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  5. Raj Madabushi, Ph.D. - Transcript
  1. Regulatory Science in Action

Raj Madabushi, Ph.D. - Transcript

Applying different approaches to improve the drug development process

Dr. Madabushi: I’ve been here close to around 11 years. I cannot think of another place to go and work. Given the variety of problems I get to see, the different types of solutions that people apply and different disciplines, how they come together, it’s just like a team sport.

Text Slide:

Raj Madabushi, Ph.D.

Team Leader

Guidance and Policy Team

Office of Translational Sciences

Office of Clinical Pharmacology

Dr. Madabushi: I see almost on a daily level the Office of Clinical Pharmacology routinely applying model-informed drug development approaches not only in trying to better understand the diseases and provide solutions for them, but also to help the drug development programs, as well as inform the drug labeling for getting the right dose for the right patient at the right time.

Model-informed drug development is an approach, which uses mathematical modeling of data from drugs, especially the information on efficacy and safety from studies in animals as well as humans to improve the drug development and decision making.

We all know that while drugs are developed generally with one dose for all. So, the effect of the body on how it processes the drugs and thereby resulting in differential drug levels can be modeled and we can then provide dosing instructions for these different subpopulations.

One of the exciting areas in the Office of Clinical Pharmacology are pediatric drug development programs.

Even in children as they’re growing, there is maturation and organs are developing. Mathematical models account for these to try to help inform what might be the right dose for children and different age groups of children.

Information is in the form of numbers. That’s our understanding, and how can we leverage and add them as layers from one stage to another stage. So that we can provide contemporary guidances with the evolving knowledge that comes from the application of all this information so that we can have guidances that are most proximately related to all the learnings and applications that we are having, so that these become routine as we go forward and as we are encountering newer challenges and newer solutions.

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