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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".