Pharmacogenetics for off-patent antipsychotics: reframing the risk for tardive dyskinesia and access to essential medicines
Bibliographic record
Abstract
First-generation antipsychotics (FGAs) induce tardive dyskinesia, a debilitating involuntary hyperkinetic movement disorder, in 20-50% of individuals with a psychotic illness during chronic treatment. There is presently no curative treatment or definitive predictive test for tardive dyskinesia. The authors note that the three antipsychotic drugs enlisted in the most recent (14th) World Health Organization Model List of Essential Medicines--chlorpromazine, fluphenazine and haloperidol--belong to the FGA therapeutic class. In this regard, the need to choose between the competing objectives of ensuring global access to affordable and efficacious medicines, such as FGAs, and the formidable long-term risk for tardive dyskinesia, may create an ethical conundrum. Pharmacogenetics has thus far been conceptually framed as a tool to individualize therapy with new drugs under patent protection. However, the authors suggest that pharmacogenetics may also improve access to pharmacotherapy through the reintroduction of affordable second-line generic drugs or FGAs with suboptimal safety, as first-line therapy, in targeted subpopulations in whom they present a lower risk for tardive dyskinesia. To impact positively on global public health and distributive justice, a directory complementary to the essential medicines library--one that enlists the 'essential biomarkers' required for optimal pharmacotherapy--may benefit patients who do not have adequate access to new antipsychotic medications. This review discusses pharmacogenetic associations of tardive dyskinesia that are in part supported by meta-analyses and the oxidative stress-neuronal degeneration hypothesis.
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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".