S3-E41.2 - Improving NASH Clinical Trials By Reducing Screen Fail Rates

Send us Fan MailTHE NASH Tsunami audience came to know Jörn Schattenberg in the Fall of 2020 when he shared a paper he had recently co-authored on why NASH drug trials failed. This week, the same group that discussed that paper – Jörn, Stephen Harrison, Louise Campbell and Roger Green – reflect on what has improved in the intervening time period and what has not. The group suggest that while non-invasive techniques (NITs) have made a significant contribution to patient enrolment, there remains reliant on additional screening methods.This conversation opens with Jörn's observation that NITs are not always part of the protocol when assessing patients for their inclusion or exclusion in clinical trials. However, he states, experienced researchers will rely on these tools along with presence or absence of other metabolic diseases to decide which patients should not progress to biopsy due to high likelihood of screening out of the study anyway. Stephen concurs, adding that by considering these other risk factors, the clinical trials benefit from a more enriched patient population. and fewer unnecessary biopsies. From here, Stephen goes on to discuss two other pivotal issues: advance in strategies for reading biopsies and the impact of high screen fail rates on staff morale. On the first point, he states that good drugs have been lost in development because the strategy for reading biopsies, which relied on a single pathologist to read, created interpretative barriers that led to trials failing or being discontinued. This has led to a place today where all trial design incorporate consensus histopathology reads. On the second, he contrast staff performance for two studies on the same drug: an obesity study with a 25% screen fail rate and a NASH study with an 80% rate. He describes the palpable difference how staff feels about working on each of these projects. As the conversation ends, Louise comments on changing FibroScan thresholds for admission to some trials and Stephen describes how inclusion thresholds might vary from trial to trial and ways he uses other NITs to complete his assessments.