11 January 2022
The first article in our ‘IP trends for 2022’ series on innovation and technology focuses on ‘Biology 2.0’ and how it is impacted by deep tech and artificial intelligence (AI).
You may have heard of ‘Industry 4.0’ as being the ‘fourth industrial revolution’, relating to the digital transformation of manufacturing, production and related industries, as well as value creation processes. Looking to the year ahead, the term you should become familiar with is ‘Biology 2.0’ –where biology and technology unite in deep tech companies.
As summarised by Eroom’s law (a clear contradiction of Moore’s law), to date advances in biology, such as drug discovery, are becoming slower and more expensive over time, despite improvements in technology – with the costs of bringing a drug to market now amounting to about $2.5 billion and taking over 10 years. Now, 20 years since the human genome project, things finally look set to change. Over recent years we have heard of – and previously reported on – British Research Lab DeepMind’s AI ‘AlphaFold’ programme (owned by Google parent company Alphabet), which uses artificial intelligence (AI) to perform predictions of protein structure. Due to advances in engineering and computer science, and in particular AI, we are now starting to see fundamental changes to the way in which problems in biology are addressed, making use of these advances in AI technologies. This has been referred to by some in the field as ‘Biology 2.0’ – where engineering and computer science principles are applied to biological problems.
Examples of applications where advances are already taking place include:
Companies such as Foundation Medicine develop, manufacture and sell genomic profiling assays based on next-generation sequencing technology for solid tumours, hematologic malignancies and sarcomas, that can be used to match patients to targeted therapies, immunotherapies and clinical trial options. They also partner with the biopharma industry to help discover, develop and distribute the next breakthrough treatments for patients.
The Medicines Discovery Catapult, is testing an in silico (i.e. on a computer) pipeline for identifying new molecules for cancer treatment, for example, applying AI to find new disease ‘drivers’ and candidate drugs for lung cancer. Backed by Innovate UK, it is hoped that this will derisk future research and development projects and also demonstrate new cost and time-saving approaches to drug discovery.
It has been found that neural networks (used in AI) in particular are great at applications that depend on sequential data such as DNA sequence data. Biofacturing companies, such as Zymergen, are exploiting protein structures, such as those produced by DeepMind relating to microbes (e.g. organisms like E. coli), and using them to make products with biology. It is hoped that such data generated by DeepMind can be used for ‘synthetic biology’ – for example, to create microbes that better produce breakthrough chemicals and materials.
Companies such as Brainomix specialise in the creation of AI-powered imaging biomarkers that enable precision medicine for better treatment decisions.
Making use of advanced AI techniques, scientists in the US have developed a tiny camera the size of a coarse grain of salt that takes full-colour images that are as good as ones taken with camera lenses 500,000 times larger in size – which may have real world use, for example in diagnostic imaging (as well as potentially improving the quality of selfies in your smartphone!).
CoMind is working on next-generation non-invasive brain-computer interfaces which will help to develop and improve understanding of the human brain and neurological disorders.
It is clear that there are numerous opportunities to explore within ‘Biology 2.0’ and we are excited to already be working with many innovators in this field. Of course, as with many deep tech companies, those involved in ‘Biology 2.0’ are very IP-rich, however, they often need significant capital investment before they can begin generating revenue. Once a specific milestone is reached and such organisations do start generating revenue, progress can be very significant and. This requires a different model and mindset from investors and those involved in the field, as well as an effective IP strategy to match that can be used to monetise the IP assets.
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