We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement

twoXAR, Stanford Collaboration

Listen with
Speechify
0:00
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

twoXAR, Inc. has announced a collaboration with the Asian Liver Center at Stanford University School of Medicine to support research focused on the identification of drug candidates targeting hepatocellular carcinoma (HCC). As part of this collaboration, twoXAR will make disease-to-candidate predictions using the company’s software-driven discovery platform. These candidates will be validated through preclinical studies by researchers at the Asian Liver Center, under the direction of Mei-Sze Chua, PhD, Senior Scientist in the laboratory of Samuel So, MD, FACS.

HCC is a primary malignancy of the liver and occurs predominantly in patients with underlying chronic liver disease, often caused by hepatitis B or C virus infection. HCC is generally refractory to chemotherapy, and only one targeted treatment, the tyrosine kinase inhibitor sorafenib, is indicated for HCC. However, sorafenib has been shown to marginally extend survival and can elicit severe side effects. 

“New drugs in development for HCC primarily target tyrosine kinases, but they have demonstrated mixed success in clinical trials, suggesting a need for new therapies targeting a more diverse set of biomarkers,” said Andrew A. Radin, co-founder and CEO of twoXAR. “We are very pleased to be working with Dr. So and his colleagues at the Asian Liver Center who are dedicated to improving outcomes for patients and raising awareness of chronic hepatitis B infection and its connection to liver cancer through efforts such as the JOINJADE initiative.” 

twoXAR has developed patent‐pending algorithms that enable it to find unanticipated associations between disease and drug candidates orders of magnitudes faster than wet lab‐based approaches. The company’s integrative biomedical software platform rapidly evaluates massive public and proprietary datasets to identify and rank high probability disease‐to‐candidate matches. These matches can then be used to prioritize existing candidates, perform targeted searches and identify novel drug candidates for further preclinical and clinical testing. The platform is disease agnostic and has been tested on more than 60 conditions to date in therapeutic areas including autoimmune, oncologic, and neurologic disorders.