Physiologically-based Modeling Supports Drug Development Decisions, Regulatory Interactions and Drug Labeling

Today’s powerful and actively evolving computational tools enable sponsors and regulators to understand potential drug characteristics and subject responses earlier in development, with greater certainty. Model-based approaches support timely, confident decisions across the development and regulatory life cycle by gathering disparate sources of information about a drug, its competitors, target disease and patients into a mathematical knowledge framework. That framework outlines the candidate’s risk-benefit profile and quantifies uncertainties at each stage in development.

In model-based drug development (MBDD), scientists apply these models to explore new chemistries, extrapolate from in vitro properties to in vivo behaviors, and understand sources of variability in dose-exposure and dose-exposure-response relationships—making it possible to predict results for unstudied doses, formulations, populations, concomitant medications, and more. Drug sponsors and regulators use these tools to:

  • Speed discovery of safe and effective new compounds
  • Identify nonviable candidates earlier in development
  • Optimize dosing, sampling schemes and trial designs
  • Anticipate drug interactions and subpopulation effects
  • Evaluate—and often avoid—the need for additional trials