Cariprazine’s efficacy in treating affective symptoms – pooled data from schizophrenia and bipolar depression trials
Bibliographic record
Abstract
Introduction Affective symptoms are a common feature of schizophrenia and define bipolar disorder. Alterations in dopamine neurotransmission and activity at D3-D2 receptors is associated with depressive symptoms providing the rationale for targeting D3-D2 receptors with partial agonists. Objectives The aim of the analysis herein is to examine and compare the efficacy of cariprazine in treating affective symptoms in both schizophrenia and bipolar depression. Methods Data from 3 schizophrenia [NCT00694707, NCT01104766, NCT01104779] and 3 bipolar I depression studies [NCT013896447, NCT02670538, NCT0267055] were pooled for the analyses. To investigate efficacy across individual affective symptoms, the Marder anxiety/depression and negative symptom items of the Positive and Negative Syndrome Scale (PANSS) and single items of the Montgomery-Asberg Depression Rating Scale (MADRS) were analysed. Improvement across affective symptoms was examined primarily evaluating least square mean differences (LSMDs) in comparison to placebo in mean change from baseline. Results The pooled ITT population was comprised of persons with schizophrenia (placebo=442, cariprazine=1024) and bipolar disorder (placebo=460, cariprazine=923). Cariprazine resulted in a significantly greater reduction when compared to placebo in three out of the four Marder anxiety/depression items; anxiety (p<0.01), tension (p<0.001) and depression (p<0.05). Similarly, cariprazine was significantly better than placebo in 9 out of the 10 MADRS individual items; apparent sadness (p<0.001), reported sadness (p<0.001), reduced sleep (p<0.05), reduced appetite (p<0.001), concentration difficulties (p<0.001), lassitude (p<0.001), inability to feel (p<0.001), pessimistic thoughts (p<0.01) and suicidal thoughts (p<0.05). Conclusions The results herein indicate that cariprazine treatment is significantly effective at treating affective symptoms in persons with both schizophrenia and bipolar I depression. Disclosure I am an employee of Gedeon Richter Plc.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| 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) | 0.002 | 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".