Applied BioMath, 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 BYOMass, Inc. for systems pharmacology modeling in chronic diseases. BYOMass is a preclinical stage pharmaceutical company focused on the TGF-ß superfamily.
"We chose Applied BioMath given their proven track record of helping companies identify ideal therapeutic properties and platforms, We hope that this collaboration will help us identify the properties of a lead candidate as efficiently as possible and aid in the design of future studies."
- Margaret Jackson, D.Phil., Founder and CEO of BYOMass
Applied BioMath employs a rigorous fit-for-purpose model development process which quantitatively integrates 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.
About Applied BioMath
"We often work with early-stage companies to help them understand what their therapeutic needs to look like in order to be best in class, We look forward to collaborating with BYOMass and helping them decide next steps for this project."
- John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.
Founded in 2013, Applied BioMath's mission is to revolutionize drug invention. 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 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.