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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".