Genetics and Personalized Medicine in Antidepressant Treatment
Bibliographic record
Abstract
BACKGROUND: Antidepressant medication is a major cornerstone in treatment of mood and anxiety disorders. Numerous substances are available on the market; however, only 60% of treated patients show sufficient response to medication and side effects are common. Lengthy trials are not uncommon until the optimized drug and dose is found and unfortunately, no valid predictors to match the 'right' drug to the 'right' patient exist nowadays. Genetic factors are thought to be involved as evidenced by numerous pharmacogenetic studies. This comprehensive review summarizes the most interesting findings and discusses clinical implications of pharmacogenetic results. METHODS: We reviewed available literature on pharmacogenetics of antidepressant response and side effects until summer 2011 using the PubMed database. RESULTS: Promising findings exist for several variants in candidate genes involved in the pharmacokinetics or pharmacodynamics of antidepressants. These include association findings in the serotonin transporter gene (5-HTT), serotonin receptor genes, a gene coding an efflux pump in the blood-brain-barrier (ABCB1), and genes involved in the HPA axis. Promising candidate genes increasing risk for side effects include some of the genes associated with treatment response and cytochrome P450 genes. CONCLUSION: A high number of studies on pharmacogenetics of antidepressants have been published during the past decades. However, contradictory results still limit clinical use of these findings. Future studies should include functional analyses and consider gene-gene and gene-environment interactions. This will aid in facilitating a future use of pharmacogenetics in clinical practice, likely leading to improved patient care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".