Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, today announced a collaboration with Sanofi to develop quantitative systems pharmacology (QSP) models in immuno-oncology. Applied BioMath will leverage its proprietary modeling platform to develop QSP pharmacokinetic (PK) and pharmacodynamics (PD) models for multiple therapeutics in both animal and human species. "We chose to work with Applied BioMath because of their significant expertise and strength in developing mechanistic QSP models in oncology," said Karim Azer, Senior Director and head of Systems Pharmacology at Sanofi US. "QSP modeling is a valuable tool that enables project teams to make strategic R&D decisions on candidate molecules in development and evaluate mechanistic synergies that can arise with carefully thought out drug combinations."
Applied BioMath leverages mathematics and high-performance computing which enables massive scale simulations in a very short time. Its proprietary software platform, co-developed by Dr. Josh Apgar, Co-Founder and CSO at Applied BioMath, while at MIT, has been further developed by Applied BioMath to produce faster, more accurate results than other modeling platforms. "Our modeling platform was designed specifically for biological modeling which allows us to avoid shortcuts commonly used in other software platforms," said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath. "Because of this, our predictive analytics are 100x faster than industry averages and our results are 10x more accurate in predicting drug properties more than 3 years before being validated in the clinic."
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.
Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.