Non-alcoholic steatohepatitis (NASH) remains a challenge for the gastroenterologist as results from clinical trials have been dismal. There is now growing appreciation that hard endpoints such as steatosis, inflammation and scarring are not necessarily driven by a single mechanism across animal models and in patients but rather via disease-driving networks comprising genes and gene clusters. Identification of cross-species i.e. animal-human disease-driving networks and development of drugs that engage and neutralize these networks might translate to clinical success in at least a subset of NASH patients sharing that network. Furthermore, identification of potential non-reponders can spare that subset of any adverse drug effects. Presented herein is an exercise that identifiies a common and early disease-driving network in NASH and applies machine learning to repurpose existing drugs or identify novel chemical entities that target that network.

Disease drivers, drug discovery & drug positioning in NASH: Smart medicine outsmarts disease

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.