc.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".