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Related Experiment Video

Updated: Sep 11, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

Building virtual patients using simulation-based inference.

Nathalie Paul¹, Venetia Karamitsou², Clemens Giegerich²

¹Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany.

Frontiers in Systems Biology

|August 14, 2025

Summary

This summary is machine-generated.

Simulation-based inference (SBI) generates virtual patients for in silico trials by learning probability distributions from clinical data. This approach captures patient variability and enhances drug development by providing probable alternative virtual patient populations.

Keywords:

QSP modeling artificial intelligence individual patient fitting machine learning simulation-based inference virtual patients

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Area of Science:

Computational Biology and Pharmacology
Machine Learning in Healthcare

Background:

Quantitative Systems Pharmacology (QSP) models simulate pathophysiology and pharmacology for drug development and treatment response prediction.
Generating 'virtual patients' from high-dimensional, sparse, and noisy clinical data for QSP models is a significant challenge.
Clinical variability necessitates robust methods for creating diverse and representative virtual patient populations.

Purpose of the Study:

To investigate the applicability of simulation-based inference (SBI) for virtual patient generation using individual patient data.
To develop and evaluate the novel 'nearest patient fits' (SBI NPF) concept to enhance fitting performance.
To assess the capability of SBI in capturing inter-patient variability in complex diseases like rheumatoid arthritis.

Main Methods:

Applied simulation-based inference (SBI), a probabilistic machine learning technique, to generate virtual patients from clinical data.
Developed and validated the nearest patient fits (SBI NPF) method for improved parameterization.
Utilized rheumatoid arthritis as a case study due to difficulties in predicting treatment response.

Main Results:

SBI approaches effectively captured substantial inter-patient variability in rheumatoid arthritis clinical data.
SBI methods demonstrated competitive performance compared to established fitting techniques.
SBI naturally provides probability distributions for parameterizations, enabling the generation of probable alternative virtual patient populations.

Conclusions:

Simulation-based inference is a viable and powerful approach for generating virtual patients from individual clinical data.
The developed SBI NPF method enhances the accuracy and robustness of virtual patient creation.
SBI-generated virtual patient populations can potentially improve the assessment of drug candidates in silico clinical trials.