Three Questions with Dr. Gerard Honig on Mucosal Healing Project

June 24, 2021 — This past April, 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these goals to address the heterogeneity of biopsy collection for UC patients and in response to newly issued FDA draft guidance, which encouraged mucosal healing, rather than clinical remission, as the primary treatment objective due to its link to better long-term health outcomes.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 co-chair Gerard Honig, Director of Research Innovation, Crohn’s & Colitis Foundation, says about the unmet need that led to this project and the benefits it presents for patients diagnosed with UC.

Three Questions with MH Project Co-Chair Dr. Gerard Honig

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?

Ulcerative colitis (UC) is a chronic inflammatory disease that progresses over years. Inflammation of the gastrointestinal tract, the hallmark of UC pathology, causes acute symptoms such as severe pain and bloody diarrhea, but also causes long-term damage to the gut. Over time, such damage can become irreversible, requiring aggressive surgery such as colectomy. While alleviating symptoms is an important goal of therapy in UC, it has become clear that this is insufficient to achieve improved long-term outcomes; a treat-to-target approach, in which the goal of therapy is not just alleviation of symptoms but also healing of the gut wall, i.e. mucosal healing, can help patients achieve durable remission. Unfortunately, there is not a consensus regarding how mucosal healing should best be assessed in clinical trials. This increases uncertainty for clinical trials and makes it much more difficult to validate approaches to monitor response to therapy in clinical practice. The consortium will address this issue by developing a validated approach to assess mucosal healing in UC trials, which could also be implemented in clinical practice.

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?

The UC community is fortunate in that there has been a great deal of investment in the development of new therapies, including many clinical trials. The data and samples from these historical trials, in aggregate, represent an incredible resource to address barriers in the field, such as the one that this consortium is focused on. It’s important to recognize that each and every UC patient who chooses to participate in a clinical trial is making a serious commitment in order to help advance care for future patients. Clinicians and industry also commit incredible time and resources to making these trials happen. The UC research community should honor these incredible contributions by making the most of the data and samples from past trials. While this has been discussed for years, it takes a coalition including voices from industry, patient advocacy, nonprofits and academia to make this a reality, and it takes an organization like FNIH to bring these parties together in the spirit of cooperation to advance their shared mission of transforming life for UC patients.

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?

One of the challenges with the current methods of assessing mucosal healing is that they critically rely on highly trained and specialized clinicians, such as pathologists and endoscopists, who need to ‘read’ and score each endoscopy and pathology slide based on years of experience. While the expert opinion of the trained clinician observer will always be incredibly important, it’s also important to recognize that not all patients and clinical centers have adequate access to such support, for example in the developing world where UC incidence is increasing. In addition, dependance on highly trained clinicians to score mucosal healing increases the expense and time needed to perform UC trials and can even deter advancement of innovative investigational therapies. I hope and expect that the machine learning tools developed to automate assessment of mucosal healing will incentivize companies to launch new and innovative clinical trials of potentially transformative therapies for U C, and eventually, will help patients and clinicians consistently receive consistent and accurate information about disease status, whether at an academic medical center in New York or a community clinic in Africa.

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