Innovation in biotechnology is driven by automatization

When Marc Andreessen, co-founder of Andreessen Horowitz, wrote a Wall Street Journal essay in 2011, he left us the quote: "Software is eating the world". More than a decade later it is fair to say that software has at least taken a deep bite into our social lives, and became an integral (and mostly seamless) part of almost any business operation in the world. Software is everywhere and often everything – it controls the way we navigate cars, interact with peers, consume goods, do politics, and drive innovation. Software codes and algorithms continue to impact economic dynamics, disrupt traditional business models, and generate new revenue streams.

There is little doubt that Artificial Intelligence is the buzzword of the year. Albeit seldom truly "intelligent", technology that helps, facilitates or even enables human decision-making is ubiquitous. The hype around Generative Pre-trained Transformation (as in ChatGPT or GPT-4), i.e. AI that creates novel content, has fuelled the hopes and visions that artificial neural networks will soon be able to interpret complex data just like a human brain. 

In Biotechnology, though, the disruption is yet to come. Lacking interrelated data and molecular design information, biopharma still employs the traditional iterative and manual research process. Despite the many AI-driven biotech companies that draw the attention of investors and industry partners, pharma R&D is facing a continuous decline in productivity and a massive increase in development costs. GPT is no help yet: Generative AI needs training in order to become predictive, and training requires multi-dimensional data sets. Today, simply, any such data sets are virtually non-existent, at least for the complex molecules needed to fight devastating diseases like cancer.

The answers to the biopharma data gap are parallelization and automatization, just like in the automotive industry decades ago. Only automatization can generate meaningful and interrelated experimental data sets on multi-targeting modalities. And only those data sets will unlock the full potential of AI and ML in biopharma R&D. The traditional paradigm that biopharma investments are risk-investments because they depend on just a small set of key assets, and portfolios often consist only of one or two single assets, will change. Overall, this would also be a fundamental change of the way biopharma R&D is currently done and it is expected to make a change to the way investment opportunities are looked at in Biopharma, too. It is not a coincidence that drug discovery and drug development platforms start to draw the attention of investors. Companies like Iktos (for small molecules, at least), Exscientia (for monoclonal antibodies), Perspix (for complex multi-specific molecules) and Flagship's portfolio company Generate Biomedicines start floodlighting the way through the productivity crisis of biopharma R&D with automated and integrated platform technologies that were long unheard of. There is great potential that this will result in the creation of vast portfolios of optimized drug candidates. Chances are that automated platforms will help researchers to understand the core design principles for complex biologics. Automated and fully integrated platform technologies that are able to pre-screen millions of drug variants in silico (or even design them de novo) and then validate them in vitro may, hence, set a new and truly innovative drug development standard. 

The estimated value of the global biotech market is reported to be bigger than €100 billion. Those numbers are based on the traditional R&D processes; the massive productivity gains and levers of the proposed automatization have not even been considered yet. 

We, therefore, expect to see an uptake in robotics and drug discovery platform investments in 2023 (and beyond) and conclude that for the first time in biopharma history it may become possible to de-risk pharma investments just by the number of molecules automatization is able to generate.

Matthias Wiedenfels, industrial expert and senior adviser in our Healthcare practice group, states: "The integration of AI and ML in automated end-to-end closed-loop feedback systems will generate data sets of unprecedented quantity and quality and may end the biopharma productivity crisis. Chances for early-stage investors to ignite the disruption in Biopharma R&D have seldom been higher than today."

"Ashurst has in-depth knowledge of the market, the regulatory requirements and the key success factors when it comes to drive innovation through technology. We help our clients shape the legal framework for tech investments and adapt to a changing regulatory landscape" adds Ashurst Germany's Tech and Data expert and digital economy partner Alexander Duisberg. 

Ashurst has abundant experience in the tech industry and regularly advises on investments in digital transformation projects, technology and data-driven business models as well as in AI, ML and IoT projects. Ashurst is exceptional in combining legal with industrial expertise so that we provide extraordinary value to our clients.