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In Silico Medicine: Investment in next-generation life sciences innovations empowered by computational modelling and simulations

Authors: Whorwood, H; Frangi, AF; Wilkinson, K;

In Silico Medicine: Investment in next-generation life sciences innovations empowered by computational modelling and simulations

Abstract

In silico studies’ refers to using computer modelling and simulation to undertake virtual experiments (chips are largely made of silicon, hence the name). This report will focus on in silico technologies applied to the life sciences or healthcare to facilitate the discovery, development, optimisation or regulatory evaluation of medicines or healthcare products. In silico studies utilise computational modelling to simulate or predict cellular, molecular, or even subatomic interactions, such as DNA replication, protein folding, and RNA splicing. However, more recently, methods underpinned by very similar fundamental approaches have been used to model tissues, organs, full organisms and entire populations in health and disease. In silico differs from in vivo (meaning “in life”) and in vitro (“in glass”). In vivo refers to studies conducted on living organisms, while in vitro refers to studies conducted in test tubes or other laboratory settings outside a living organism. In silico approaches emerged to overcome limitations inherent to lab experimentation with living organisms, cells and specimens. Primarily, it reduces the need for extensive lab work and expensive clinical trials, accelerates regulatory approval, and allows experimentation to be done on a much faster and larger scale. In some form, these techniques have been used for around 30 years, often in combination with in vitro research methods. However, in silico approaches have increased over the last few years thanks to upgrades in computational power and innovations in machine learning and computational sciences. They are becoming an increasing part of research and innovation in life sciences and healthcare thanks to the increasing and vast availability of digital data. This report focuses on UK SMEs using in silico technologies to support a range of outcomes in the life sciences and medical technology sectors. These companies range from those using in silico technologies for drug discovery purposes to those using the technology to test medical devices and those are developing in silico technologies as their primary proposition. The data in the report pertains to 77 UK headquartered in silico companies that are either currently active, independent private companies or have historically been part of this category but have exited via acquisition or IPO or have ceased operations. The rapid development of COVID-19 vaccines shows how rapidly new and effective drugs can be created. In silico technologies promise to improve further and accelerate our ability to tackle disease and health problems with safer and more effective therapeutic practices, drugs, and medical devices.

InnovateUK KTN and the Royal Academy of Engineering Chair in Emerging Technology (INSILEX CiET1819/19) funded this report. We also acknowledge contributions from many members of the InSilicoUK Innovation Network (www.insilicouk.org). Report also available from https://www.beauhurst.com/research/in-silico-medicine/

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Keywords

in silico discovery, in silico medicine, in silico trials, computational modelling, computational simulation, pharmaceuticals, medical devices

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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