Pharmacogenetics of clozapine treatment response and side-effects in schizophrenia: an update
Bibliographic record
Abstract
INTRODUCTION: Clozapine (CLZ) is the most effective treatment for treatment-resistant schizophrenia (SCZ) patients, with potential added benefits of reduction in suicide risk and aggression. However, CLZ is also mainly underused due to its high risk for the potentially lethal side-effect of agranulocytosis as well as weight gain and related metabolic dysregulation. Pharmacogenetics promises to enable the prediction of patient treatment response and risk of adverse effects based on patients' genetics, paving the way toward individualized treatment. AREA COVERED: This article reviews pharmacogenetics studies of CLZ response and side-effects with a focus on articles from January 2012 to February 2015, as an update to the previous reviews. Pharmacokinetic genes explored primarily include CYP1A2, while pharmacodynamic genes consisted of traditional pharmacogenetic targets such as brain-derived neurotrophic factor as well novel mitochondrial genes, NDUFS-1 and translocator protein. EXPERT OPINION: Pharmacogenetics is a promising avenue for individualized medication of CLZ in SCZ, with several consistently replicated gene variants predicting CLZ response and side-effects. However, a large proportion of studies have yielded mixed results. Large-scale Genome-wide association studies (e.g., CRESTAR) and targeted gene studies with standardized designs (response measurements, treatment durations, plasma level monitoring) are required for further progress toward clinical translation. Additionally, in order to improve study quality, we recommend accounting for important confounders, including polypharmacy, baseline measurements, treatment duration, gender, and age at onset.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".