Ethical and Policy Considerations in the Application of Pharmacogenomic Testing for Tardive Dyskinesia: Case Study of the Dopamine D3 Receptor
Bibliographic record
Abstract
Tardive dyskinesia (TD) is a serious adverse effect often associated with the first generation antipsychotic medications used in the management of mental health disorders such as schizophrenia. Pharmacogenomics is the study of human genomic variation in relation to individual and population variability in medication response and side effects. Neuropsychiatry is one of the clinical domains in which pharmacogenomic approaches have been extensively studied. In the late 1990s, the Glycine9 (Gly9) allele of the Serine-9-Glycine (Ser9Gly) polymorphism in dopamine D3 receptor gene (DRD3) was found to be associated with both a liability to, and worsened severity of, TD in schizophrenic patients treated with typical antipsychotics. This initial discovery has been subsequently replicated and testing for the Ser9Gly polymorphism has now become commercially available. The question that currently presents itself is whether its use should be encouraged for patients who may be prescribed a typical or atypical antipsychotic medication. However, the translation of this new technology to clinical practice presents multiple social, ethical and policy challenges. Though pharmacogenomic testing holds much promise in this scenario, many important questions remain to be answered before its widespread use can be medically and ethically justified. This article highlights the key advances in our understanding of the role of human genetic variation in the D3 receptor in relation to TD. Then, issues of uncertainty, consent, confidentiality, and access are considered with respect to the use of DRD3 polymorphism testing in risk stratification for susceptibility to tardive dyskinesia. We propose three recommendations that may help bring this technology into the clinic: 1) prospective pharmacogenomic studies of DRD3 polymorphism and TD risk should be conducted; 2) the design of such studies should be influenced by scientists, ethicists and policy makers to protect potentially vulnerable patients; and 3) appropriate knowledge transfer to front-line health care workers must take place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".