In silico medicine

Last updated

In silico medicine (also known as "computational medicine") is the application of in silico research to problems involving health and medicine. It is the direct use of computer simulation in the diagnosis, treatment, or prevention of a disease. More specifically, in silico medicine is characterized by modeling, simulation, and visualization of biological and medical processes in computers with the goal of simulating real biological processes in a virtual environment. [1]

Contents

History

The term in silico was first used in 1989 at a workshop "Cellular Automata: Theory and Applications" by a mathematician from National Autonomous University of Mexico (UNAM). [2] The term in silico radiation oncology, a precursor of generic in silico medicine was coined and first introduced by G. Stamatakos in Proceedings of the IEEE in 2002. [3] The same researcher coined and introduced the more generic term in silico oncology. [4] In silico medicine is considered an extension of previous work using mathematical models of biological systems. [4] It became apparent that the techniques used to model biological systems has utility to explain and predict dynamics in the medical field. The first fields in medicine to use in silico modeling were genetics, physiology and biochemistry. The field saw a dramatic influx of data when the human genome was sequenced in the 1980s and 1990s. Concurrently the increase in available computational power allowed for modeling of complex systems that were previously impractical. [5]

Rationale

There are numerous reasons why in silico medicine is used. For example, in silico medical modeling can allow for early prediction of success of a compound for a medicinal purpose and elucidate potential adverse effects early in the drug discovery process. [6] In silico modeling can also provide a humane alternative to animal testing. [2] It has been purported by a company in the field, that computer-aided models will make the use of testing on living organisms obsolete. [7]

Examples

The term in silico medicine is exemplified in initiatives such as the Virtual Physiological Human by the European Commission [8] and in institutes such as the VPH Institute and the INSIGNEO Institute at the University of Sheffield.

The In Silico Oncology Group (ISOG) [9] at the Institute of Communication and Computer Systems, National Technical Institute of Athens (ICCS-NTUA) aims at developing clinically driven and oriented multiscale simulation models of malignant tumors (cancer) to be utilized as patient individualized decision support and treatment planning systems following completion of clinical adaptation and validation. An additional aim of the Group's research is to simulate oncological clinical trials which would otherwise be too costly or time intensive and to this direction, grid computing infrastructures have been exploited, such as the European Grid Infrastructure, to increase the performance and effectiveness of the simulations. [10] ISOG has led the development of the first technologically integrated Oncosimulator, a joint Euro-Japanese research venture. [11]

In 2003, the first vaccine based solely off of genomic information was developed. The technique of developing the vaccine, termed "reverse vaccinology", used the genomic information and not the infectious bacteria itself to develop the vaccine. [12]

In December 2018, the four-year PRIMAGE project was launched. This EU-funded Horizon 2020 project proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumours, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow up, based on the use of novel imaging biomarkers, in-silico tumour growth simulation, advanced visualization of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points. [13] [14]

In 2020, the first CADFEM Medical Conference on the topic of in vivo, in vitro, in silico was held with the guiding theme: "The central role of in silico medicine – what it can do and what we need for its practice." [15]

Research

As modeling of human, social, behavioral, and cultural (HSBC) characteristics of patient behavior becomes more sophisticated, there is speculation that virtual patients may replace patient actors in medical school curriculum. Additionally, there are projects underway that utilize virtual cadavers, computer simulated models of human anatomy based on CT images of real people. [16]

See also

Related Research Articles

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer science and engineering which uses bioengineering to build computers.

<span class="mw-page-title-main">Health informatics</span> Computational approaches to health care

Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be viewed as branch of engineering and applied science.

<i>In silico</i> Latin phrase referring to computer simulations

In biology and other experimental sciences, an in silico experiment is one performed on a computer or via computer simulation software. The phrase is pseudo-Latin for 'in silicon', referring to silicon in computer chips. It was coined in 1987 as an allusion to the Latin phrases in vivo, in vitro, and in situ, which are commonly used in biology. The latter phrases refer, respectively, to experiments done in living organisms, outside living organisms, and where they are found in nature.

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.

<span class="mw-page-title-main">Biocomplexity Institute of Virginia Tech</span> Research organization for computational biology and related fields

The Biocomplexity Institute of Virginia Tech was a research institute specializing in bioinformatics, computational biology, and systems biology. The institute had more than 250 personnel, including over 50 tenured and research faculty. Research at the institute involved collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology and medicine. The institute developed -omic and bioinformatic tools and databases that can be applied to the study of human, animal and plant diseases as well as the discovery of new vaccine, drug and diagnostic targets.

<span class="mw-page-title-main">Personalized medicine</span> Medical model that tailors medical practices to the individual patient

Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept though some authors and organisations use these expressions separately to indicate particular nuances.

The cancer Biomedical Informatics Grid (caBIG) was a US government program to develop an open-source, open access information network called caGrid for secure data exchange on cancer research. The initiative was developed by the National Cancer Institute and was maintained by the Center for Biomedical Informatics and Information Technology (CBIIT) and program managed by Booz Allen Hamilton. In 2011 a report on caBIG raised significant questions about effectiveness and oversight, and its budget and scope were significantly trimmed. In May 2012, the National Cancer Informatics Program (NCIP) was created as caBIG's successor program.

The term virtual patient is used to describe interactive computer simulations used in health care education to train students on clinical processes such as making diagnoses and therapeutic decisions. Virtual patients attempt to combine modern technologies and game-based learning to facilitate education, and complement real clinical training. Using virtual patients is increasing in healthcare due to increased demands on healthcare professionals, education of healthcare trainees, and providing learners with a safe practice environment. There are many formats from which a virtual patient may be chosen, but the overarching principle is that of interactivity. Virtual patients typically have mechanisms where information is parsed out in response to the learners, simulating how patients respond to different treatments. Interactivity can be created with questions, specific decision-making tasks, text composition, etc., and is non-sequential. Most systems provide quantitative and qualitative feedback. In some cases, virtual patients are not full simulations themselves, but are mainly based on paper-based cases; as they do not allow for physical examination or an in-depth medical history of an actual patient. There are certain drawbacks as crucial clinical findings may be missed due to the lack of examining patients in person.

The Virtual Physiological Human (VPH) is a European initiative that focuses on a methodological and technological framework that, once established, will enable collaborative investigation of the human body as a single complex system. The collective framework will make it possible to share resources and observations formed by institutions and organizations, creating disparate but integrated computer models of the mechanical, physical and biochemical functions of a living human body.

Systems immunology is a research field under systems biology that uses mathematical approaches and computational methods to examine the interactions within cellular and molecular networks of the immune system. The immune system has been thoroughly analyzed as regards to its components and function by using a "reductionist" approach, but its overall function can't be easily predicted by studying the characteristics of its isolated components because they strongly rely on the interactions among these numerous constituents. It focuses on in silico experiments rather than in vivo.

Igor I. Goryanin is a systems biologist, who holds a Henrik Kacser Chair in Computational Systems Biology at the University of Edinburgh. He also heads the Biological Systems Unit at the Okinawa Institute of Science and Technology, Japan.

Imaging informatics, also known as radiology informatics or medical imaging informatics, is a subspecialty of biomedical informatics that aims to improve the efficiency, accuracy, usability and reliability of medical imaging services within the healthcare enterprise. It is devoted to the study of how information about and contained within medical images is retrieved, analyzed, enhanced, and exchanged throughout the medical enterprise.

<span class="mw-page-title-main">Gajendra Pal Singh Raghava</span>

Gajendra Pal Singh Raghava is an Indian bio-informatician and head of computational biology at the Indraprastha Institute of Information Technology.

<span class="mw-page-title-main">Amira (software)</span> Software platform for 3D and 4D data visualization

Amira is a software platform for visualization, processing, and analysis of 3D and 4D data. It is being actively developed by Thermo Fisher Scientific in collaboration with the Zuse Institute Berlin (ZIB), and commercially distributed by Thermo Fisher Scientific — together with its sister software Avizo.

An in silico clinical trial, also known as a virtual clinical trial, is an individualized computer simulation used in the development or regulatory evaluation of a medicinal product, device, or intervention. While completely simulated clinical trials are not feasible with current technology and understanding of biology, its development would be expected to have major benefits over current in vivo clinical trials, and research on it is being pursued.

<span class="mw-page-title-main">Artificial intelligence in healthcare</span> Overview of the use of artificial intelligence in healthcare

Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to arrive at approximate conclusions based solely on input data.

MBN Explorer is a software package for molecular dynamics simulations, structure optimization and kinetic Monte Carlo simulations. It is designed for multiscale computational analysis of structure and dynamics of atomic clusters and nanoparticles, biomolecules and nanosystems, nanostructured materials, different states of matter and various interfaces. The software has been developed by MBN Research Center.

<span class="mw-page-title-main">Merative</span> U.S. healthcare company

Merative L.P., formerly IBM Watson Health, is an American medical technology company that provides products and services that help clients facilitate medical research, clinical research, real world evidence, and healthcare services, through the use of artificial intelligence, data analytics, cloud computing, and other advanced information technology. Merative is owned by Francisco Partners, an American private equity firm headquartered in San Francisco, California. In 2022, IBM divested and spun-off their Watson Health division into Merative. As of 2023, it remains a standalone company headquartered in Ann Arbor with innovation centers in Hyderabad, Bengaluru, and Chennai.

<span class="mw-page-title-main">Dean F. Sittig</span> US Professor in Biomedical Informatics and Bioengineering

Dean Forrest Sittig is an American biomedical informatician specializing in clinical informatics. He is a professor in Biomedical Informatics at the University of Texas Health Science Center at Houston and Executive Director of the Clinical Informatics Research Collaborative (CIRCLE). Sittig was elected as a fellow of the American College of Medical Informatics in 1992, the Healthcare Information and Management Systems Society in 2011, and was a founding member of the International Academy of Health Sciences Informatics in 2017. Since 2004, he has worked with Joan S. Ash, a professor at Oregon Health & Science University to interview several Pioneers in Medical Informatics, including G. Octo Barnett, MD, Morris F. Collen, MD, Donald E. Detmer, MD, Donald A. B. Lindberg, MD, Nina W. Matheson, ML, DSc, Clement J. McDonald, MD, and Homer R. Warner, MD, PhD.

<span class="mw-page-title-main">Konstantina Nikita</span> Greek engineer

Konstantina "Nantia" Nikita is a Greek electrical and computer engineer and a professor at the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA), Greece. She is director of the Mobile Radiocommunications Lab and founder and director of the Biomedical Simulations and Imaging Lab, NTUA. Since 2015, she has been an Irene McCulloch Distinguished Adjunct Professor of Biomedical Engineering and Medicine at Keck School of Medicine and Viterbi School of Engineering, University of Southern California.

References

  1. ↑ "Project Success Stories - In silico medicine reaches the clinic". European Commission CORDIS. Retrieved 9 November 2017.
  2. 1 2 "In Silico methods | ORCHESTRA". Archived from the original on 2014-04-13. Retrieved 2014-04-10.
  3. ↑ G.Stamatakos; D.Dionysiou; E.Zacharaki; N.Mouravliansky; K.Nikita; N.Uzunoglu (2002). "In silico radiation oncology: combining novel simulation algorithms with current visualization techniques". Proceedings of the IEEE. 90 (11): 1764–1777. doi:10.1109/JPROC.2002.804685.
  4. 1 2 Stamatakos, Georgios (2006). "Spotlight on Cancer Informatics". Cancer Informatics (2): 83–86. doi:10.1177/117693510600200029. PMC   2675506 . PMID   19458760.
  5. ↑ Schneider, Maria Victoria (2013). In Silico Systems Biology. Methods in Molecular Biology. Vol. 1021. Methods in Molecular Biology. pp. Chapter 1. doi:10.1007/978-1-62703-450-0. ISBN   978-1-62703-449-4. S2CID   3690704.
  6. ↑ Ahmet, Ahmet; Sean, Ekins; Sandhya, Kortagere (2012-01-08). "Applications and Limitations of In Silico Models in Drug Discovery". Bioinformatics and Drug Discovery. 910: 87–124. doi:10.1007/978-1-61779-965-5_6.
  7. ↑ "Improving the Welfare of Animals: Researchers Develop Computer-Aided Models to Replace Animal Testing". 4 October 2013. Archived from the original on 4 March 2016.
  8. ↑ "The Seventh Framework Programme (FP7)". FP7 European Commission. Retrieved 10 May 2013.
  9. ↑ "In Silico Oncology and in Silico Medicine Group". in-silico-oncology.iccs.ntua.gr. 2014-04-10. Archived from the original on 2018-10-15.
  10. ↑ Athanaileas, Theodoros; et al. (2011). "Exploiting grid technologies for the simulation of clinical trials: the paradigm of in silico radiation oncology". SIMULATION: Transactions of the Society for Modeling and Simulation International. 87 (10). Sage Publications: 893–910. doi:10.1177/0037549710375437. S2CID   206429690.
  11. ↑ G. Stamatakos; D. Dionysiou; A. Lunzer; R. Belleman; E. Kolokotroni; E. Georgiadi; M. Erdt; J. Pukacki; S. Rueping; S. Giatili; A. d'Onofrio; S. Sfakianakis; K. Marias; C. Desmedt; M. Tsiknakis; N. Graf (2014). "The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context". IEEE Journal of Biomedical and Health Informatics. 18 (3): 840–854. doi:10.1109/JBHI.2013.2284276.
  12. ↑ Nicholls, Henry (2008). "In silico vaccine". Nature Biotechnology. 26 (6): 597. doi: 10.1038/nbt0608-597b . S2CID   32402661.
  13. ↑ "Background".
  14. ↑ Martí-Bonmatí, Luis; et al. (2020). "PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers". European Radiology Experimental. 4 (1): 22. doi: 10.1186/s41747-020-00150-9 . PMC   7125275 . PMID   32246291.
  15. ↑ "CADFEM Medical Conference". conference.cadfem-medical.com. Retrieved 2021-09-28.
  16. ↑ Sokolowski, John A., Banks, Catherine M. Modeling and simulation in the medical and health sciences. Hooken: Wiley, c2011.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.