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  4. Building virtual patients using simulation-based inference
 
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September 12, 2024
Journal Article
Title

Building virtual patients using simulation-based inference

Abstract
In the context of in silico clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support the decision making for drug candidates and elucidate the (potential) response of patients to existing and novel treatments. These models are built on disease mechanisms and then parametrized using (clinical study) data. Clinical variability among patients is represented by alternative model parameterizations, called virtual patients. Despite the complexity of disease modeling itself, using individual patient data to build these virtual patients is particularly challenging given the high-dimensional, potentially sparse and noisy clinical trial data. In this work, we investigate the applicability of simulation-based inference (SBI), an advanced probabilistic machine learning approach, for virtual patient generation from individual patient data and we develop and evaluate the concept of nearest patient fits (SBI NPF), which further enhances the fitting performance. At the example of rheumatoid arthritis where prediction of treatment response is notoriously difficult, our experiments demonstrate that the SBI approaches can capture large inter-patient variability in clinical data and can compete with standard fitting methods in the field. Moreover, since SBI learns a probability distribution over the virtual patient parametrization, it naturally provides the probability for alternative parametrizations. The learned distributions allow us to generate highly probable alternative virtual patient populations for rheumatoid arthritis, which could potentially enhance the assessment of drug candidates if used for in silico trials.
Author(s)
Paul, Nathalie
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Karamitsou, Venetia
Sanofi (Germany)
Giegerich, Clemens
Sanofi (Germany)
Sadeghi, Afshin  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Lücke, Moritz
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Wagenhuber, Britta
Sanofi (Germany)
Kister, Alexander  
Federal Institute For Materials Research and Testing
Rehberg, Markus
Sanofi (Germany)
Journal
Frontiers in Systems Biology  
Open Access
File(s)
Download (2.03 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.3389/fsysb.2024.1444912
10.24406/publica-4103
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • virtual patients

  • QSP modeling

  • individual patient fitting

  • machine learning

  • artificial intelligence

  • simulation-based inference

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