Pharmacogenomics of Antidepressant and Antipsychotic Treatment: How Far Have We Got and Where Are We Going?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent decades, very few new psychiatric drugs have entered the market. Thus, improvement in the use of antidepressant and antipsychotic therapy has to focus mainly on enhanced and more personalized treatment with the currently available drugs. One important aspect of such individualization is emphasizing interindividual differences in genes relevant to treatment, an area that can be termed neuropsychopharmacogenomics. Here, we review previous efforts to identify such critical genetic variants and summarize the results obtained to date. We conclude that most clinically relevant genetic variation is connected to phase I drug metabolism, in particular to genetic polymorphism of CYP2C19 and CYP2D6. To further improve individualized pharmacotherapy, there is a need to take both common and rare relevant mutations into consideration; we discuss the present and future possibilities of using whole genome sequencing to identify patient-specific genetic variation relevant to treatment in psychiatry. Translation of pharmacogenomic knowledge into clinical practice can be considered for specific drugs, but this requires education of clinicians, instructive guidelines, as well as full attention to polypharmacy and other clinically relevant factors. Recent large patient studies (n>1,000) have replicated previous findings and produced robust evidence warranting the clinical utility of relevant genetic biomarkers. To further judge the clinical and financial benefits of pre-emptive genotyping in psychiatry, large prospective randomized trials are needed to quantify the value of genetic-based patient stratification in neuropsychopharmacotherapy and to demonstrate the cost-effectiveness of such interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it