Pharmacogenomics and response to lithium in bipolar disorder
Bibliographic record
Abstract
AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of lithium response in the treatment of bipolar disorder. We focused our research article on reports describing findings from genome-wide association studies, polygenic risk scores, and gene expression analyses associated with lithium response. METHODS: We conducted a non-systematic review of studies from PubMed/Medline by using terms such as "pharmacogenomics," "GWAS," "gene expression," and "lithium response." Inclusion criteria focused on original research involving human subjects and assessing lithium response outcomes as well as in vitro studies. An extensive pearl-growing strategy was employed to further enlarge the scope of the piece by capitalizing on the knowledge of study authors on the subject. RESULTS: The observed results, albeit promising, remain preliminary in terms of clinical relevance. Machine learning combining genetic and clinical data appears associated with moderate success in predicting lithium responsiveness. Gene expression studies and genome-wide association studies have helped identify possible targets of lithium and have the potential to support target-specific drug research. CONCLUSIONS: Pharmacogenomics may support further discoveries in precision medicine further enlarging our understanding of the underlying mechanisms of lithium for its efficacy. However, clinical applications currently appear out of reach in the near future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".