Biologic Therapies Against Inflammatory Bowel Disease: A Dysregulated Immune System and the Cross Talk with Gastrointestinal Mucosa Hold the Key
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a GI tract disorder that manifests as either Ulcerative colitis (UC) or Crohns disease (CD). The precise etiology of IBD is still not completely elucidated but research into the immunopathogenesis of IBD suggests that dysfunctions of the intestinal immune system and cross-reactivity against host epithelial cells hold the key. In both UC and CD, polarized immune activity towards Th1 (marked by upregulation of TNF-α, IL-1β, IFN-γ, IL-6) and Th17 (marked by IL-17 secretion) response is reported, while UC appears to exhibit an added contribution of Th2 responses (characterized by secretion of IL-4, IL-5, and IL-13). Additionally, other molecules involved in leukocyte trafficking (adhesion molecules), chemokines (IL-8) and tissue repair molecules (PGE2 and its receptors) are also crucial. Emergence of these new paradigms in the pathogenesis of IBD led to a recent trend of novel biological therapies that specifically inhibit molecules involved in the inflammatory cascade. In this review, we critically discuss recent advances in the pathogenesis of IBD, drug therapies (conventional versus biologic), drug efficacy and pharmacokinetics (murine versus human versus chimeric) and their adverse effects. We also discuss emerging novel biological therapies targeting pro-inflammatory cytokines including TNF-α and IFN-γ, cytokine receptors and those targeting adhesion molecules-anti-integrin and anti-ICAM antibodies. Other potential approaches using anti-inflammatory cytokines (IL-10), anti-sense oligonucleotide and probiotics are also discussed. Finally, we summarized few imperative targets whose more detailed exploration can help to pave the way for an efficacious IBD therapy. Keywords: IBD, Biological therapy, Conventional therapy, Infliximab, Probiotics, EP receptor
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".