Transforming Evidence Generation for Drug Label Changes: A Case Study.

Area of Science:

• Regulatory Science
• Pharmacokinetics and Pharmacodynamics
• Computational Science

Background:

• Computer Modeling and Simulation (CM&S) offers a pathway to reduce patient burden in clinical trials and enhance regulatory decision-making.
• The US Food and Drug Administration (FDA) initiated the Model-Informed Drug Development (MIDD) pilot program to integrate CM&S into regulatory processes.

Purpose of the Study:

• To support a label change for the initial dose of Afrezza® (insulin human) using CM&S within the FDA's MIDD pilot program.
• To evaluate the utility of a CM&S framework in generating evidence for regulatory submissions.

Main Methods:

• Development of a simulation framework based on empirical data for Afrezza®.
• Submission of the CM&S framework and supporting clinical data to the FDA for validation.
• Engagement with the FDA through two meetings over four months as part of the MIDD pilot program.

Main Results:

• The FDA found the simulation framework insightful and capable of predicting glycemic outcomes.
• The simulation was deemed low-risk for the specific question of interest.
• The FDA accepted the CM&S evidence and requested additional traditional clinical data for the proposed label change.

Conclusions:

• CM&S is a credible method for generating evidence in regulatory decision-making.
• Collaboration among stakeholders, including sponsors and the FDA, is crucial for advancing CM&S in regulatory science.
• The MIDD pilot program demonstrates the potential of CM&S to benefit patients, sponsors, and regulatory agencies.