Applying Mechanistic PK/PD Modeling Approaches in Preclinical Research


John M. Burke, PhD, Co-founder, President and CEO at Applied BioMath


In part two of this three-part GENcast series, we dive into the various aspects of the drug discovery and development process and, in particular, how advanced computational techniques like mechanistic modeling can accelerate the therapeutic drug pipeline for developers. Dr. John Burke, President, CEO, and Co-founder of Applied BioMath, delves deeper into the specific applications of mechanistic modeling and how Applied BioMath helps biotech and pharma organizations reduce late-stage drug attrition rates. Dr. Burke answers the following questions:

  • What is mechanistic modeling and why is it important for drug development?
  • Once the level of difficulty in developing a particular drug has been assessed, what comes next?
  • How can partners update an existing model along the pipeline? What does the next iteration of the model look like?
  • How many iterations of the model are there, or can there be?
  • Can the competition factor into these analyses, and how?
  • How does mechanistic modeling help companies prepare for the clinic?
  • Recently, the FDA released Project Optimus, which is a new approach to dose optimization across oncology. How does Applied BioMath help with this goal?

To read a shortened version of this podcast, click here: Transforming Preclinical Research with Mechanistic PK/PD Modeling Approaches.

View our other podcasts in this GENcast series

What is Mechanistic Modeling and How Can it be Applied in Drug Discovery?

Mechanistic Modeling in Clinical Development