Three Questions with Mucosal Healing Project Team Members

In April 2021, the FNIH launched the “Mucosal Healing for Ulcerative Colitis” Project—a three-year-long initiative that aims to generate best practices and standards for assessing histologic disease activity in Ulcerative Colitis (UC) clinical trials. The program also hopes to establish machine learning methodology as a validated objective method for scoring of mucosal healing in clinical trials. The project team developed these goals to address the heterogeneity of biopsy collection for UC patients and in response to newly issued FDA draft guidance, which encouraged mucosal healing, rather than clinical remission, as the primary treatment objective due to its link to better long-term health outcomes. The project’s success will help industry stakeholders develop more effective UC drugs and lead to better treatment decisions that are less invasive for patients with this severe and chronic disease.

Read what the project team members say about the unmet need that led to this project and the benefits it presents for patients diagnosed with UC. 

1. What do you believe is the most important contribution this project will make to this disease area, and how might it benefit clinician treatment decisions and industry drug development?

This project will contribute toward developing a reliable and reproducible measurement of mucosal healing to guide appropriate treatment with approved therapies but also can be used as a tool in the development of new therapies for ulcerative colitis patients. There has been a shift in the approach to the treatment of UC patients toward achieving mucosal healing rather than clinical remission alone based on signs and symptoms being a primary objective. Histological assessment for mucosal healing has become increasingly important not only for drug development but also can be an important tool in clinical practice. While histology is routinely done in UC clinical trials, there is currently no standardized criteria for biopsy collection and histological measurement of mucosal healing. Furthermore, automated evaluation of mucosal inflammation using an AI approach will potentially be more precise, faster, may reduce the number of biopsies needed and the timeframe for having the results available.

2. How does the consortium approach provide advantages in obtaining and generating best practices for disease activity assessment for UC that could not be achieved by a single stakeholder?

Significant resources and expertise are needed for regulatory qualification of a biomarker for a given context of use. Besides use in clinical trials and clinical practice, the results of the current project will be shared with the FDA to potentially impact guidance to industry for upcoming UC clinical trials with new and approved therapies. Collaborative efforts such as this Consortia are critical for such endeavors as they provide a framework to facilitate sharing of data, scientific knowledge and expertise. This concerted effort will generate a common protocol and a white paper for submission to the FDA to drive regulatory guidance for industry around conducting clinical trials in UC.

3. What do you see as the immediate implications of a validated machine learning methodology for scoring mucosal healing in clinical trials and in hospital settings?

There is growing interest to use an automated approach to improve the ability to predict mucosal healing in patients with UC undergoing treatment with new and approved therapies. A fully automated, reliable and consistent grading system to monitor inflammation and mucosal healing could help guide appropriate treatment to reduce/prevent relapses including complications. Furthermore, an AI based approach is not critically dependent on the expertise of endoscopists and pathologists.