Opportunities in Bio 2.0
The last part of my series is about the opportunities that “software eating bio” creates. Opportunities are plentiful in this brave new world. Due to the exponential advancements we’re seeing in processes, tools and cost, biotech is experiencing the same kind of dynamic as digital technology at large. All this fundamentally changes the economics of starting up in Bio 2.0.
This is the final part of my five-part-series about exponential technologies in healthcare. Post 4 explains how data processing and machine learning enable a new kind of medicine. In part 3, I’m looking at the gene editing technology CRISPR. Read in part 2 how the exponential development in genome sequencing has resulted in an explosion of genome data. The series intro puts all of it into context and explains my hypothesis that the convergence of several exponential technologies in healthcare represents the largest opportunity of our time.
All tech startups benefit from open source software and hardware. It provides great tools as well as cheap computing and storage to develop and run products. Startups don’t need to spend tens of thousands upfront anymore and can instead pay and scale as they grow.
The same is happening to biotech as it becomes more tech. Speed and cost of tooling is dropping rapidly (see my the posts on NGS and CRISPR).
As more biology becomes digital, it reaps the benefits of tech in computing and storage. We’re seeing the first guard of startups that are building on top of these developments. They’re trying to solve large problems across the pharmaceutical industry and healthcare delivery.
- Drug Development: Companies such as Benevolent AI are applying AI to that problem.
- Patient-Therapy-Matching: Among other startups, NotableLabs stands out. They us the molecular fingerprint of a patient’s brain cancer to match her with a combination of approved drugs in order to improve outcomes.
- Pre-Clinical Development: Vium is automating large parts of the pre-clinical work with mice in order to make it more scalable, cheaper and more reliable.
Other companies are providing the tooling for Bio 2.0.
Things get easier over time, as open source tools are developed to help with task automation. One interesting recent example is Kronos “a software tool for automating reproducible, audible and distributable bioinformatics workflow development.”.
Other startups abstract away the actual lab by providing Lab-as-a-Service products. One example is Transcriptic that develops automated labs (work cells) that can be booked and programmed remotely. As a sidetone, they’ve recently added CRISPR gene editing capabilities. Just as Amazon’s AWS allows you to run large applications from your home without owning a server, these kinds of companies allow you to do biotech without owning/running a lab.
Solutions coming out of Bio 2.0 change the actual practice of care delivery. New processes need to be established and managed in order to take advantage of molecular diagnostics and machine learning.
After all, the routine use of molecular diagnostics and data matching, needs to be connected to existing systems such as EHR or Lab reports. Companies such as Syapse, Foundation Medicine or Flatiron Health provide such services.
I believe that major opportunities are available for biotech companies that have software at their core. Thanks to convergence in the different areas of traditional life sciences, it has become increasingly cheaper to start up and scale. Additionally, more data is making the software component more relevant, moving biotech closer to the algorithmic world.
Most people studying molecular biology need to understand computer science now anyway. This enables them to address problems “full stack”. In startups biologists and computer scientists are sitting side-by-side. In large companies this is usually not the case (yet).
As Vijay Pande says: Biology in 2015 is like software in 2005. And just imagine what happened in software over the last 10 years!