PharmaNest to Present Six Digital Pathology Abstracts at the International Liver Congress-EASL 2023, Including Liver Related Clinical Outcomes Prediction Results in NASH

PharmaNest, a leader in high resolution, single-fiber, quantitative Digital Pathology and Artificial Intelligence, showcases the utility and performance of its FibroNest Ph-FCS continuous fibrosis biomarker for the quantification of the severity of fibrosis and prognostics of liver related clinical events in NASH.

FibroNest High Resolution NASH Digital Pathology

PharmaNest Inc, a leader in high resolution quantitative Digital Pathology and Artificial Intelligence, announces today that it will present, alongside with its collaborators, six abstracts at this year's International Liver Congress - EASL in Vienna, Austria, June 21-24, 2023. The abstracts highlight the utility and performance of its FibroNest Ph-FCS continuous fibrosis digital pathology biomarker for the quantification of the severity of fibrosis and prognostics of liver related clinical events in patents with NASH.

FibroNest is the first multivendor, high resolution and single fiber digital Pathology quantitative image analysis platform for the assessment of the severity and progression of Fibrosis and disease activity in NASH. Presentations also include results from the application of the FibroNest method in anti-fibrotic compound screening studies and to evaluate anti-fibrotic and anti-steatotic candidates in rodent models. 

"The automated high-resolution quantification of the phenotype of fibrosis severity from the same slides reviewed by pathologists offers a robust and scalable method to generate continuous scores that resolve faint changes in fibrosis severity, and can predict liver related clinical events," said Mathieu Petitjean, Ph.D., CEO and founder of PharmaNest. Because the approach is fully quantitative and is not based on existing staging or grading paradigms, it can be used along the drug discovery and development process, and across etiologies of fibrosis.

The first abstract showcases the prognostic performance of the FibroNest Ph-FCS to predicts the occurrence of liver-related clinical events from the retrospective analysis of liver biopsies digital images in the multicentric, European, Hepatic OuTcomes and SURvival Fatty Liver Registry (HOTSURFR) study (in partnership with Pr. Vlad Ratziu and the European HOTSURF Research team).

 A second abstract discusses the correspondence of the FibroNest digital pathology fibrosis scores with the Laennec and Beijing histological stages in the context of the evaluation of cirrhotic patients (in partnership with the Icahn School of Medicine at Mount Sinai, New York, USA).

A third abstract explores the differences of the histological phenotype of liver fibrosis in pre- and post-menopausal women with NASH and moderate to severe fibrosis (in collaboration with Pr. Manuel Romero Gomez and the SeLIVER Group, Seville, Spain).

Another abstract presented in collaboration with Pr. Arun Sanyal (Virginia Commonwealth University) and an esteemed team of pathologists from the Universities of California demonstrates how the high resolution and quantitative digital pathology methods developed by PharmaNest generate cell tissue panels that augment the quantification of steatosis and inflammation in the context of fibrotic and NASH conditions.

Finally, several abstracts highlight the value of FibroNest to quantify fibrosis in the context of in-vitro assays for mid-throughput screening of antifibrotic compounds (in partnership with InSphero AG) and the validation for lead NASH treatment candidate in rodent models (in collaboration Netherlands Organization for Applied Scientific Research).

These abstracts will be available on the PharmaNest website at the end of the week at fibronest.com/science.

Source: PHARMANEST INC

About PHARMANEST

PharmaNest is an Artificial Intelligence and Digital Pathology company focused on the development and validation of novel standards for the quantification of the histological phenotypes Fibrosis at its associated features

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