Pharmacogenetics and pharmacogenomics in psoriasis treatment: current challenges and future prospects
Bibliographic record
Abstract
INTRODUCTION: Topical, systemic, oral disease modifying, and biologic agents are part of the armamentarium to manage psoriatic disease. The choice of therapy depends upon disease severity, relevant co-morbidities and patient preference. There is great variability in patient response with these agents, and there is still no clear method of selecting the preferred therapeutic agent for efficacy or lack of adverse events. AREAS COVERED: This article will review the pharmacogenetic and pharmacogenomic targets that are currently known with respect to psoriasis vulgaris, and the most frequent co-morbidity of psoriasis, psoriatic arthritis. EXPERT OPINION: Presently, no clinically actionable biomarker exists for any therapeutic agent used to treat psoriasis or psoriatic arthritis. The lack of validated outcome measures and conflicting results of open-label studies conducted may be attributed to a multitude of issues that confound discovery. Consequently, studies have been underpowered to identify genes or genetic variants worth translating to clinical practice. In order to achieve a pharmacogenetic/pharmacogenomic signature, improvements in study design of future investigations are required, including carefully designed prospective studies. It is imperative to combine known clinical, serological, and molecular markers with consistent outcomes and an adequate health economic evaluation before they can be adopted widely in clinical practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".