LINCOLN, Mass., Oct. 16, 2017 /PRNewswire/ -- 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.
Press contact:
Kristen Zannella
kristen.zannella@appliedbiomath.com
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SOURCE Applied BioMath