Review article: therapeutic targets for the pharmacologic management of coeliac disease—the future beyond a gluten‐free diet
Bibliographic record
Abstract
BACKGROUND: Coeliac disease (CeD) is an immune-mediated small bowel enteropathy resulting from dietary gluten exposure. Presently, the only effective treatment is adoption of a gluten-free diet (GFD), although strict adherence is challenging to maintain, and inadvertent gluten exposures are inevitable for most patients. Hence, there is substantial interest in drug development in CeD and multiple novel therapies are under investigation. AIMS: To review existing and upcoming clinical trial programmes for pharmacologic agents for CeD. METHODS: A narrative review was performed, informed by a search of MEDLINE, Embase, the Cochrane CENTRAL Library and clinicaltrials.gov. RESULTS: We summarise the pathophysiology of CeD and the specific steps that are potentially amenable to pharmacologic treatment. We evaluate the evidence supporting existing and future drug targets, including trials of peptidases, gluten sequestrants, tight junction regulators, anti-transglutaminase 2 therapies, immune tolerizing agents, advanced biologics and small molecules, and microbiome-targeted strategies. We highlight unique considerations for conducting CeD trials, including identifying appropriate study populations, assessing results in the context of a gluten challenge, and interpreting CeD-specific clinical and histologic outcomes. Understanding these factors is crucial for accurately appraising the evidence. Finally, we outline what the future of CeD therapy may hold with the introduction of pharmacotherapies. CONCLUSIONS: There is a need for pharmacologic options for CeD, either used adjunctively with a GFD for accidental or intentional gluten exposures or for refractory disease. Multiple promising agents are in development, and these trials are likely to lead to approvals for the first generation of pharmacologic agents for CeD within the next 5 years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".