Safety of Anti-Tumor Necrosis Factor Therapies in Arthritis Patients
Bibliographic record
Abstract
PURPOSE: Inflammatory and rheumatic arthritis remain leading causes of disability worldwide. The arthritis therapeutic area commands the largest market for the prescription of biological and non-steroidal anti-inflammatory drugs (NSAID). Yet biotechnology and pharmaceutical companies conducting research and providing therapeutics in this area frequently face challenges in patient safety. The purpose of our study was to assess safety of anti-tumor necrosis factor therapies in arthritis patients. METHODS: The present study systematically reviews adverse events of biologicals alone or in the presence of NSAIDs and other immunosuppressant therapeutics such as disease-modifying antirheumatic drugs (DMARD). We assessed the rheumatology literature that included clinical trials with anti-tumor necrosis factor (TNF) biologicals and case reports published between 2010 and 2014. RESULTS: Currently approved anti-TNF biologicals in arthritis include the monoclonal antibodies infliximab, adalimumab, certolizumab pegol and golimumab, and the fusion protein etanercept. The most frequently-reported adverse event was infection. We grouped the adverse reactions as immune-mediated, hypersensitivity syndrome reactions including cutaneous and hepatic manifestation, neurological, hematological, and malignancy. DISCUSSION: Most adverse events are due to the failure of host immunological control, which involves susceptibility to the drug itself, or de novo infection or reactivation of a latent bacterial or viral infection, often with a different expression of disease. Drug-induced liver injury associated with anti-TNF biologicals must be kept in mind when evaluating patients with increased liver enzymes. CONCLUSION: Risk assessment in individuals undergoing treatment with biologicals represents a step towards achieving a personalized medicine approach to identify those patients that will safely benefit from this therapeutic approach. Patients and physicians must be alert of anti-TNF agents as potential causes of drug-induced liver injury and monitor the therapies. Personalizing therapeutic pharmacovigilance promises to optimize benefits while minimizing side effects.
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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".