Exploring “TechBio” — what’s behind the hype?
In recent months, ‘Generative AI’ and ‘TechBio’ have become hot topics in healthcare innovation circles (see link for Benjamin Belot’s article on the transformative effects of Generative AI in radiology).
In contrast to “Biotech”, “TechBio” refers to applications of the tech revolution to biology. Biotech and Tech VCs are ramping up their investments in the field to address this surge of innovation. But what is it all about, and why is it becoming hot now? This article seeks to define TechBio, discuss how it may defer from Biotech, and uncover the reasons behind the recent surge of interest.
General Definition and Examples
Let’s start by defining TechBio. This emerging sector combines engineering and computer principles to address biological challenges, paving the way for innovative treatments and diagnostic solutions.
For a clearer understanding of what falls within this sector, see below for a breakdown of the TechBio space with examples of companies:
Decoding TechBio vs Biotech
Biotech focuses on applying engineering (like tissue, and molecular engineering) to develop biological assets, while TechBio companies create tools that have an impact on biology to support and accelerate the development of new drugs or diagnostics.
However, it’s primarily the model of TechBio startups that sets them apart from Biotechs, based on the following three aspects:
Management team
From a management perspective, the typology of entrepreneurs at the helm often differs.
In Biotech, most promising technologies are coming from top-tier academic institutions and rely on decades of discoveries on cellular pathways or new mechanisms of action for example. These innovations are converted into products within biotech startups. Even though expert scientists are behind innovations, they are often joined by experienced CEO, most of the time seasoned entrepreneur, to lead the company’s development. Some biotech funds, such as Kurma Partners, have even opted for the start-up studio model, scouting out promising scientific discoveries from academic labs and supporting formation of management teams. This is what they did, for example, with EmergenceTx, a company recently acquired by Eli Lilly (link).
TechBio companies, on the other hand, are often founded and run (at least in the early stages of development) by young founders with diverse backgrounds in technology, engineering, and biology. Many of these creative founders are first-time entrepreneurs who have quickly identified a market opportunity and carried out rapid iterations to develop a minimum viable product (MVP) and bring innovations to the world.
A prime example is Kurma Partners portfolio company DNA Script. It was founded by a team of young engineers and scientists with the ambition to print DNA on a bench. To reach this goal, the company assembled the best of molecular biology, hardware engineering, and software development.
Platform vs asset-centric Model
Another key distinction between TechBio and Biotech lies in the platform-centric approach of some TechBio companies.
Biotech companies are typically asset-based companies that have a specific initial intellectual property on a new form of therapy/compound.
AI-enabled drug discovery (AIDD) solutions, on the other hand, are often platform companies that focus on a technological approach or system that should enable them to produce several new drugs across various indications. These platform-centric companies are not only defined by their asset pipeline; instead, they are characterized by their robust discovery engines.
However, while AIDDs begin by focusing on platforms, they may evolve into a hybrid of the two models and develop their proprietary asset pipeline. For instance, some AIDDs start with a platform model, generate early revenue by aiding crucial R&D processes for other companies, and simultaneously accumulate the necessary data and expertise to create proprietary assets (co-owned or fully owned). A notable example is Insilico Medicine, which has evolved into a clinical-stage, end-to-end AI drug discovery company and has built a pipeline of drug candidates.
Capital requirements
While the path to market for biotech is capital intensive due to the various stages of technological, clinical, and regulatory development, TechBio companies may prove to be more capital efficient. They can leverage technology to de-risk and bring products/services to market faster with funding cycles/ rounds similar to software companies. This similarity with the software model is probably what caught the eye of some of the more generalist Tech funds out there.
A notable exception could be AIDDs, which need capital for both technology and asset development early on. However, the assumption is that the next generations of hits/candidates will be cheaper to churn out.
Why now?
The TechBio landscape is on the upswing, driven by a combination of technological advancements and shifts in industry dynamics. We’ve witnessed the following key transformations.
Shift in mindset within the industry: design-first approach. Traditionally the drug discovery process involved searching for a specific biological target and a drug that affects it, screening millions of potential drugs. Now, companies can simulate structures and dynamics, virtually screen libraries of compounds, and also lean towards intentional drug design. In novel drug design, they can start with desired properties and use computational techniques to find or design the right drugs.
Increased availability of data:
The cost of genomic data has significantly reduced, making genomic knowledge more accessible and widely used. Companies like Illumina, which played a big role in modern genomics, along with others like 10X Genomics and Oxford Nanopore Technologies, are making it easier to understand complex genetic sequences by developing new methods, including long-read technologies.
It is also now possible to combine genomic data with other omics to better understand the relationship between systems and hence the underlying mechanisms of diseases.
More and more companies are creating virtuous feedback loops by using available data to generate a model and testing the model’s predictions in wet lab conditions…thus generating additional proprietary data that can further improve the model’s accuracy
Artificial intelligence (AI) and machine learning (ML) advancements play a crucial role in analyzing biological data. Recent breakthroughs, in large language models (LLMs) and generative AI (GenAI), enhance our understanding of biological complexity.
Some examples here:
· Cradle.bio uses generative AI to design new proteins by capturing the link between protein sequence and structure.
· We are also seeing a trend towards the development of multimodal foundation models i.e. able to ingest and analyze multiple data sources. For example, EvolutionaryScale wants to go beyond predicting protein structure, as Alphafold does, by developing a multimodal foundation model integrating other biological data from DNA sequences, gene expression, and epigenetic states.
· In clinical trials, we are also seeing the impact of these technological advances, with companies such as Nova Discovery. Through in silico modeling, they can generate insights enabling smaller and more targeted studies. This allows for a decrease in the required number of patients within control groups or the creation of complete synthetic control arms. Recently, they’ve also successfully predicted AstraZeneca’s phase 3 clinical trial outcomes in advance of their announcement with precision and foresight. (see link here)
Automatization: new technologies are making it possible to automate biological processes taking place in the lab. Automata, for instance, is a good example of innovation combining software and hardware to automate scientific laboratories, saving scientists time and effort.
Recent regulatory changes encourage the use of more effective human-based models to test new drugs. The FDA Modernization Act of 2022 no longer requires animal trials, aiming to address ethical concerns as well as the lack of predictive powers of certain animal models. Instead of using animals, companies can now opt for organoids (patient-derived mini organs) in pre-clinical testing. Orakl, a spinoff of the renowned cancer center Gustave Roussy Institute, is building one of the largest biobanks of tumor avatars from fresh patient biopsies obtained through collaboration with the institute. Avatars can be used to discover new targets, increase drug throughput compared to animal models and screen drug efficacy early to mitigate clinical trial risks for Pharma and Biotech. Their approach merges organoid biology, high-quality clinical data, and AI that provide wet-lab and dry-lab insights that capture the full complexity of cancer.
Conclusion
Tech is having an ever-increasing impact on bio, and a growing number of companies are attracting the attention of investors. Unlike Biotech, TechBio startups have models similar to software companies, founder-led and platform-focused, they are often able to generate revenue quickly. This is attracting interest from generalists and technology investors interested in life sciences.
We’ve been in this game since 2015, and we’ve seen an acceleration of innovation driven by a cultural shift in drug development approaches. The focus is on data-driven rather than hypothesis-driven methodologies, propelled by recent advances in AI and ML applied to biology. Opportunities for automation and scalability of tedious processes are generating valuable data, complemented by regulatory support for emerging technologies.
Our Healthtech team is more than ever interested in meeting and supporting visionary entrepreneurs developing impactful technologies in the space. Having a biotech team is also a big plus as we also benefit from their clear understanding of the nuanced requirements of the biotech and pharma industries. Feel free to reach out!
