Infection profile of immune-modulatory drugs used in autoimmune diseases: analysis of summary of product characteristic data
Bibliographic record
Abstract
OBJECTIVE: Serious infection remains a concern when prescribing immune-modulatory drugs for immune-mediated inflammatory diseases. The 'summary of product characteristics' (SmPCs) provide information on adverse events for example, infections, from clinical trials and postmarketing pharmacovigilance.This review aimed to compare infection frequency, site and type across immune-modulatory drugs, reported in SmPCs. METHODS: The Electronic Medicines Compendium was searched for commonly prescribed immune-modulatory drugs used for: rheumatoid arthritis, spondyloarthritis, connective tissue disease, autoimmune vasculitis, autoinflammatory syndromes, inflammatory bowel disease, psoriasis, multiple sclerosis and/or other rarer conditions.Information was extracted on infection frequency, site and organisms. Frequency was recorded as per the SmPCs: very common (≥1/10); common (≥1/100 to<1/10); uncommon (≥1/1,000 to<1/100); rare (≥1/10,000 to<1/1,000); very rare (<1/10 000). RESULTS: 39 drugs were included, across 20 indications: 9 conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs), 6 targeted synthetic DMARDs, 24 biologic (b)DMARDs.Twelve infection sites were recorded. Minimal/no site information was available for most csDMARDs, certolizumab pegol and rituximab. Upper respiratory tract was the most common site, especially with bDMARDs. Lower respiratory, ear/nose/throat and urinary tract infections were moderately common, with clustering within drug groups.Data for 27 pathogens were recorded, majority viruses, with herpes simplex and zoster and influenza most frequent. Variable/absent reporting was noted for opportunistic and certain high-prevalence infections for example, Epstein-Barr. CONCLUSION: Our findings show differences between drugs and can aid treatment decisions alongside real-world safety data. However, data are likely skewed by trial selection criteria and varying number of trials per drug and highlight the need for robust postmarketing pharmacovigilance.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".