Applied BioMath, the industry-leader in applying systems pharmacology and mechanistic modelling, simulation, and analysis to de-risk drug research and development, today announced a collaboration with Cullinan Oncology. Applied BioMath will develop a systems pharmacology model for CLN-049, a novel FLT3XCD3 bispecific antibody for Acute Myeloid Leukemia, being developed by Cullinan Florentine, a Cullinan Oncology company.

This model will be used to determine first in human starting dose and to predict the efficacious dose range in the clinic.

"We look forward to leveraging the proven modelling approaches of Applied BioMath in our development project," said Jennifer Michaelson, Chief Development Officer, Biologics at Cullinan Oncology. "We see significant potential for Applied BioMath's modelling efforts to augment our program, from predicting starting dose and efficacious dose ranges and informing optimal dose regimens in the clinic, to helping us design efficacy studies in mice and tolerability studies in non-human primates."

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 employs proprietary algorithms and software that were designed specifically for systems pharmacology model development, simulation, and analysis. "Our mechanistic approach to systems pharmacology modelling is the most accurate way to translate across species and prepare for first in human doses," said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath. "We look forward to leveraging our modelling expertise to help Cullinan Oncology advance their therapeutic to the clinic

Founded in 2013, Applied BioMath's mission is to revolutionize drug invention. Applied BioMath uses mathematical modelling and simulation to provide quantitative and predictive guidance to biotechnology and pharmaceutical companies to help accelerate and de-risk drug research and development. Their 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 modelling 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.