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Immune digital twins for complex human pathologies: applications, limitations, and challenges.

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Digital twins are computational models for precision health. Immune digital twins integrate complex immune system data for personalized medicine and drug discovery, overcoming current challenges.

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Area of Science:

  • Computational biology
  • Immunology
  • Precision medicine

Background:

  • Digital twins are computational models for precision health, representing individual patient states over time.
  • Integrating the immune system is crucial for medical digital twins due to its complexity and role in disease.

Purpose of the Study:

  • To review progress in developing immune digital twins.
  • To outline design aspects, implementation prerequisites, and use cases for immune digital twins.
  • To discuss the role of immune digital twins in drug discovery.

Main Methods:

  • Literature review of current digital twin technologies and immune system modeling.
  • Analysis of interdisciplinary initiatives for communication and collaboration.
  • Proposal of use cases for immune digital twins across various disease scales.

Main Results:

  • Initial progress in immune digital twin development and interdisciplinary collaboration initiatives are outlined.
  • Key design aspects and clinical implementation prerequisites are identified.
  • Potential use cases demonstrate the utility of immune digital twins for diverse diseases.

Conclusions:

  • Immune digital twins are a promising technology for personalized therapeutics and prognosis.
  • Overcoming scientific challenges and fostering interdisciplinary collaboration are essential for clinical realization.
  • Immune digital twins hold potential for advancing drug discovery and precision health.