Understanding Technology Convergence: a Case of Moderna

Before we proceed to the article, there is a little announcement: I have launched a Substack newsletter called ‘Where Tech Meets Bio ,’ which will be an experimental format for myself -- more personalized, with a wider horizon of topics, and more focus on practical applications of biotech to everyday life (not only industry analytics). Join our community on Substack!?

Back to today’s topic.

In order to understand a specific technological trend or a particular company’s growth potential, I am often using traditional consulting techniques, common in the consulting space: SWOT analysis, PEST analysis, Porter’s five forces, the 3 Cs, formal benchmarking, the 4 Ps, BCG growth share matrix, and some others.?

As an aspiring consultant, however, I came up with my own analytical framework. I am using it to answer three main questions:?

• Identify and reflect on growth/innovation trend drivers in certain scientific niches
• Reason growth potential for tech-driven drug discovery and biotech companies and explain it to clients (and to myself)
• Guide my writing and analytical work (who is going to be the next industry disruptor and why?)

?

So, what’s the framework? I call it the FFS framework.?

It is based on the idea that technologies can roughly be divided into foundational and functional categories. Foundational technologies are general-purpose, applicable to various aspects of the industry, or even different industries. A good example is a family of deep neural networks (DNNs), or a more specific example -- Transformers. Some large language models (LLMs) are based on transformers.?

DNNs are applicable to a multitude of tasks in drug discovery, biotech, and far beyound, and so it is foundational tech. Other examples include quantum computing, virtual reality, etc.

In contrast to foundational technologies (which are mostly digital, data, or hardware related), there are functional technologies, peculiar to a specific field of science or application use case, For example, Cryo-EM is a typical functional technology (in my view), which is focusing on predicting 3D structures of molecules. There is not much you can do with it beyond structural biology, or structural chemistry, but it is powerful when it comes to its function.?

The combination of functional technologies with foundational technologies often leads to disruptions in the relevant fields and new applications of old technologies. Obviously enough, combining good-old nex gen sequencing (NGS) with deep learning algorithms may lead to the next level in target identification, gene editing, diagnostics, and myriads of other use cases and novel areas of research.?There is a temptation to call NGS a foundational tech, since it is so broad in its applications...

Finally, there is a third crucial component for a successful growth: scalability infrastructure. Both functional and foundational technologies must be compatible with some technologies that would allow running things in high throughput mode, or on a large production scale.?

For example, let’s say there is a novel assay for preclinical research (functional technology) and it can be rapidly analyzed using deep learning techniques to give unique insights. Is the new assay compatible with lab equipment, hardware/lab software? Can it be done at scale? Those kinds of questions might not be very important for a scientist to run a proof-of-concept study, but they will be crucial for a company to scale to commercially meaningful results.?

Below is a simplified representation of FFS framework for company/industry analysis:?

Let’s review the recent success story of Moderna and apply the FFS framework in action (here in a very simplified manner, just to illustrate the idea). Below is a chart of a possible FFS analysis, where quadrants I, II and III represent functional, foundationa, and scalability technologies, respectively, and I-II, I-III and II-III represent tech compatibility considerations (is a given technology scalable at all):

Moderna rose to prominence after it managed to come up with mRNA vaccine as their answer to the rapidly rising COVID-19 pandemics, and they did it really fast. They also apply their platform for creating therapeutic vaccines. From the technological point of view, their success can be explained by an efficient match of functional, foundational and scalability technologies.?

Their core functional technologies are mRNA and the corresponding delivery system based on lipid nanoparticles (LNPs). Let’s roughly simplify it to just mRNA technology for the purpose of this example.?

Next, they apply an organization-scale digital platform that integrates all R&D into a data-centric workflow, including analysis of target proteins, design of corresponding vaccines in silico and automation of various experiments down the line. They are using cloud-based data infrastructure to connect the dots between all the stages of R&D pipeline and they are known to be a “digital-first” company (read their case study ‘How Building a Digital Biotech is Mission-Critical to Moderna’).??

Finally, the critical element is the ability to synthesize RNA. It is not a unique technology to Moderna in general, but this technology is available to them, scalable and they integrate it into their business model:?

In summary, the timely and efficient combination of digital approaches (foundational tech) with state-of-the-art mRNA discovery (functional technology) and scalability infrastructure (high throughput mRNA preclinical production facility, cloud infrastructure for operating data flows company-wide, automation), of course, combined with urgent market need, led to the emergence of one of the most successful biotech stories in the history of this industry.?

On a more general note, using the FFS framework allows us to quickly understand areas of disruptive potential, where we can expect a lot of research and a wave of novel startups. If we have growing new Functional technology or an improved variation of legacy technology, and it can be enabled using one of the foundational technologies (e.g. deep learning, NLP, computer vision, quantum computing etc), we can certainly expect the area to grow.

Take metabolomics + machine learning and we have room for disruptive drug discovery and diagnostics startups. Take organ-on-a-chip + machine learning and we have next generation preclinical research models, etc.?

---

Welcome to Biotech Oracle's newsletter, "Where Technology Meets Biology." I am sharing noteworthy news, trends, biotech startup picks, industry analyses, and interviews with pharma KOLs. Contact me for consulting or sponsorship opportunities here or at www.BiopharmaTrend.com. Shop world-class chemistry for drug discovery at www.enaminestore.com.

Enjoying the newsletter? Subscribe to become part of 6800 readers here on LinkedIn. Please help us spread the word by sharing it with your colleagues and friends.

Also, consider joining my Substack community (3600+ industry professionals).

-- Andrii