Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, today announced their participation in the World Bispecific Summit occurring September 26-28, 2017 in Boston, MA. In addition to sponsoring and exhibiting at the 8th annual summit, Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath, will present a talk titled “Model Aided Drug Invention Case Studies in Research: In silico differentiation for dual targeting PD-1 and Tim-3 in I/O, and predicting optimal drug properties of a bispecific biologic to maximize tissue targeting in OA.”
“Quantitative Systems Pharmacology (QSP) is a mathematical modeling and engineering approach to translational medicine that aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms,” said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath. “We are excited to share two of our case studies that highlight examples of QSP efforts that have de-risked projects, accelerated the discovery and development of best-in-class therapeutics, and impacted critical decisions, in the continuum from preclinical exploration to clinical research.”
Dr. Burke will immediately follow-up his presentation at the World Bispecific Summit with a talk at the Model-informed drug discovery and development using Quantitative Systems Pharmacology modeling (QSP): An industry and regulatory perspective event hosted by the Boston Pharmaceutical and BioScience Society. Additionally, you can find Dr. Burke presenting multiple case studies at the American Conference of Pharmacometrics (AcoP) October 15-18, 2017 in Ft. Lauderdale, Florida. For more information on any of these events, please visit www.appliedbiomath.com/events.
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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