A preclinical multi-modal system for dynamic noninvasive assessment of liver disease

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Abstract Chronic liver disease (CLD) affects millions of people in America every year, and annually kills tens of thousands of these patients. Despite very active research efforts, there still have been no specific antifibrotic drugs approved by the FDA. The liver disease and drug development communities do not currently have well- validated, low-cost, easy-to-use, noninvasive tools for studying the progression of liver fibrosis, steatosis, and inflammation in preclinical rodent models making it challenging to execute high-quality longitudinal studies. To address this need, SonoVol Inc. proposes to develop a novel multi-modal benchtop imaging system capable of providing rapid, noninvasive measurements of liver disease in rodents. The platform will utilize four core imaging technologies: quantitative ultrasound (QUS), shear wave elasticity imaging (SWEI), acoustic angiography (AA), and targeted molecular imaging (MI). While some preclinical products implement the abovementioned technologies, all of them follow the conventional ultrasound paradigm of data collection – a trained sonographer uses a handheld probe and manually selects regions of interest to interrogate. Making consistent and accurate measurements requires in-depth knowledge of probe placement and sonographic technique, which academic biologists or drug researchers are unlikely to possess. Therefore, SonoVol will develop a device capable of fully-automated, non-contact imaging that eliminates the need for a trained sonographer and will enable large scale adoption of these powerful technologies in the preclinical liver diseases research community. To validate the new system, two different animal models for hepatic injury will be evaluated, and the results compared to conventional postmortem assessments of liver disease. This technology represents an innovative combination of a widefield 3D robotic ultrasound imaging system and noninvasive SWEI, QUS, and contrast-enhanced imaging. Furthermore, the technology can be applied in the future to many other diseases, including cancer or cardiac models, increasing the potential market and impact on the field.