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It is widely known that the attrition rate of oncology drugs in the clinical setting can be largely linked to the poor understanding and utilization of animal models during preclinical drug discovery. With failure rates around 95% for new drug candidates moving through clinical trials, it has been found that one of the main contributors to poor preclinical screening is the lack of a robust experimental design.
The need for better efficacy screening and biomarker discovery in the preclinical setting can be addressed by looking at how we use in vivo models and how we design strong mouse clinical trials. The use of Patient-Derived Xenograft (PDX) models has addressed the need for a more translational in vivo model and has proven to be a predictive model for human population heterogeneity. The key now is to design mouse clinical trials which confidently address crucial questions being asked by the drug discovery community: Which cancers are being targeted? Do specific models/targets benefit from treatment over others? And most importantly, are they truly effective?
Dr. Sheng Guo, Senior Director of Bioinformatics at Crown Bioscience, is trained as a computational biologist. After receiving his PhD from the University of Pennsylvania, he worked for Boehringer Ingelheim Pharmaceuticals and Syngenta Biotechnology Inc. In 2011, Sheng joined Crown Bioscience to initiate the company’s bioinformatics function that now covers genomics/efficacy data management and databases, scientific software development, genomics research on PDX and tumor animal models, and statistical methods for in vivo animal studies. Dr. Guo is an expert in the design and analysis of PDX mouse clinical trials (MCTs), the methods he developed leverage on the unique properties of MCTs to make them a great tool for drug efficacy evaluation and biomarker discovery.
