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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?
Sheng Guo, PhD, Vice President, Data Science and Bioinformatics at Crown Bioscience
Dr. Sheng Guo leads the Data Science and Bioinformatics team at Crown Bioscience, bringing with him extensive experience from his previous roles at Boehringer Ingelheim Pharmaceutical Inc. and Syngenta.
Dr. Guo’s current research interests are tumor model omics, preclinical statistics, biomarker discovery, NGS product design, AI imaging analysis, and bioinformatics software development. Dr. Guo has authored more than 50 abstracts and manuscripts, with recent major-author papers in Cancer Research, Cancer Research Communications, NAR Genomics and Bioinformatics, and BMC Cancer, amongst others.
Dr. Guo received his PhD in Genomics and Computational Biology from the University of Pennsylvania, and his BSc in Biochemistry from Nankai University.
