Cariprazine’s efficacy in treating depressive symptoms – pooled data from schizophrenia, bipolar depression and major depression trials
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».