Recently patented agents with possible antipsychotic indication
Bibliographic record
Abstract
Background: The use of antipsychotic medication for disparate psychiatric and medical disorders has increased rapidly during the past decade. Newer second-generation antipsychotic agents have largely replaced older agents in many regions; nevertheless, most treated individuals remain insufficiently responsive and/or intolerant inviting the need for newer agents with novel mechanisms. Objective/method: Herein, we review the US and Canadian databases regarding recently patented agents (i.e., 2003 – 2008) with putative antipsychotic properties. Additionally, a PubMed review was conducted utilizing the key search term antipsychotic, cross-referenced with: novel, mechanism, psychoses, mania and indication. A manual review of the bibliographies in each of the retrieved articles was also conducted. Conclusion: Hitherto, antagonism of the canonical dopamine D2 receptor has been the prevailing hypothesis regarding the mechanism of action of all approved antipsychotic agents. During the past 5 years, several newer patented agents have been introduced that exhibit unique pharmacodynamic profiles, for example, D3/D4 receptor antagonism, 5-HT2C receptor agonism/partial agonism, selective serotonin re-uptake/α2-adrenoceptor antagonism and mGlu2/3 receptor agonism. Efforts to refine novel treatments have provided the impetus for testing and patenting agents with novel pharmacodynamic mechanisms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".