CONCORD, Mass., April 18, 2018 /PRNewswire/ -- Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling, simulation, and analysis to de-risk drug research and development, today announced a collaboration with Xencor to perform semi-mechanistic PK/PD modeling to analyze Xencor's XmAb® IL15/IL15Rα-Fc program for the treatment of solid tumors. The collaboration will focus on developing a mathematical model relating XmAb®24306 modulated potency and extended half-life to observed pharmacokinetics and in vivo T-cell stimulation. Such models can guide preclinical and clinical development of new biologic agents such as XmAb24306.
"Applied BioMath's modeling approach offers robust analysis of complex biological systems and can leverage the dense data sets of in vitro and in vivo data we have generated in our IL15/IL15Rα-Fc program," said John Desjarlais, CSO of Xencor. "We hope to refine our understanding of the details of how our drug candidates modulate anti-tumor immune responses, so that we can guide XmAb24306 development and future candidate designs to balance anti-tumor activity and tolerability."
Applied BioMath employs a rigorous fit-for-purpose model development process, referred to as Model-Aided Drug Invention (MADI), which aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms. In this collaboration, Applied BioMath will apply MADI to create semi-mechanistic PK/PD models for mouse, cynomolgus monkey, and human species, leveraging in vitro functional and in vivo biomarker and efficacy data from mouse and monkey in concert with exposure data to evaluate PK/PD relationships. These models will be used to:
Understand the PK/PD relationship
Provide a robust prediction of human PK and PD
Help inform new T-cell targeting drug discovery
"Our MADI approach employs proprietary algorithms and software that were designed specifically for mechanistic PK/PD modeling," said John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath. "Because of this mechanistic component, our models produce more accurate results than traditional PK/PD which is crucial to improving lead candidate selection and human PK/PD prediction."
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.