Fig. 2: Minimal set of in silico model categories and their context of uses required for the virtual representation of an eACI-VT.

CAR T cells are complex, patient-specific (autologous) or donor-specific (allogeneic) therapies. Therefore, it is beneficial to model a patient’s unique (patho)biology alongside the molecular and cellular characteristics of the retrieved T cells and the resulting CAR T cell product. There are four levels of context of uses of an eACI-VT (outer layer): (i) characterizing the patients’/donors’ status prior, the patients’ status during and after therapy, (ii) characterizing the cells during leukapheresis, manufacturing, and in the final cell product, (iii) characterizing the changes of the cell product due the interaction with the patient, and (iv) characterizing the changes of the target cells due to interaction with the CAR T cells. Therefore, the minimal set of in silico models required in an eACI-VT encompass multiple biological scales (middle layer): models for the whole body and for organs reflecting the system-wide status prior to treatment and the impact of treatment, tissue scale, and intercellular scale models for CAR T cell interaction with target cells and the target cells’ tissue during therapy, and cellular scale models that represent intracellular signaling of T cells at time of leukapheresis, (CAR) T cells during manufacturing, the CAR T cells in the medicinal cell product and their target cells. Appropriate in silico model categories that can be used to model events at the different biological scales include (inner layer) system biology models, knowledge-driven models, mechanistic cell models, stochastic models, statistical models (SL), machine learning (ML), and artificial intelligence (AI), ordinary or partial differential equations (ODEs, PDEs), as well as computational structural biology (3D protein structure and 3D/2D RNA structure models). These in silico models receive data generated on different devices and from various systems throughout patient care (central circle). In inpatient care, data from hospital information systems like the electronic health record, as well as lab data and molecular data, fuel the models. Outpatient care provides data via wearables, digital health devices, and the Internet of Things (IoT). During manufacturing, data are supplied via IoT and Cyber-Physical Systems (CPS). The figure was created by the authors using Canva.com.