A deep dive into the clinical and biological effects of dupilumab and tralokinumab in atopic dermatitis
Bibliographic record
Abstract
Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease, and its treatment landscape is rapidly evolving with new targeted therapies. This thesis aims to refine the use of the biological therapies dupilumab and tralokinumab in AD patients trough clinical and translational research, contributing to a more personalized treatment approach. For dupilumab, we focused on the immunological effects of dose tapering and on the response to treatment in patients with different immune endotypes. Our findings highlight a critical tipping point when transitioning dupilumab from 300mg every four weeks to every six weeks, marking the window for potential disease relapse. Additionally, we found that a type 2 dominant endotype is not associated with increased responsiveness to dupilumab treatment. For tralokinumab, we confirmed its efficacy in a real-world AD cohort, showing that it is suitable for both dupilumab-naïve and dupilumab non-naïve patients. We demonstrated that tralokinumab reduces Th2 cytokine production by skin-homing T-cells without increasing Th1/Th17/Th22-activity. Furthermore, this thesis explores the pathomechanisms underlying ocular surface disease (OSD), a side effect of dupilumab and tralokinumab. While OSD is present in all AD patients before starting tralokinumab, those previously treated with dupilumab tend to have more severe OSD, despite frequent ophthalmic medication use. However, during tralokinumab treatment, conjunctival goblet cell numbers remain stable during treatment in both D-naïve and D-non-naïve patients as well as the percentage of mucin-producing cells. By deepening our understandings of these therapies and their immunological effects, this thesis provides valuable insights to optimize AD management and improve patient outcomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".