TY - JOUR
T1 - Immune digital twins for complex human pathologies
T2 - applications, limitations, and challenges
AU - Niarakis, Anna
AU - Laubenbacher, Reinhard
AU - An, Gary
AU - Ilan, Yaron
AU - Fisher, Jasmin
AU - Flobak, Asmund
AU - Reiche, Kristin
AU - Martinez, Maria Rodriguez
AU - Geris, Liesbet
AU - Ladeira, Luiz
AU - Veschini, Lorenzo
AU - Blinov, Michael L.
AU - Messina, Francesco
AU - Fonseca, Luis L.
AU - Ferreira, Sandra
AU - Montagud, Arnau
AU - Noel, Vincent
AU - Marku, Malvina
AU - Tsirvouli, Eirini
AU - Torres, Marcella M.
AU - Harris, Leonard A.
AU - Sego, T. J.
AU - Cockrell, Chase
AU - Shick, Amanda E.
AU - Balci, Hasan
AU - Salazar, Albin
AU - Rian, Kinza
AU - Hemedan, Ahmed Abdelmonem
AU - Esteban-Medina, Marina
AU - Staumont, Bernard
AU - Hernandez-Vargas, Esteban
AU - Martis, B. Shiny
AU - Madrid-Valiente, Alejandro
AU - Karampelesis, Panagiotis
AU - Sordo Vieira, Luis
AU - Harlapur, Pradyumna
AU - Kulesza, Alexander
AU - Nikaein, Niloofar
AU - Garira, Winston
AU - Sheriff, Rahuman S. Malik
AU - Thakar, Juilee
AU - Tran, Van Du T.
AU - Carbonell-Caballero, Jose
AU - Safaei, Soroush
AU - Valencia, Alfonso
AU - Zinovyev, Andrei
AU - Glazier, James A.
PY - 2024/11/30
Y1 - 2024/11/30
N2 - 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.
AB - 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.
KW - SOFA SCORE
KW - SEPSIS
KW - CANCER
KW - STANDARD
KW - CRITERIA
KW - DRIVEN
U2 - 10.1038/s41540-024-00450-5
DO - 10.1038/s41540-024-00450-5
M3 - (Systematic) Review article
SN - 2056-7189
VL - 10
JO - NPJ systems biology and applications
JF - NPJ systems biology and applications
IS - 1
M1 - 141
ER -