Understanding Anti-TNF Treatment Failure: Pre-Existing Antidrug Antibodies Are Common but Do Not Neutralize Drug Activity In Vitro or Influence Clinical Response to Infliximab or Adalimumab in Patients with Crohn Disease
Bibliographic record
Abstract
BACKGROUND: We sought to determine the prevalence, clinical impact, neutralizing capacity, and cross-reactivity of pre-existing antibodies to infliximab and adalimumab in antitumor necrosis factor (TNF) treatment-naïve patients with active luminal crohn disease. METHODS: The prevalence of antibodies to infliximab and adalimumab at entry to the "Personalised Anti-TNF Therapy in Crohn's Disease Study" (PANTS) were measured using the drug-tolerant IDKmonitor Total antidrug antibody ELISAs. Neutralizing capacity in positive cases was determined using iLite™ cell-based assays. RESULTS: Pre-existing antibodies to infliximab were more common (6.3%, 96/1525 vs 2.4%, 36/1525, P < 0.01) and detectable at higher concentrations [median (IQR) 23.9 (14.2-55.2) AU/mL vs 7.2 (6.3-10.4) AU/mL, P < 0.0001] to infliximab than adalimumab. A few patients [1.3% (95% CI 0.6-1.8) 20/1525] had antidrug antibody reactivity to both drugs. None of the detected antidrug antibodies had demonstrable anti-TNF neutralizing capacity. No associations were seen between pre-existing antibody positivity, adverse drug reactions, drug levels, or response status at weeks 14 or 54 or subsequent immunogenicity. Cross-reactivity with rheumatoid factor and antimouse antibodies was detected (>10 IU/mL) in 6.6% (95% CI 3.2-13.0) (7/106) and 17.0% (95% CI 10.5-25.2) (19/112) of patients with pre-existing antibodies. CONCLUSIONS: Pre-existing antidrug antibodies are common but do not neutralize anti-TNF drug activity in vitro, promote drug clearance, or influence clinical response to anti-TNF drugs. Further work is required to understand this cross-reactivity and to determine the exact nature of the antibodies detected in drug-naïve individuals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".