Applied BioMath, the industry-leader in applying mechanistic modeling to drug research and development, announced a collaboration with Obsidian Therapeutics to provide semi-mechanistic models and analysis services. Applied BioMath will create semi-mechanistic models, using in vitro and in vivo preclinical data, to aid in dose prediction and knowledge gap identification for Obsidian’s promising therapeutic strategy. “Our Destablizing Domain platform is exceedingly powerful but also pharmacologically complex,” said Steve Shamah, Senior Vice President of Research, of Obsidian Therapeutics. “We chose to work with Applied BioMath because of their proven track record of delivering impactful results in a very short timeframe.”
Obsidian Therapeutics, founded by Atlas Venture in 2015, is developing next-generation cell and gene therapeutics that employ precise exogenous control of transgenes for improved safety and efficacy. Applied BioMath will leverage their proprietary algorithms and software to develop semi-mechanistic models for Obsidian using rigorous fit-for-purpose model development processes and optimization techniques to estimate model parameters and variability. Applied BioMath employs a hybrid modeling approach whereby parts of the model are mechanistic and some are semi-mechanistic, or statistical. This hybrid approach allows models to link, for example, dose, drug affinity, and half-life to target biology, downstream biological effects, and pharmacodynamics to potential clinical endpoints. “Our models are loyal to the biophysics of the therapeutic mechanism-of-action as well as relevant disease biology,” said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath. “Because of this, Obsidian will be able to leverage these models at this early stage of their platform development, but they will also be able to update these models and gain valuable insight from these models as their platform matures into the clinic.”
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.