Emerging drugs for antipsychotic-induced tardive dyskinesia: investigational drugs in Phase II and Phase III clinical trials
Bibliographic record
Abstract
INTRODUCTION: Antipsychotic drugs (APs) represent the mainstay of treatment for schizophrenia and other forms of psychosis. Tardive dyskinesia (TD) is a motor disorder associated with the ongoing use of APs and is characterized by involuntary, repetitive movements that are potentially irreversible. Current treatment is wanting, due in part to our limited understanding of the mechanisms underlying TD. AREAS COVERED: Risk of TD associated with APs appears linked to continuous blockade of dopamine D2 receptors in the basal ganglia. Proposed mechanisms include increased dopamine activation of D2 receptors caused by supersensitivity and neurodegeneration of dopamine-producing neurons due to biochemical changes incurred by ongoing AP exposure. Existing treatments are designed to reverse or prevent the neurochemical/biological changes caused by dopamine D2 receptor blockade and include vesicular monoamine transporter (VMAT) inhibitors, antioxidants, compounds with serotonin receptor agonism as well as antagonism, GABA agonists and cholinergic agents. Randomized, controlled trials in Phase II and Phase III (ClinicalTrials.org/ClinicalTrialsRegister.eu) are summarized and discussed. EXPERT OPINION: Effective adjunctive treatment for the symptoms of TD will depend on gaining a better understanding of the neurological changes induced by chronic dopamine D2 receptor antagonism from APs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".