Applied BioMath (, the industry-leader in applying mechanistic modeling, simulation, and analysis to accelerate and de-risk drug research and development, today announced their upcoming webinar titled "QSP Approaches to Determine Best in Class Properties for Targeted Anabolic Growth Factor to Arthritic Joints" is airing live Thursday, November 8, 2018 at 2p.m. ET / 11a.m. PT.

In this webinar, Raj Kamath, PhD, Project Director, AbbVie, will present a QSP model of targeted anabolic growth factors for intra-articular injection for the treatment of diseased joints which was created during a collaboration between Applied BioMath and AbbVie. The QSP model was used to systematically map out drug-and target-parameter space to identify tradeoffs between target and drug properties and to identify key missing data, as well as, guide drug development and discovery of disease-retaining bispecific antibodies for Osteoarthritis.

As part of this collaboration, species specific models of rat, dog, and human were developed to enable cross species translation and integration of preclinical data. The model simulations and analysis identified the efficacy receptor turnover rate as a key parameter for establishing dose feasibility. This differentiated between some potential targets in cases where high receptor turnover rate may make dosing a challenge. The model also enabled the identification of the requirements for the targeting epitope and targeting arm affinity to maximize therapeutic window and tissue targeting.

This webinar is ideal for scientists and decision makers in R&D who want to learn more about how to leverage QSP to provide quantitative guidance for their drug discovery and development.

To register, visit For more information about Applied BioMath's upcoming events, visit

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

Applied BioMath and the Applied BioMath logo are registered trademarks of Applied BioMath, LLC.

Press contact:
Kristen Zannella