A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models

Area of Science:

• Computational Biology
• Pharmacometrics
• Drug Development

Background:

• In silico clinical trials (ISCTs) are crucial for model-informed drug development (MIDD), optimizing therapies, and personalizing treatment.
• Complex models like quantitative systems pharmacology (QSP) pose implementation challenges.
• Existing ISCT guidelines are difficult to apply to nonlinear mixed effects (NLME) models common in the pharmaceutical industry.

Purpose of the Study:

• To illustrate a practical modeling workflow for conducting ISCTs with NLME models.
• To detail key considerations, methods, and challenges in applying ISCTs to NLME models.
• To demonstrate the workflow's applicability through diverse examples.

Main Methods:

• Developed and detailed a step-by-step modeling workflow for ISCTs incorporating NLME fitting approaches.
• Utilized two distinct case studies: a tumor growth model and a hepatitis B virus QSP model.
• Focused on generating plausible virtual patients and calibrating virtual populations within the NLME framework.

Main Results:

• Successfully demonstrated a practical workflow for ISCTs using NLME models.
• Showcased the workflow's adaptability across different model complexities and therapeutic areas.
• Provided insights into challenges and considerations for implementing ISCTs with NLME models.

Conclusions:

• The proposed workflow facilitates the effective implementation of ISCTs using NLME models, a common industry practice.
• This approach supports optimizing drug development, personalizing treatments, and informing regulatory decisions.
• The demonstrated examples highlight the broad applicability and potential of this ISCT workflow.