A practical guide for the generation of model-based virtual clinical trials

Area of Science:

• Pharmacometrics and computational biology.
• Application of mathematical modeling in drug development.

Background:

• Mathematical modeling is crucial for drug design, development, and optimization.
• Virtual clinical trials are gaining traction for exploring patient heterogeneity.

Purpose of the Study:

• To provide best practices for creating virtual patients from mathematical models.
• To guide the implementation and execution of virtual clinical trials.
• To promote the use of virtual population-based analysis.

Main Methods:

• Discussing and providing examples of model design.
• Detailing parameter estimation and sensitivity analysis.
• Explaining model identifiability and virtual patient cohort creation.

Main Results:

• A practical framework for building virtual patient cohorts.
• Methodologies for ensuring model robustness and reliability.
• Demonstration of applying these methods in virtual clinical trial contexts.

Conclusions:

• Researchers can adopt these best practices for robust virtual clinical trials.
• Enhanced virtual population-based analysis can accelerate drug development.
• This approach facilitates a deeper understanding of therapeutic responses in diverse populations.