From Multiscale Biophysics to Digital Twins of Tissues and Organs: Future Opportunities for <i>in-silico</i> Pharmacology

With many advancements in in silico multiscale biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and brain tissues; 2) To discuss the present and future role of multiscale biophysics in in silico pharmacology as a digital twin solution by defining a roadmap from simple biophysical models to powerful prediction tools. Digital twins have the potential to pave the way for extensive clinical and pharmaceutical usage of multiscale models, and our paper shows the fundamentals and opportunities for their accurate development, enabling the quantum leaps of future precise and personalized medical software.

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Publication Details

Journal
IEEE Transactions on Molecular Biological and Multi-Scale Communications
Published
2024-08-12
DOI
https://doi.org/10.1109/tmbmc.2024.3442083
Citations
10
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
1.84
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article

From Multiscale Biophysics to Digital Twins of Tissues and Organs: Future Opportunities for in-silico Pharmacology

Kerstin Lenk, Jussi T. Koivumäki, Jari Hyttinen, Aapo Tervonen et al.
10 citations
IEEE Transactions on Molecular Biological and Multi-Scale Communications
3D Printing in Biomedical Research
1.84
article

From Multiscale Biophysics to Digital Twins of Tissues and Organs: Future Opportunities for in-silico Pharmacology

Kerstin Lenk, Jussi T. Koivumäki, Jari Hyttinen, Aapo Tervonen, Michelangelo Paci, Michael Taynnan Barros, Elisa Passini
article en
10 citations

Abstract

With many advancements in in silico multiscale biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and brain tissues; 2) To discuss the present and future role of multiscale biophysics in in silico pharmacology as a digital twin solution by defining a roadmap from simple biophysical models to powerful prediction tools. Digital twins have the potential to pave the way for extensive clinical and pharmaceutical usage of multiscale models, and our paper shows the fundamentals and opportunities for their accurate development, enabling the quantum leaps of future precise and personalized medical software.

IEEE Transactions on Molecular Biological and Multi-Scale CommunicationsVol. 10(4)
University of Essex (GB), Tampere University (FI), Marconi University (IT), University of Oxford (GB), Graz University of Technology (AT), BioTechMed-Graz (AT), Science Oxford (GB), University of Bologna (IT)
Openalex Percentile: Top 15%
3D Printing in Biomedical Research
1.84
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