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

Javiera Cortés-Ríos1, Mindy Magee2, Anna Sher3

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This study presents a new workflow for in silico clinical trials (ISCTs) using nonlinear mixed effects (NLME) models. This approach enhances drug development by simulating virtual patient responses to novel therapies.

Keywords:
in silico (virtual) clinical trialsnonlinear mixed effectsquantitative systems pharmacologyvirtual patientsvirtual population

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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.