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