Discover more from Happy Warrior by Seth Bannon
Biotech in the Garage
Much like the costs of sequencing the human genome,1 the cost of founding a biotech startup is dropping precipitously.2 If current trends continue, biotech companies will soon be founded in garages, funded off their founders’ credit cards.
The exact same trend that happened in software over the last decade and happened in hardware over the last 5 years or so is happening in biotech now. A smart software developer can build and launch a web or mobile app and get paying customers for under $2,000. The same will soon be true for biotechnology.
Here are a number of trends that are fueling the steep drop in the costs of founding a biotech startup.
1) Biohacking spaces & shared wet labs.
Research intensive companies used to have to either grow out of a university lab or spend quite a lot of capital on their own lab. Now, they can get off the ground in biohacking spaces like Counter Culture Labs or Biocurious and, once they’ve matured from project to company phase, cut costs by joining a shared wet lab like Harlem Biospace or Qb3. There, in exchange for a monthly fee resembling that of a co-working space, they have shared access to cutting edge equipment needed to incubate cultures, grow tissues, and much more.
The Real Vegan Cheese project,3 born out of Counter Culture Labs, is an example of what can be done for very little money. Through genetic modification, they’re very close to getting yeast to produce milk through the process of fermentation instead of alcohol. They funded the project with ~$37,000 that they raised on Indiegogo.
2) Better tools.
The primary thing that dropped the cost of building software companies was access to better and cheaper tools and services. The quintessential example is Amazon Web Services, which allows developers to host their servers in the cloud, meaning they no longer have to invest in expensive and difficult to configure hardware (servers).
The same thing is happening in biotech. Tools like Transcriptic are essentially AWS for the life sciences – a cloud laboratory accessible through an API. OpenTrons offers affordable robots for wetlab automation. Mousera is Heroku for mouse studies, and helps automate much of that process. Google launched Google Genomics, which allows researchers to run complex computational analysis and store their data in the cloud. Benchling drops the cost and speeds up the pace of research by automating many tasks (like counting bacteria colonies) that were previously manual.
Other tools are helping solve the problems of unused equipment capacity. When companies had to buy their own servers, they would often sit at 15% capacity for most of the time – a terribly inefficient setup. As things moved to the cloud, the entire system became more efficient, dropping costs for everyone. The same thing is happening in biotech. Companies like Science Exchange allow researchers to outsource their experiments to university and commercial labs that have excess capacity. On the site whole genome sequencing goes for around $1,000. Others like Synaptic allow universities that have expensive research equipment sitting idle to more effectively lease time on the equipment to researchers that need it – be they academics or entrepreneurs.
Other tools are being developed that will eliminate the need for animal (and maybe even human) testing of new pharmaceutical treatments. Emulate, for instance, is a DARPA supported company that is creating living systems on a chip that can be used to test drugs with more predictive results than animal testing. In-vitro techniques are being used to grow human skin which can be used to test new topical treatments.
Tools like these allow researchers to cut both the time and capital cost of research oftentimes by orders of magnitude, and the tools space is only just heating up.
3) “Open source" biotech.4
One of the factors that contributed most to the cost of software falling was the advent of open source software. Software companies make use of freely available tools – from server technology to front end frameworks – that speed up development time and reduce overall costs. The same thing is starting to happen in biotechnology.
There are more and more freely available open source datasets that allow biotechnology researches to skip the collection step, which can often be incredibly costly and time consuming.
The most famous and the largest such dataset is the Human Genome Project, which mapped and made publicly available a complete genetic blueprint for building a human being. Another model example is the International HapMap Project, an open source catalog of genetic variants that occur in humans, on top of which researchers can find connections between specific genetic variations and certain diseases. Amazon freely hosts human DNA sequence datasets, microbiome DNA sequence datasets, and many more collections of data that people can experiment with easily and cheaply.
The increasing abundance of freely available life science data will enable biotech entrepreneurs to move faster, cheaper.
Simultaneously, the open access movement in biomedical publishing is gaining steam. Organizations like PLOS and PeerJ are leading the way here. A world where life science scientific papers are widely available for no or little cost is within sight, which may help spawn a new generation of scientist-entrepreneurs.
4) Software + biotech.
More and more research that previously necessitated capital-intensive labs can now be done computationally. The best analogy here is the impact CAD tools had on the process of developing a hardware product. CAD allows hardware designers to test a multitude of different design variations without incurring large time and capital costs. Software is transforming the life sciences in much the same way.
Software and AI are inserting themselves into more and more parts of the processes of biotech, reducing costs and timelines dramatically. One of my favorite examples of this is the startup Atomwise, which uses deep learning to discover promising drugs, making one of the most costly parts of the drug discovery process more capital efficient.
These techniques are even more important in drug discovery than in hardware. The chemical space in drug discovery – the ensemble of all possible molecules that are potentially interesting – has been estimated at 1060. This is a tremendously large number that is only truly navigable through computational means. By enabling pharma companies to more intelligently choose which candidate drugs to bring to trial, tools like Atomwise will speed up the process and reduce costs in a significant way.
5) New sources of funding.
Traditionally the only checks available for biotech commercialization were very large. There haven’t been “seed stage” biotech VCs as we understand the term. This means that it’s hard for “hackers with a crazy idea” to test some of their assumptions unless they could self-fund. This too is starting to change.
The exact same thing happened in hardware. In 2008 when fitbit raised only $2M in their first round of funding, many were very skeptical that a company making a hardware product could do very much with such a small amount. Last month, they IPO’d after having raised only $66M in total.5
We’re starting to see more seed-stage VCs focusing on biotech, and it’s now possible for early stage companies to enter biotech-specific incubators like IndieBio that offer both small amounts of funding and access to expensive wet lab equipment. Y Combinator, the world’s most prestigious startup incubator, is funding more and more biotech startups. In the last graduating batch, there were 18 biomedical companies, and these numbers are likely to only increase. This trend is both a result of some smart investors recognizing the above trends and will also help fuel them, by allowing biotechnologists to raise a small amount of capital to reach key milestones and prove some assumptions in a low cost way.
Sites like Experiment offer nontraditional sources of funding for researchers in the early stages. One example of what the future might hold is a project aimed at discovering a new treatment for Ebola, which recently raised $140,000 in crowdfunding. Before launching the $140,000 campaign, they first launched a $5,000 fundraising campaign, which gave them enough capital to prove some assumptions cheaply by running experiments through Science Exchange. Only after getting positive results did they decide to raise more for the next phase. This lean setup – raising a small amount to test assumptions cheaply then repeating the process with increasing amounts of capital – will likely become more common over the next decade. This mirrors what happens now with software but has only recently become possible in biotech because of the trends mentioned above.
From consumer to SaaS to hardware – it’s always interesting to imagine what form the most exciting innovation will take in the next decade. For the reasons above, I can’t stop thinking it’ll be biotech.6
We’re not quite there yet, but in the near future biotech entrepreneurs will able to get their companies off the ground from their garage, funded off their credit card. It’s interesting to imagine the wave of innovation this will make possible.
1 The costs of sequencing the human genome are falling far faster than Moore’s Law would predict, as shown in this chart.
2 The one notable exception here is pharma and specifically the cost of taking a new drug to FDA approval, which has jumped from around $800M in 1999 to $3B to $4B in 2012. That said, some of the trends mentioned in this post should play a big role in dramatically cutting those costs.
3 Wired published a great article on the Real Vegan Cheese project that’s worth a read in full.
4 ”Open source” is not the most technically accurate term for the phenomenon I’m describing, especially as it’s used in the software world. It is close, though, and there doesn’t seem to be anything better. Yochai Benkler uses the phrase “nonproprietary peer-production of information-embedding goods”, which is technically more precise but doesn’t make for a good blog post header.
5 The Fitbit IPO returned one of their early investors (Foundry Group) over $1B. When an industry is getting increasingly capital efficient increasingly fast, it certainly pays to spot the trend and put money to work early.
6 Biotech is a broad field. While I think costs will drop across the entire market and many sub-areas will see much innovation in the next decade, synthetic biology in particular seems to offer the greatest promise.
Thanks to Max Hodak, Matt Owens, Cindy Wu, and Alexander Levy for sharing their insights on these trends and to Ela Madej for reading a draft of this post.