On June 23, 2020 Applied BioMath (www.appliedbiomath.com), the industry-leader in applying systems pharmacology and mechanistic modeling, simulation, and analysis to de-risk drug research and development, reported their participation at the Third Annual Next-Gen Immuno-oncology Congress occurring virtually June 25-26, 2020 (Press release, Applied BioMath, JUN 23, 2020, View Source [SID1234561418]).

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John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath, will present "Targeting Bispecific Antibodies and Developing Systems Disease Models in Immuno-oncology" Thursday, June 25th at 12:55 p.m. US EST. In this presentation, Dr. Burke will highlight mathematical modeling efforts that have de-risked projects, accelerated the discovery and development of best-in-class therapeutics, and impacted critical decisions or provided deep biological insights in the continuum from preclinical exploration to clinical research. Examples will include: T cell engager at the new target through lead generation stage, GITR-mediated T cell dynamics, species translation to make human predictions, and mechanistic predictions in checkpoint inhibitors.

"We are excited to present at the Next-Gen Immuno-Oncology Congress for the first time this year," said John Burke, PhD, Co-Founder, President and CEO of Applied BioMath. "This presentation establishes how developing and interrogating a systems pharmacology model earlier in the project helps project teams make informed decisions that can provide better mechanistic prediction into humans and help identify the winners, screen for the best candidates possible, and accelerate the winner into the clinic."

Applied BioMath employs a rigorous fit-for-purpose model development process which quantitatively integrates knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms. Their approach employs proprietary algorithms and software that were designed specifically for systems pharmacology model development, simulation, and analysis