Pharmacogenetics of rheumatoid arthritis: Potential targets from susceptibility genes and present therapies
Bibliographic record
Abstract
Darren D O’Rielly, Proton RahmanFaculty of Medicine, Memorial University of Newfoundland, St. John’s, Newfoundland, CanadaAbstract: Rheumatoid arthritis (RA) is a chronic heterogeneous autoimmune disorder of unknown etiology resulting in inflammation in the synovium, cartilage, and bone. Genetic factors play an important role in susceptibility to RA as the heritability of RA is between 50% and 60%, with the human leukocyte antigen (HLA) locus accounting for at least 30% of overall genetic risk. Outside the major histocompatibility complex (MHC) region, six additional risk loci have been identified and validated including PTPN22, STAT4, PADI4, CTLA4, TNFAIP3-OLIG3, and TRAF1/C5. Genetic factors are also important in RA pharmacotherapy due to the gene-dependent activity of enzymes involved in the pharmacokinetics and/or pharmacodynamics of RA medications. Indeed, there is great variability in drug efficacy as well as adverse events associated with any anti-rheumatic therapy and genetics is thought to contribute significantly to this inter-individual variability in response. This review will summarize the genetic factors that have been implicated in the pathogenesis of RA, and how these determinants may factor into the potential pharmacogenetics of this disease. We will also review the therapeutic agents that are currently being utilized or presently being evaluated in the treatment of RA, along with potential pharmacogenetic markers that have been proposed for such medications.Keywords: rheumatoid arthritis, susceptibility genes, pharmacogenetics
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".