Review: Anti-adhesion molecule therapy for inflammatory bowel disease
Bibliographic record
Abstract
Although biologic agents directed against tumor necrosis factor alpha (TNFα) continue to be an effective therapeutic strategy for patients with inflammatory bowel disease (IBD), approximately 30% of patients with Crohn's disease (CD) who are refractory to standard treatment do not respond to induction therapy with TNFα inhibitors and, of those who initially respond, 50% or more cease to respond within a year. Moreover, their use can be associated with significant safety issues. Clearly, there is a need to target alternative pathways involved in the inflammatory process. IBD is driven by the trafficking of lymphocytes from the circulation into the gut tissue that is mediated by adhesive interactions between the lymphocytes and endothelial cells. The adhesion molecules involved represent attractive targets for the development of new therapeutics which should aid in the resolution of existing inflammation, prevent recurrence of inflammation, and may potentially lead to long-term control of disease. In this article we review current opportunities and challenges facing anti-adhesion therapy in IBD, and discusses recent clinical development efforts that have focused on having an impact on two particular adhesive interactions: α(4)-integrin/MAdCAM-1 and β(2)-integrin/ICAM-1. Of particular interest is natalizumab, a humanized monoclonal antibody against human α(4) integrin that is approved for the treatment of patients with moderately-to-severely active CD and evidence of active inflammation. This agent represents an efficacious therapeutic option for patients who do not respond to, or have failed, a TNF-α inhibitor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".