S4-E3.2 - NASH-TAG Review: NITs, Clinical Trial Design and Addressing “Indeterminate Zones”

Send us Fan MailNASH-TAG 2023 proved to be a watershed moment for Fatty Liver disease as exciting drug development readouts, powerful academic work on non-invasive tests and the willingness to dive into the toughest questions aligned in Deer Valley, Utah. In this weekend’s conversation series, Surfing NASH reviews its diverse coverage of the conference by showcasing key excerpts across six recordings with various KOLs, patient advocates and stakeholders.This conversation featuring Stephen Harrison, Mazen Noureddin and Jörn Schattenberg begins with Mazen describing how exciting and informative the new sessions on non-invasive tests were. Specifically, he details his own surprise at how much LITMUS and NIMBLE were able to share. Jörn’s main point: we will find more NITs, but do not need them to be able to push treatment far forward today. Roger Green suggests that correct classification metrics (based on the percentage of patients classified correctly) might be more important in practical treatment than other tests which have better positive or negative predictive values but have large “indeterminate zones” of patients the test does not predict for at all. Stephen compliments the NIT discussion and commends Mazen’s observation that NASH is not the only disease area working its way through predictive non-invasive tests. Mazen shifts discussion onto the next session, where Vlad Ratziu, Rohit Loomba and Stephen Harrison investigate important areas in clinical trial design. Vlad discussed combination therapies, Rohit discussed gene SNPs and Stephen discussed cirrhosis.Stephen refocuses on the idea that by providing continuous scoring, NITs appear to provide better guidance on whether drugs are “working” or not. Mazen agrees, pointing out how high the bar is to label a patient treated with drug a success. As the talk winds down, Jörn notes that Intercept and Madrigal are providing data we can dive into for years. Lastly, Stephen describes the high value that AI-based digital pathology will provide in sorting out how many patients are improving using continuous measures.