Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
IEEE Reviews in Biomedical Engineering: The Physiome Project and Digital Twins
Peter Hunter et al
Abstract
Interest in the concept of a virtual human model that can encompass human physiology and anatomy on a biophysical (mechanistic) basis, and can assist with the clinical diagnosis and treatment of disease, appears to be growing rapidly around the globe. When such models are personalised and coupled with continual diagnostic measurements, they are called 'digital twins'. We argue here that the most useful form of virtual human model will be one that is constrained by the laws of physics, contains a comprehensive knowledge graph of all human physiology and anatomy, is multiscale in the sense of linking systems physiology down to protein function, and can to some extent be personalized and linked directly with clinical records. We discuss current progress from the IUPS Physiome Project and the requirements for a framework to achieve such a model.
---------------------------------------
IEEE Journal of Biomedical and Health Informatics: Advancing In Silico Clinical Trials for Regulatory Adoption and Innovation
Georgia Karanasiou et al
Abstract
The evolution of information and communication technologies has affected all fields of science, including health sciences. However, the rate of technological innovation adoption by the healthcare sector has been historically slow, compared to other industrial sectors. Innovation in computer modeling and simulation approaches has changed the landscape in biomedical applications and biomedicine, paving the way for their potential contribution in reducing, refining, and partially replacing animal and human clinical trials. In Silico Clinical Trials (ISCT) allow the development of virtual populations used in the safety and efficacy testing of new drugs and medical devices. This White Paper presents the current framework for ISCT, the role of in silico medicine research communities, the different perspectives (research, scientific, clinical, regulatory, standardization, data quality, legal and ethical), the barriers, challenges, and opportunities for ISCT adoption. In addition, an overview of successful ISCT projects, market-available platforms, and FDA- approved paradigms, along with their vision, mission and outcomes are presented.
---------------------------------------
Journal of Responsible Innovation: Brokering responsible research and innovation in in silico medicine
Elisa Elhadj et al
Abstract
The implementation of Responsible Research and Innovation (RRI) in research projects has increased the need for interdisciplinary collaboration. This article presents our RRI approach within the Horizon 2020 ‘In Silico World' project, which aims to accelerate the adoption of in silico medicine through computer modeling and simulation tools in healthcare. To address the shortcomings of the ‘checklist approach' for integrating ethics and the risk of becoming checkboxes ourselves, we introduce the term ‘RRI brokers.’ It serves as a lens for evaluating the project's RRI activities and the dynamics that can be faced by Social Science and Humanities scholars (SSH), and as a means to acquire agency in our own positioning. We suggest that to strengthen RRI, more consideration is needed on how we present our expertise in these collaborations, and awareness of how we, as RRI brokers, move between translating, balancing, and shaping worlds, affecting what we broker and ourselves.
---------------------------------------
Journal of Personalized Medicine: Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine
Konstantinos Papachristou et al
Abstract
This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.
---------------------------------------Computer Methods and Programs in Biomedicine: A porohyperelastic scheme targeted at High-Performance Computing frameworks for the simulation of the intervertebral disc
Dimitrios Lialios et al
Abstract
Background and Objective:
The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources.
Methods:
In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model’s ability to simulate alternating loading. Finally, the implementation’s circadian response is compared to previous implementation of similar material models in commercial software.
Results:
Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V.
Bone: OMIBONE: Omics-driven computer model of bone regeneration for personalized treatment
Mahdi Jaber et al
Abstract
Treatment of bone fractures are standardized according to the AO classification, which mainly refers to the mechanical stabilization required in a given situation but neglect individual differences due to patient's healing potential or accompanying diseases. Specially in elderly or immune-compromised patients, the complexity of individual constrains on a biological as well as mechanical level are hard to account for. Here, we introduce a novel framework that allows to predict bone regeneration outcome using combined proteomic and mechanical analyses in a computer model. The framework uses Ingenuity Pathway Analysis (IPA) software to link protein changes to alterations in biological processes and integrates these in an Agent-Based Model (ABM) of bone regeneration. This combined framework allows to predict bone formation and the potential of an individual to heal a given fracture setting. The performance of the framework was evaluated by replicating the experimental setup of a mouse femur fracture stabilized with an intramedullary pin. The model was informed by serum derived proteomics data. The tissue formation patterns were compared against experimental data based on x-ray and histology images. The results indicate the framework potential in predicting an individual's bone formation potential and hold promise as a concept to enable personalized bone healing predictions for a chosen fracture fixation.
