Adalimumab and Infliximab Impair SARS-CoV-2 Antibody Responses: Results from a Therapeutic Drug Monitoring Study in 11 422 Biologic-Treated Patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Infliximab attenuates serological responses to SARS-CoV-2 infection. Whether this is a class effect, or if anti-tumour necrosis factor [anti-TNF] level influences serological responses, remains unknown. METHODS: Seroprevalence and the magnitude of SARS-CoV-2 nucleocapsid antibody responses were measured in surplus serum from 11 422 (53.3% [6084] male; median age 36.8 years) patients with immune-mediated inflammatory diseases, stored at six therapeutic drug monitoring laboratories between January 29 and September 30, 2020. Data were linked to nationally held SARS-CoV-2 PCR results to July 11, 2021. RESULTS: Rates of PCR-confirmed SARS-CoV-2 infection were similar across treatment groups. Seroprevalence rates were lower in infliximab- and adalimumab- than vedolizumab-treated patients (infliximab: 3.0% [178/5893], adalimumab: 3.0% [152/5074], vedolizumab: 6.7% [25/375], p = 0.003). The magnitude of SARS-CoV-2 reactivity was similar in infliximab- vs adalimumab-treated patients (median 4.30 cut-off index [COI] [1.94-9.96] vs 5.02 [2.18-18.70], p = 0.164), but higher in vedolizumab-treated patients (median 21.60 COI [4.39-68.10, p < 0.004). Compared to patients with detectable infliximab and adalimumab drug levels, patients with undetectable drug levels [<0.8 mg/L] were more likely to be seropositive for SARS-CoV-2 antibodies. One-third of patients who had PCR testing prior to antibody testing failed to seroconvert, all were treated with anti-TNF. Subsequent positive PCR-confirmed SARS-CoV-2 was seen in 7.9% [12/152] of patients after a median time of 183.5 days [129.8-235.3], without differences between drugs. CONCLUSION: Anti-TNF treatment is associated with lower SARS-CoV-2 nucleocapsid seroprevalence and antibody reactivity when compared to vedolizumab-treated patients. Higher seropositivity rates in patients with undetectable anti-TNF levels support a causal relationship, although confounding factors, such as combination therapy with a immunomodulator, may have influenced the results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".