Building digital twins of the human immune system: toward a roadmap

Laubenbacher, Reinhard; Niarakis, Anna; Helikar, Tomáš; An, Gary; Shapiro, Bruce E.; Sheriff, Rahuman S. Malik; Sego, T. J.; Knapp, Ádám; Macklin, Paul; Glazier, James A.

doi:10.1038/s41746-022-00610-z

Cited by 70 publications

(54 citation statements)

References 29 publications

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“…It may revolutionise the management of obesity and its comorbidities, and provide a pillar for healthy ageing. Moreover, Laybenbacher et al 63 outlined a roadmap for building an immune DT to capture key features of the immune system, which was to structure in four stages: specification, model constructions, personalisation and continued improvement. It would help to change the nature of biomedical research and accelerate the clinical translation of basic research.…”

Section: Resultsmentioning

confidence: 99%

Digital twin in healthcare: Recent updates and challenges

Sun

2023

DIGITAL HEALTH

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As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.

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Section: Resultsmentioning

confidence: 99%

Digital twin in healthcare: Recent updates and challenges

Sun

2023

DIGITAL HEALTH

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“…One of the primary benefits of in silico clinical trials is the capacity to run experiments in a virtual environment without the need for expensive and timeconsuming real-world experiments. One technology being heavily discussed in the clinical trial space is the idea of a digital twin -an artificial physiological model of a patient that responds to various clinical trial experiments [65,66].…”

Section: Trial Outcome Predictionmentioning

confidence: 99%

“…However, digital twins are a relatively novel idea in the clinical space. While there has been much work in the form of position papers promoting the general concept of digital twins [68,69,70,71,66], only a few have described their methodology in detail.…”

Section: Trial Outcome Predictionmentioning

confidence: 99%

Artificial Intelligence for In Silico Clinical Trials: A Review

Wang¹,

Gao²,

Glass³

et al. 2022

Preprint

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A clinical trial is an essential step in drug development, which is often costly and timeconsuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications.

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“…There are increasing interests and efforts in the space of digital twinning of patients ( 10 , 1 ). And we believe that amongst the most natural, feasible and on-the-near-immediate-horizon approaches to digital twins are so-called theranostic digital twins (TDTs), which we describe next.…”

Section: Introductionmentioning

confidence: 99%

Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology

Rahmim

Brosch-Lenz²,

Fele-Paranj

et al. 2022

Front. Oncol.

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This work emphasizes that patient data, including images, are not operable (clinically), but that digital twins are. Based on the former, the latter can be created. Subsequently, virtual clinical operations can be performed towards selection of optimal therapies. Digital twins are beginning to emerge in the field of medicine. We suggest that theranostic digital twins (TDTs) are amongst the most natural and feasible flavors of digitals twins. We elaborate on the importance of TDTs in a future where ‘one-size-fits-all’ therapeutic schemes, as prevalent nowadays, are transcended in radiopharmaceutical therapies (RPTs). Personalized RPTs will be deployed, including optimized intervention parameters. Examples include optimization of injected radioactivities, sites of injection, injection intervals and profiles, and combination therapies. Multi-modal multi-scale images, combined with other data and aided by artificial intelligence (AI) techniques, will be utilized towards routine digital twinning of our patients, and will enable improved deliveries of RPTs and overall healthcare.

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Building digital twins of the human immune system: toward a roadmap

Cited by 70 publications

References 29 publications

Digital twin in healthcare: Recent updates and challenges

Digital twin in healthcare: Recent updates and challenges

Artificial Intelligence for In Silico Clinical Trials: A Review

Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology

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