Adalimumab drug and antibody levels as predictors of clinical and laboratory response in patients with Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Adalimumab is an effective treatment for Crohn's disease (CD). Anti-adalimumab antibodies (AAA) and low trough serum drug concentrations have been implicated as pre-disposing factors for treatment failure. AIMS: To assess adalimumab and AAA serum levels, and to examine their association and discriminatory ability with clinical response and serum C-reactive protein (CRP). METHODS: We performed a cross-sectional study using trough sera from adalimumab-treated CD patients. Demographical data, Montreal classification, treatment regimen and clinical status were recorded. Serum adalimumab, AAA and CRP were measured. Receiver operating characteristic analysis and a multivariate regression model were performed to find drug and antibody thresholds for predicting disease activity at time of serum sampling. RESULTS: One hundred and eighteen trough serum samples were included from 71 patients. High adalimumab trough serum concentration was associated with disease remission (Area Under Curve 0.748, P < 0.001). A cut-off drug level of 5.85 μg/mL yielded optimal sensitivity, specificity and positive likelihood ratio for remission prediction (68%, 70.6% and 2.3, respectively). AAA were inversely related with adalimumab drug levels (Spearman's r = -0.411, P < 0.001) and when subdivided into categorical values, positively related with disease activity (P < 0.001). High drug levels and stricturing vs. penetrating or inflammatory phenotype, but not AAA levels, independently predicted disease remission in a multivariate logistic regression model. CONCLUSIONS: Adalimumab drug levels were inversely related to disease activity. High levels of anti-adalimumab antibodies were positively associated with disease activity, but this association was mediated mostly by adalimumab drug levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".