Lithium Pharmacogenetics: Where Do We Stand?
Bibliographic record
Abstract
Preclinical Research Bipolar disorder (BPD) is a chronic and disabling psychiatric disorder with a prevalence of 0.8-1.2% in the general population. Although lithium is considered the first-line treatment, a large percentage of patients do not respond sufficiently. Moreover, lithium can induce severe side effects and has poor tolerance and a narrow therapeutic index. The genetics of lithium response has been largely investigated, but findings have so far failed to identify reliable biomarkers to predict clinical response. This has been largely determined by the highly complex phenotipic and genetic architecture of lithium response. To this regard, collaborative initiatives hold the promise to provide robust and standardized methods to disantenagle this complexity, as well as the capacity to collect large samples of patietnts, a crucial requirement to study the genetics of complex phenotypes. The International Consortium on Lithium Genetics (ConLiGen) has recently published the largest study so far on lithium response reporting significant associations for two long noncoding RNAs (lncRNAs). This result provides relevant insights into the pharmacogenetics of lithium supporting the involvement of the noncoding portion of the genome in modulating clinical response. Although a vast body of research is engaged in dissecting the genetic bases of response to lithium, the several drawbacks of lithium therapy have also stimulated multiple efforts to identify new safer treatments. A drug repurposing approach identified ebselen as a potential lithium mimetic, as it shares with lithium the ability to inhibit inositol monophosphatase. Ebselen, an antioxidant glutathione peroxidase mimetic, represents a valid and promising example of new potential therapeutic interventions for BD, but the paucity of data warrant further investigation to elucidate its potential efficacy and safety in the management of BPD. Nevertheless, findings provided by the growing field of pharmacogenomic research will ultimately lead to the identification of new molecular targets and safer treatments for BPD. Drug Dev Res 77 : 368-373, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".