Editorial: drug monitoring targets for optimising adalimumab in Crohn's disease
Bibliographic record
Abstract
The reasons for loss of response to adalimumab are poorly understood. Previous studies of infliximab have shown that measuring drug and antibody-to-infliximab concentrations may be valuable tools for optimising TNF-inhibitors.1, 2 In their cross-sectional study,3 Mazor and colleagues presented new evidence regarding the relationship between adalimumab trough concentrations, antibodies to adalimumab (ATA), and clinical outcomes in Crohn's disease (CD). Specifically, they proposed targets for adalimumab monitoring that are associated with clinical remission. In their study of 118 samples from 71 patients, adalimumab drug and ATA were inversely related to disease activity measured by physician's global assessment and CRP. Clinical assessment is limited by the fact that CRP may miss mucosal inflammation in a third of patients with CD,4 A concentration ≥ 5.85 μg/mL had the optimal sensitivity and specificity for remission (68.6%, 70.6%, respectively). A value of ATA ≥3 μg/mL-eq had a specificity of 98% (95% CI 95.5–100%) for active disease. ATA and adalimumab trough concentrations had modest significant associations with CRP (r = 0.29, P = 0.002 and r = −0.425, P < 0.001). These associations may be influenced by the study population, who were largely in remission, and that samples were collected at unselected troughs rather than at time when a patient experienced a flare. About 30.5% of sera had ATA; however, the smaller proportion of antibody positive patients is unknown. Other factors that confound outcomes such as body mass index, albumin and prior anti-TNF exposure were not reported. The lack of data regarding the timing of initiation of immunomodulators limits the ability to interpret their impact. Despite these limitations, little published data beyond abstracts regarding the optimal trough concentrations for adalimumab exists. Mazor and colleagues have proposed specific targets for optimising the efficacy of adalimumab therapy. These targets, and strategies utilising them, remain to be validated and further understood in larger clinical trials5 of therapeutic monitoring in clinical practice. In addition, important clinical questions of how and when to assess these measures, how to optimise therapy based on the results, and how other factors contribute to these complex interactions require further study. Declaration of personal interests: Jason M. Swoger: has served as a consultant for Abbvie, TNI Biotech and Genentech. Barrett G. Levesque: has severed as a speaker for UCB Pharma and Warner Chilcott, and a consultant for Prometheus Laboratories Inc and Nestle Health Sciences and Takeda and Santarus and Abbvie, and is an employee of Robarts Clinical Trials. Declaration of funding interests: None.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".