Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling, simulation, and analysis to accelerate and de-risk drug research and development, today announced their participation at the American Association for Cancer Research Annual Meeting occurring March 29 – April 3, 2019 in Atlanta, GA.
During the conference Applied BioMath will present their science through the four posters listed below.
- Sunday, March 31 | 1:00-5:00p.m. | Poster #678 | "Drug-induced TMDD: a novel class of PK models relevant to Immune-stimulating therapies"
- Sunday, March 31 | 1:00-5:00p.m. | Poster #684 | "A semi-mechanistic platform model to capture individual animal responses to checkpoint inhibitors in a syngeneic mouse model"
- Sunday, March 31 | 1:00-5:00p.m. | Poster #685 | "Development of a multiscale QSP model to characterize the tumor suppressive effects of a cytokine mRNA immunotherapy in a preclinical melanoma mouse model"
- Tuesday, April 2 | 1:00-5:00p.m. | Poster #4126 | "Inferring target occupancy from fitting nonlinear-PK data with mechanistic PKRO model for Pembrolizumab"
"We are thrilled to showcase the work we completed internally, as well as, in collaboration with our partners," said John Burke, PhD, Co-Founder, President and CEO, Applied BioMath. "Our posters demonstrate the value that Model-Aided Drug Invention (MADI) brings to various projects and we're excited to share this science with the attendees at the AACR Annual Meeting."
In addition to their posters, Applied BioMath has a booth throughout the conference. For more information about Applied BioMath's presence at the AACR Annual Meeting, visit https://www.appliedbiomath.com/news-resources/events/aacr-annual-meeting-2019.
For more information about Applied BioMath's upcoming events, visit www.appliedbiomath.com/news-resources/events.
About Applied BioMath
Founded in 2013, Applied BioMath uses mathematical modeling and simulation to provide quantitative and predictive guidance to biotechnology and pharmaceutical companies to help accelerate and de-risk drug research and development. Their Model-Aided Drug Invention (MADI) approach employs proprietary algorithms and software to support groups worldwide in decision-making from early research through clinical trials. The Applied BioMath team leverages their decades of expertise in biology, mathematical modeling and analysis, high-performance computing, and industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, patients, and the best path forward into and in the clinic. For more information about Applied BioMath and its services, visit www.appliedbiomath.com.
Applied BioMath and the Applied BioMath logo are registered trademarks of Applied BioMath, LLC.