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TechBio as a Label is meaningless!

As I have noted previously, the historical demarcation used to differentiate between ‘tech’ and ‘bio’ is largely meaningless: sadly ‘tech’ has been used as pre-fix or a suffix (mainly by investors) to largely describe the use of ‘pervasive’ digital technologies in a wide range of market spaces including medical devices and financial services.

However, the ‘bio’ worlds of biology and chemistry, for example, also include many different aspects of science and technology. This flawed taxonomy may have been helpful in increasing the perceived ‘sizzle’ of different investment opportunities but has clouded the picture in many situations.

More recently ‘tech-bio’ has been proffered as a label to describe developments in bioscience enabled by AI and Machine Learning, supposedly to differentiate from biotech which is now perceived as the science and technology associated with biological innovations (although bio-tech was originally defined to cover the technologies associated with bio-reactors, not fundamental developments, for example, in genomics).

We need a more robust taxonomy to understand the differences between pervasive digital technologies, life-science technologies, and ‘cross-over’ or hybrid areas where digital technologies interact with life science technologies. This is becoming increasingly important because of the significant impact of pervasive digital science and technology on the creation of new products and processes in the life sciences.

Our data-driven research over the last three years has provided a firm foundation for a new taxonomy which enables the excitement around AI driven therapeutics to be understood more precisely. More importantly it also provides the basis for understanding the key components of new commercialisation infrastructures required to support the innovation process. This new taxonomy has three key components as follows:

  • Pervasive digital science and technology which covers the following key elements: ‘conventional’ IT, data management, robotics & rule-based AI, the metaverse (including imaging), and machine learning

  • Hybrid science and technology which covers the following elements: insight generation, correlation, simulation, ML-based training & discovery, and synthesis (for example, new molecular entities)

  • Life science and technology which covers the following: transcriptomics, genomics, proteomics, cells, organs, metrology, prognostics, diagnostics, and therapeutics

This clarity can help all those involved in the commercialisation process to define the tools, talent and experience they require, to create innovative new products and services more precisely. In particular, it can help those building commercialisation infrastructures to identify the right resources partners they need to provide to accelerate commercialisation journeys, which may be at odds with the perceptions of what they need to facilitate (for example access to computing and database infrastructures may only be the starting point).

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