A narrative review of non-racemic amisulpride (SEP-4199) for treatment of depressive symptoms in bipolar disorder and LB-102 for treatment of schizophrenia
Bibliographic record
Abstract
Introduction The challenges posed by treatment-resistant schizophrenia and depressive symptoms have led to ongoing difficulties despite the availability of antipsychotics and antidepressants. This review addresses the potential of amisulpride analogs, particularly SEP-4199, in addressing these challenges through enhanced efficacy and reduced side effects.Areas covered This review focuses on the pharmacological profile of amisulpride analogs, exemplified by LB-102 and its derivative SEP-4199. PubMed gathered articles (up to 10 March 2023) on ‘amisulpride,’ ‘schizophrenia,’ ‘bipolar disorder,’ and ‘major depressive disorder;’ ClinicalTrials.gov tracked SEP-4199 and LB-102 trials. LB-102, a newly identified N-methylated analog of amisulpride, exhibits enhanced lipophilicity at lower doses, as demonstrated in a phase 1 study, indicating significant promise for therapeutic applications. The discovery of SEP-4199, a non-racemic analog composed of R- and S-enantiomers in an 85:15 ratio, is discussed, emphasizing its potential to enhance antidepressant effects while minimizing extrapyramidal side effects via selective D2 receptor binding. Recent phase 2 trials have demonstrated SEP-4199’s efficacy in treating depressive symptoms in bipolar disorder I, capitalizing on D2-mediated anti-anhedonic and D3-mediated reward effects.Expert opinion The development of SEP-4199 presents a potential breakthrough for managing depressive symptoms in bipolar disorder I. Further exploration of D2 and D3 receptor-mediated effects could lead to improved treatment strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".