This piece was written with Ron Boger, PhD student at UC Berkeley and Venture Partner at Compound. Ron’s website can be found here.
The struggles of public biotech markets have led to sector wide reflection. Specifically, there has been a reckoning of TechBio businesses, such as Recursion, Ginkgo, Zymergen, and others. The first TechBio investments made in the mid 2010s are amongst the biggest losers in today’s public markets. Since 2010, TechBio businesses represent 5 of the 10 worst performing IPOs while accounting for jugst 5% of market entrants. Our view is that this collapse of vision has roots in naive forecasting and fundamental misunderstandings of the purpose and nature of utilizing biotechnologies for social benefit.
The thesis then as it is now, is familiar to most. At its core, it is the view that taking an engineering approach to problem solving is orders of magnitude more effective than whatever existing systems are being used. By applying new technologies, drug development can be faster, less expensive, and scalable. TechBio is engineering biology with a tech focused approach. These companies are typically platforms, aiming to have multiple shots on goal, instead of focusing on singular indications. It’s about controlling biological machinery, turning discovery into search, creating engines that continuously optimize until a drug pops out.
Several groups have written interesting articles about the upside and promise of TechBio. Those in venture are quite good at telling the story, building followings, and seeding tons and tons of companies. It’s an exciting time and everyone needs to know about it. How can you not be excited when…
“Bio is being digitized. It has become programmable.”
“The 21st century is for the biorevolution”
“Biology is eating the world”
The costs of drug development continue to climb, reaching up to $2 billion by some estimates. Meanwhile, increasing the size of drug R&D efforts has led to declining efficiencies, rather than more discovery, and new drug approvals have seemingly gotten less exciting – more me-too approvals with lower bars for efficacy and greater leniency for toxicity. It seems that amazing news happens every day in the biotech industry, but we can’t always be at the cusp. When will TechBio and all its promises arrive and deliver on new medicines?
The promises arise partly from silicon valley hubris, the idea that any industry should and will be disrupted by software and machine learning. They arise from real frustrations academics and industry scientists have with existing workflows of doing science. And they arise from a genuine optimism that everyone in the industry must have in order to do good science and persevere through all its disappointments.
However, in general the discourse surrounding the TechBio ‘frontier’ has been too nonspecific to be useful and too oversold versus what we actually have. From using ‘bio’ as a catch all for therapeutics, agriculture, consumer, infrastructure, and more, to even the premise of TechBio being ‘a direct application of engineering to biology’, it is difficult to understand precisely what anyone is saying these days or what separates the real advancements of today from the hopes and dreams of next century. It’s unsophisticated and conducive to hive-mind behavior. When no one knows what you are saying, it is impossible to be wrong. Biology is almost defined by its jungle of exceptions. The sooner we recognize how little we understand and have control over, the smarter we can be about putting the pieces of the puzzle together.
The fundamentals of TechBio rely on the convergence of advances in 3 areas: machine learning, synthetic biology, and high throughput automation. The thesis is that the combination of these advances and the network effects provided from an internal data moat can be strung together in a platform that continuously produces drugs. For discovery outfits like Recursion, the idea was that the platform provides materially higher success rates than industry by building a ‘map of biology’.
Reality has crushed this thesis. For each disease, new models are required, and little of our understanding scales from model to model. The activity of elements in biology is always context dependent, and it is currently impossible to recreate the entire context of human biology let alone between preclinical models of even related indications. Just to get cells to behave and grow in a dish requires them to be exposed to toxic levels of oxygen, glucose levels that are magnitudes higher than diabetic blood sugar, and uncharacterized reagents like fetal bovine serum that can vary from lot to lot and have no reason to be near the cell types they are cultured with physiologically. When cell lines are grown, they accumulate mutations and do not resemble the natural human networks and expression behavior of normal human cells. The reason why there is no ‘standardization’ of cell culture conditions is that any standard would be poor by default. Trying to industrialize this with technology, even just for a single model let alone many models in parallel, is a nightmare and unlikely to work. Finally, improved model systems such as iPSCs and organoids still often struggle to faithfully recapitulate underlying biology and are operator dependent in many cases. The starting cell population used to seed the system is a major contributor to experiment variation and the lack of reproducibility. Biology is not a playing field conducive to systematization.
Furthermore, even if the technological fundamentals of TechBio have progressed, the question of whether they are useful for solving problems in biology is always in question. Machine learning can be useful in certain spots such as imaging, SMILES, or sequence data but its utility past curve fitting and simple linear regression can be unclear for other applications simply because an algorithm cannot learn adequately when the underlying data does not represent the complexity of the problem. For example, our understanding of nucleic acid delivery is still quite poor and whether cell or organ specific delivery is entropy or ligand driven or mediated by other processes is not well understood. Attempting to use machine learning to solve this issue based on the chemical structure of the delivery vehicle thus has questionable value and is unlikely to offer an order of magnitude benefit over existing technology. There are too many disparate components of variation and acquiring all of them is too expensive and infeasible.
Synthetic biology, particularly CRISPR and being able to perturb biological systems is commoditized and can be better done in academia. The Broad Institute, Stanford University, and the NIH among other leading academic centers have constructed publicly available datasets that rival any of those that can be collected in industry. Across CMap, DepMap, ENCODE, TCGA, and many others is a wealth of expression data that provides at least 80% of whatever competitive advantage a next generation synbio screening startup may provide, while the remaining 20% of differentiation continues to be eroded by new projects. Academic centers have cheaper pricing, access to enthusiastic students, and the flexibility to collect data that may not be directly useful to company objectives.
High throughput automation technologies like plate handling and sequencing are useful only when asking relevant biological questions. Here again, screening facilities built into CROs or even organizations like Broad PRISM and NCI-60 already provide enough capacity to ask whether there is a need for TechBio companies to double check their work. Again, so much of problem solving in biology requires custom experiments to get useful data because the dimensionality is so high. CROs are surprisingly cheap and if a team is truly smart about designing experiments it’s unlikely that the type of speedup that high throughput will solve for is a limiting factor. HTS and sequencing progress may prove useful for discovery work, but are there examples of where this is better done in TechBio startups versus academia or large pharmas?
The next generation of PIs in academia and directors in pharma are frequently already up to speed on how to utilize machine learning, bioinformatics, lab automation, etc to modernize their experiments. At the very least, they are willing to try, and academic researchers have a multitude of advantages including scientific freedom, talented cheap labor, and access to collaborators and CROs at discounted rates to execute. The rise of alternative funding sources like Fast Grants, decentralized science grant distribution, and non-profit grants provide additional bandwidth to academic groups who wish to implement new technologies.
Overall, academia and big pharma aren’t poor and will continue to get more efficient as the biology stack improves. Big science projects that were once thought to only be possible with company funding are increasingly done by research centers and consortia like the NIH, Broad, and cross-institute collaborations. Importantly, the first to get a hold of these datasets are the academic centers that create them. They will be first to publish, patent, and start drug development. They will be most familiar with releases of new data, the quality with which data was generated, and have the ability to influence what additional projects will be initiated.
Large pharmaceutical companies in essence function as late stage VCs, hand picking technologies that synergize with existing internal capabilities. As a startup, you are not just competing against big pharma, you are competing against every other startup that pharma has interest partnering with. Big pharma also competes for the smartest talent, providing them higher salaries, access to the most up to date resources, and the opportunity to see their drugs move through the clinic if successful. Pharma scientists are quite smart, nimble, and capable. Large pharmas have the budgets to go after high risk targets and answer questions that no one else can, by directly funding their own clinical trials.
The connectivity of academic and pharmaceutical networks has gotten far stronger over the past few years and the pace of new ideas and findings makes it difficult to start a company off a single piece of technology. Being cognizant of who else is working on similar technologies both in academia and industry will become increasingly important; they are legitimate competitors. Claiming speedups in a startup environment over these players will get harder and harder to justify. Critically, very few startups who have claimed to be able to move faster than their peers in pharma actually have.
TechBio isn’t a novel concept anymore. Given the promises of TechBio being able to accelerate discovery, allow us to learn more about biology than ever before, and discover patterns that ordinary scientists could not, we should expect TechBio companies to continuously generate and communicate new discoveries. Yet, these businesses rarely publish.
Notwithstanding publishing being a good practice of any citizen in science, the ability to put in writing and undergo peer review how a project was done and what the concrete outcomes and objectives were, is a major component of translational research. Participation in conferences, seminars, and other mechanisms of disseminating knowledge and data are essential activities for biotechs of all sizes. At the earliest stages, this may come via academic collaborators and at later stages the expectation is that the business should be generating its own data and communicating important results and discoveries.
The historical lack of involvement from TechBios with the academic community is concerning for several reasons. First, it allows for data driven inflection points and risk to be continuously pushed on to later investors. When poor data is unveiled, house of cards can quickly collapse (see Zymergen), wasting hundreds of millions of invested capital. Second, it removes scientists from any semblance of rigor or accountability when conducting experiments, potentially fueling a reproducibility and utility problem. Operator dependent results and lack of head to head comparison studies wastes capital. Finally, publishing data is simply good for the scientific community. It levels the playing field, ensures what the business is outputting is useful, and fulfills a core responsibility to the scientific community by helping train new researchers and disseminate findings. At the very least, we should expect some contribution to the pool of scientific knowledge if the pitch was good enough to merit millions in platform development funding.
Building a business to address any problem in biotechnology is difficult and as a community, we should recognize this. Foundational IP and publications should be a requirement for starting a new venture; a necessary bar for entry to prevent a flood of uninformed and non-differentiated science. The lack of published data even at the proof of concept level in many recently seeded TechBio companies is a red flag signaling a lack of patience, inability to validate, and poor focus. Publishing data does not deplete one’s first mover advantage if everything is covered by intellectual property, even Google and Facebook publish at rapid pace and in a field even more hyper competitive than biotechnology.
While private investors see TechBio businesses as platforms and thus insulated from risk associated with single asset therapeutics businesses, public markets disagree. Single asset or biology focused therapeutics are risky from the angle that if the hypothesis is wrong, the project gets shut down. Fortunately, capital requirements for these businesses are back loaded and trials are only greenlighted if the risk reward profile is favorable. In contrast, platform TechBio business have both front loaded and back loaded capital requirements. The costs of building, maintaining, and growing platforms can be enormous given that entirely new datasets need to be generated, labs built, and specialized talent hired. Recursion raised nearly $1 billion USD before ever conducting a clinical trial. Single asset businesses require investment for trials but are able to skip spending on discovery costs.
Biological risk is replaced by technical, platform risk which can be equally hard to mitigate. Platforms are fragile. They can lose utility, quickly getting replaced with the shiny next best thing just like any other asset. Before Illumina sequencing, Sanger sequencing was king, and after Illumina, will it be Nanopore or any of the other dozen Illumina killers that eventually topple the platform? Before, mRNA vaccines, DNA vaccines were mainstream, and maybe after a couple years will circular RNA or other types of RNA begin to replace mRNA? Zinc nucleases before CRISPR, and retrons after. Yeast display, then machine learning directed evolution, then phage assisted continuous evolution, and then what next? Platforms require longevity to pay off the costs of set up. If the pace of progress in biotechnology really is as fast as everyone says it is, platform moats will quickly disappear, as quick or faster than they materialized.
A common argument in favor of platforms is the idea that flywheel and network effects create a moat that allows TechBio companies to continuously develop new drugs, thus justifying platform costs. There is no proof of this. Even if it were true, it ignores the reason why we want drug companies to exist in the first place. The goal is not to approach drug development with a hammer looking for nails. A platform technology may only be (and often is) optimal for just a single indication and drug developers should be respectful of this. To remove biological risk, platform companies often opt for entering crowded indications with a multitude of existing approved or late stage assets to compete with, resulting in a bloated set of undifferentiated assets that can’t all be deployed. New drugs have known toxicity and unknown benefits and it is a net harm to society to develop them if we do not have high confidence that they will be practice changing. Unlike other industries, there are limits on consumption in healthcare; there is no hockey stick growth. Richard Murphey from Bay Bridge Bio insightfully points out that,
“[The] quality of R&D programs is more important than quantity. Most hits that come out of a screening platform are NPV negative. If quality is low, then the value of a platform decreases with scale. A valuable platform is one that can find a small number of valuable programs, not a large number of mediocre ones.”
The sad promise of platform TechBio therapeutics businesses is that the platform is worth anything meaningful at all. In any platform, value is almost entirely concentrated on the lead assets. There is the argument that TechBio platforms are derisked due to multiple streams of revenue from upfronts, milestones, and royalties. This can be true for services providers comfortable with reasonable capital outcomes, but as a discovery unit aiming for homeruns, your paycheck is directly proportional to whether you can develop drugs, no way around that. Signing away the economics of your most promising assets and the killer applications of the platform is no way to justify the expense of building the platform.
The reality is that great companies are built in a multitude of different ways and there is no use forcing excess technology onto problems if it does not require it. A curious phenomena is that the further removed one gets from the earliest rounds of investment, the quieter the table pounding for TechBio becomes. Once you start thinking about valuations, milestones, and concrete business outputs, the ‘promise, potential, and power’ of the platform takes a back seat to things that clinicians and patients actually care about. Let’s retire the monikers and be more specific and precise when talking about the businesses we are building and why they are exciting.
Achieving a reasonable valuation and growth trajectory is about ignoring frenzied expectations and not being afraid to hit singles and doubles. Markets are more segmented and always smaller than expected with greater than expected competition. It pays to be leaner, take your time in a sandbox environment like academia, and be focused and honest about the limits and benefits of a technology and the rationale in turning it into a company. These must be businesses one day; at some point, data needs to translate to free cash flows.
It is unfortunate that founders can be compelled to shift out of a product focused mindset in order to raise money from platform obsessed funding sources. The point of building a platform is to graduate into a products business and if you can graduate early, you should! There is a notion that every company has to have a shot at being the next Genentech, a generational enduring company that continuously churns out blockbuster products. In reality, I think the point is to deploy capital in the most efficient way to help patients. It should be the expectation to be nimble and pick the low hanging fruit that academia has provided. Continuously starting, building, winding down once patients see benefit, not spending a penny more on wasteful development, and recycling the capital back into the ecosystem is a terrific result. Overall, there is no right way to do drug development and while TechBio shouldn’t be dismissed outright, it certainly isn’t a panacea. Any thesis is about picking winners; TechBio and Biotech are one in the same.