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

Abstract

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.

Fig. 1. Interdisciplinarity as a key factor in Building Immune Digital Twins.

Illustration of the collaboration among diverse stakeholders to establish an international and interdisciplinary community dedicated to the development and deployment of Immune Digital Twins. Modified template from https://youexec.com/.

Fig. 2. A minimalistic conceptual design of an IDT implementation.

Producing a DT requires calibrating a computational model to data derived from a real-world patient. The connection to the real world is seen in the grey box to the left, where inputs of different types are generated for a particular individual and then passed to the virtual/computational model to personalise (“twin”) that general model to the specific patient. The “twinning” process involves parameterisation and a matching score to the real-world system by making predictions of how the real-world system propagates through time. This process iterates as new data becomes available and the DT is updated.

Fig. 3. Different steps across scientific fields for a full-circle IDT implementation.

Stepwise and domain specific steps for the implementation of IDTs. The building blocks of each scientific domain can be developed independently. However, attention to interoperability, use of common standards, and compliance with the FAIR principles will accelerate the building of IDTs that cover most of the technical/ methodological needs.

Fig. 4. Human immune response in various pathologies.

Immune/Inflammatory functions and their relationship to various classes of diseases: Autoimmune diseases in which failure in non-self-recognition or negative feedback control of proinflammation leads to persistent inflammation and long-term tissue damage; Infections in which the immune response is responding to various types of microbes (viruses, bacteria and fungi); Ageing, where changes in the function of the immune response can lead to a host of diseases; Acute Illness, where a host of perturbations rapidly activates the immune response. Immune pathophysiological processes range in time scale from hours for acute illness and sepsis to years and decades in autoimmune diseases and cancer. We propose that nearly every disease process and its potential resolution involves to some degree, inflammation and immunity.

Fig. 5. A bioinformatics ecosystem for data analysis, integration and modelling in IDT implementations.

Stepwise process to obtain a DT implementation that accommodates different types of input data, integrative methods, and modelling formalisms to template model building, simulation, and analysis to create personalised instantiations for therapeutic interventions. The predictions can be tested using in vitro assays using humanised cellular systems (like organoids) and in silico population trials.

Fig. 6. Key challenges in developing and implementing IDTs in pre-clinical and clinical settings.

The implementation of IDTs require a community-driven approach to tackle challenges in data acquisition, analysis and integration, policy and data protection, methodological aspects to address complexity, dedicated infrastructure development. Tailor-made solutions are also needed to address specific unmet needs in different fields of application, and, lastly, robust and credible scalable modelling approaches for complex human pathologies that involve characteristic immune responses. Modified template from https://youexec.com/.