LINCOLN, Mass., Dec. 12, 2016 /PRNewswire/ -- Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, announced their collaboration with Tusk Therapeutics for quantitative systems pharmacology (QSP) modeling and analysis to enable preclinical candidate selection in immuno-oncology. Applied BioMath leveraged its proprietary mechanistic modeling platform to develop a QSP model of one of Tusk Therapeutics' targets and antibody candidates, then analyzed this model to identify optimal drug properties to aid preclinical candidate selection.
Tusk Therapeutics is harnessing the power of the innate and adaptive immune systems to fight cancer through the development of novel immune modulating therapeutics based on an in-depth understanding of the immune system. Tusk Therapeutics is establishing a diversified pipeline of antibodies against a selection of both novel and validated targets that play an important role in the immune response to cancer.
"Our project with Tusk is a great example of the impact QSP modeling has on drug research and development. By creating a QSP model, we were able to identify optimal drug properties to ultimately enable preclinical drug candidate selection. Performing these analyses in silico prior to planning experiments reduces project timelines and budget."
Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath
About Applied BioMath
Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D. Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward. Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug. We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies.
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SOURCE Applied BioMath