Applied BioMath, LLC Announces Collaboration with Northern Biologics

  • Published Month : Wednesday, 22 Apr 2020 12:00 pm

Applied BioMath employs a rigorous fit-for-purpose model development process which aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms

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, today announced a collaboration with Northern Biologics for semi-mechanistic pharmacokinetic and pharmacodynamic (PK/PD) modeling of Northern Biologics' lead preclinical antibody for the treatment of cancer.  Northern Biologics will leverage Applied Biomath's modelling expertise to strengthen their understanding of key biological and preclinical parameters that will help inform selection of clinical starting dose.  "The accurate prediction of appropriate clinical doses based on preclinical data is important and challenging" said Joanne Hulme, VP, Head of Research at Northern Biologics. "We decided to collaborate with Applied BioMath given their proven track record of helping teams accurately translate preclinical PK/PD data to first-in-human studies."

Applied BioMath employs a rigorous fit-for-purpose model development process which aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms.  Their approach provides objective insight to support decision-making throughout discovery, development, and into the clinic enabling project teams to utilize experiments and resources more efficiently. "There are many factors to consider when translating PK/PD from preclinical species to human, such as cell lines of interest, receptors per cell per cell line, the presence of target-mediated drug disposition, etc." said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.  "Our modeling approach incorporates biophysical parameters related to all of these factors which enables us to accurately predict clinical starting doses."

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