Risk of Infections of Biological Therapies with Accent on Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Biological therapies using anti-tumor necrosis factor (TNF)-α agents have an important impact in the treatment of inflammatory bowel disease, rheumatoid arthritis, psoriasis, and other inflammatory conditions. However, a significant number of patients lose their response to these medications over time. Clinical trials have demonstrated that antibodies against anti-TNF agents may impact treatment response and increase the risk of infusion reactions. Of concern is also the possibility of developing adverse events induced by anti-TNF agents. The purpose of the present systematic review is to describe the current knowledge on the risk of infections associated with anti-TNF agents antagonists, as well as integrin antagonists. We also intend to describe case reports of these adverse events in inflammatory bowel disease patients. METHODS: Currently approved anti-TNF biologicals in IBD include the monoclonal antibodies infliximab, adalimumab, certolizumab pegol and golimumab. Integrin antagonists include natalizumab, etrolizumab and vedolizumab. RESULTS: The most frequently-reported adverse events of these biologicals were infections, and these are described in detail in this study. DISCUSSION: Most adverse events are due to the failure of host immunological control, which involves de novo infection, or reactivation of latent bacterial or viral infection, often with a different expression of disease. CONCLUSION: Risk assessment in individuals undergoing treatment with biologicals represents a step towards achieving treatment personalization to identify those patients that will safely benefit from this therapeutic approach. Patients and physicians must be alert for anti-TNF agents and anti-integrin medication as potential causes of drug-induced infections and monitor the therapies. Personalizing therapeutic vigilance promises to optimize benefits while minimizing infections.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".