review · npj Systems Biology and Applications
Digital twins are computational models designed to track an individual patient's health over time to forecast prognosis and determine optimal treatments. Because many medical conditions involve the immune system, capturing immune features within these models is vital for precision healthcare. However, the immune response is intricate and varies widely across different patients, diseases, and spatial and temporal scales ranging from minutes to years. Developing immune digital twins requires collaborative efforts among computational modellers, immunologists, and clinicians. Current work focuses on establishing interdisciplinary communication, defining essential design components, and setting clinical implementation requirements. Initial use cases serve as proof-of-concept demonstrations across varied immune conditions, highlighting the potential value of digital twins in therapeutic discovery and patient care while outlining the technical challenges that still need resolving.
Immune responses differ significantly between individuals, making many complex diseases difficult to predict and treat. Immune digital twins could offer computer simulations tailored to individual patients, helping doctors choose the most effective therapies and aiding scientists in discovering new drugs. Achieving this could ultimately improve personalised healthcare outcomes for conditions that depend heavily on immune function.
Potential applications include patient prognosis forecasting, personalised therapy selection, and drug discovery tools for pharmaceutical developers and healthcare providers. The technology remains at an early stage, focused on conceptual frameworks, cross-disciplinary alignment, and initial proof-of-concept use cases rather than ready-to-deploy products. Significant clinical implementation prerequisites and scientific challenges must be resolved before these computational tools can enter routine clinical or industrial workflows.
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Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.
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DOI: 10.1038/s41540-024-00450-5
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