Pharmacogenetics of antidepressant and mood-stabilizing drugs: a review of candidate-gene studies and future research directions
Bibliographic record
Abstract
Heterogeneity of clinical response to antidepressant and mood-stabilizing drugs and susceptibility to adverse effects are major clinical problems. It is reasonable to suggest a genetic contribution to these inter-individual differences. Thus, pharmacogenetic approaches could provide the clinician with tools to individualize pharmacotherapy. In this paper, published reports that address the genetic basis of response to antidepressant drugs and mood-stabilizing drugs are selectively reviewed. There is substantial support for the assumption that genetic factors play a role in response to lithium and a degree of support for a role of such factors in response to antidepressants. Based on a Medline search and access to papers accepted but not yet published, studies on the role of specific candidate genes are comprehensively evaluated. A number of studies from different groups point to a role for polymorphism of the serotonin transporter gene in the therapeutic response to specific serotonin reuptake inhibitors. There are reports of other candidate genes, particularly in the serotonergic system, but these have still to be replicated. There is little evidence thus far that points to a role for specific candidate genes in response to mood-stabilizing drugs. Future research directions including the selection of relevant candidate genes, pivotal issues in the design of studies and high throughput methods of analysis are discussed in the light of the findings. Although pharmacogenetic approaches have great potential in the treatment of major depression and bipolar disorder, substantial further research is needed. Careful attention needs to be paid to research design issues and potential confounding factors such as population stratification. High throughput, genome-wide approaches could greatly accelerate the acquisition of relevant data but their success is dependent on the availability of appropriate clinical samples.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".