Therapeutic Drug Monitoring in Hidradenitis Suppurativa Patients With Suboptimal Treatment Response to Adalimumab
Bibliographic record
Abstract
Background: Adalimumab is a biologic used in the treatment of hidradenitis suppurativa (HS). Therapeutic drug monitoring (TDM) has emerged as a potential strategy to optimize treatment efficacy, yet its utility in HS remains underexplored. Our aim was to assess the utility of TDM in HS patients with suboptimal adalimumab response by investigating the prevalence of antidrug antibodies and subtherapeutic drug levels. Methods: A cross-sectional study of 62 patients with suboptimal response to adalimumab was conducted at a dermatology clinic in Toronto, Ontario. Data on adalimumab serum trough levels, autoantibody status, and demographics were collected. Patients were divided into therapeutic (≥10.6 μg/mL) and subtherapeutic (<10.6 μg/mL) drug categories based on trough level. Results: Of 51 patients on adalimumab 40 mg weekly, 32 patients (62.7%) had therapeutic drug levels and 19 (37.3%) had subtherapeutic levels. In the 11 patients on adalimumab 80 mg weekly, 7 patients had therapeutic drug levels (28.19 μg/mL) and 4 had subtherapeutic levels (mean 3.26 μg/mL). Autoantibodies were detected in 21.06% of patients with subtherapeutic drug levels on adalimumab 40 mg weekly. There was a significant association between Hurley stage and drug level ( P = .015) in patients on adalimumab 40 mg weekly. Conclusions: In HS patients with suboptimal response to standard adalimumab dosing, a significant number of patients have subtherapeutic drug levels with a minority of those having anti-adalimumab antibodies. TDM can be helpful in identifying HS patients with subtherapeutic drug levels and without antidrug antibodies who could potentially benefit from dose escalation.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".