Extent and Clinical Consequences of Antibody Formation Against Adalimumab in Patients With Plaque Psoriasis
Bibliographic record
Abstract
OBJECTIVES: To investigate the extent antibodies to adalimumab are formed in patients with plaque psoriasis and whether these antibodies have clinical consequences. Also, to examine the relationship between antibodies to adalimumab and adalimumab trough titers. DESIGN: Prospective observational cohort study. SETTING: Two Dutch dermatology departments in university hospitals. PATIENTS: All consecutive patients starting a regimen of adalimumab for chronic plaque psoriasis. Patients were screened and fulfilled the Dutch reimbursement criteria for adalimumab to treat psoriasis. INTERVENTION: Adalimumab treatment (per label). MAIN OUTCOME MEASURES: The titer of antibodies to adalimumab, the adalimumab trough concentration, and the Psoriasis Area and Severity Index at weeks 12 and 24. RESULTS: Antibodies to adalimumab were detected in 13 of 29 patients (45%) during 24 weeks of treatment. Differences in response rates among patients with low, high, and no titers of antibodies to adalimumab were significant at weeks 12 and 24 (P = .04 and P < .001, respectively). The median adalimumab trough concentrations varied significantly among patients with low, high, and no titers of antibodies to adalimumab (1.30 [range, 0.01-5.50], 0.0 [range, 0.0-0.0], and 9.6 [range, 0.0-22.6] mg/L, respectively; P < .001). At week 24, the median adalimumab trough concentrations also differed significantly among good responders, moderate responders, and nonresponders (9.7 [range, 0.0-22.6], 8.9 [range, 3.2-12.6], and 0.0 [range, 0.0-13.3] mg/L, respectively; P = .01). CONCLUSION: Antibodies to adalimumab are associated with lower serum adalimumab trough concentrations and with nonresponse or loss of response to adalimumab in patients with plaque psoriasis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".