Applied BioMath (, the industry-leader in applying systems pharmacology and mechanistic modeling, simulation, and analysis to de-risk drug research and development, today announced they have been awarded a grant for the development of a software platform called Antibody Drug Conjugate (ADC) Workbench to facilitate efficient knowledge discovery and enable rapid knowledge qualification in support of the development of Quantitative Systems Pharmacology Models (QSPM) for ADC projects.

ADCs are a promising class of anti-cancer therapeutics, with proven clinical benefit in a range of cancer types. However, the design of such molecules is highly complex. The pre-clinical experimental models and unexpected toxicities often cause programs to fail in preclinical and clinical testing. The ADC Workbench will integrate Applied BioMath's mechanistic understanding of ADC pharmacokinetics (PK) and pharmacodynamics (PD) with their emerging understanding of ADC toxicodynamics (TD) particularly for hematological toxicities (e.g., neutropenia and thrombocytopenia), to help partners quantify risk, develop better ADCs, and reduce late stage attrition rates. "The value of this work is that for the first time there will be a consolidated modeling platform for ADCs that integrates efficacy and toxicity models to predict efficacious dose and therapeutic index," said Alison Betts, Senior Director of Scientific Collaborations and Fellow of Modeling and Simulation at Applied BioMath and the principal investigator on the grant. "The ADC workbench will be a highly valuable tool to guide ADC design and clinical decision making to facilitate success for this exciting class of molecules." Combining the model and parameter database with the powerful high-performance computing (HPC) analysis tools of Applied BioMath's cloud-based simulation engine will allow for routine and timely contribution to the ADC drug discovery process.

"Ultimately, we envision that the QSP ADC platform model, and the computational analysis and services that it will enable, will significantly reduce the cost and time to develop novel and better ADC therapeutics for cancer and to target the indications and patient populations most likely to benefit," said John Burke, PhD, Co-Founder, President and CEO of Applied BioMath. "If successful, such a model could accelerate the development of best in class ADCs by identifying critical properties to maximize therapeutic index and maintain high efficacy at Lead Generation through Clinical Candidate selection ultimately saving hundreds of millions of dollars for drug development, impacting late stage attrition rates, and helping improve cancer patients' lives."

The grant referenced in this press release is supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R44GM134790. The content is solely the responsibility of Applied BioMath, LLC and does not necessarily represent the official views of the National Institutes of Health.

About 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. For more information about Applied BioMath and its services, visit

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