While the early signals of progress for traditional tech companies are fairly well known — product, customers, revenue — biotech companies operate under a very different timeline, metrics, and milestones. Building a company that bridges computer science and biology for drug discovery therefore not only requires a common language between those two worlds, but also means understanding the inflection points that signal value creation — whether for oneself, for other investors, for pharma partners, or for regulators.
For a first-time founder — or a tech founder bringing the tools of engineering to the science of healthcare — learning this language and the associated milestones is critical. Especially since healthcare startups can experience initial success that then slows to a halt once they realize the difficulty of translating their value in the world of therapeutics companies at the intersection of technology and biology.
At first glance, starting as a service seems like the natural way to go for many startups applying computer science to drug discovery. The company has a great new technology that improves drug discovery; and pharma has a huge discovery budget — surely you can create a big business here? So the company goes and gets a few great logos and pilots with the likes of Genentech, Novartis, and Pfizer. The list keeps growing, and the pilots are small, but surely, eventually, the company will one day get big upfronts and royalties. The problem is… those big deals almost never come. The pilots either end up remaining small service agreements — or are so back-loaded that the company will maybe see a small payout in 15 years if the drug becomes a big commercial success.
The economics of these deals do not sustain a venture-backed business because value creation in therapeutics development comes in the later stages of drug development, and rarely (and with great uncertainty) in the earlier stages. Starting as a service is therefore a distraction from achieving the kind of milestones that actually create value in therapeutics development.
When a company makes the decision to develop drugs, they need to make sure they have the team to match. This may seem obvious, but it's not — it means expanding the team beyond tech founders to include experience bringing a drug from the bench (or computer model) to the clinic. When 23andMe decided to start its own therapeutics effort, for example, the company brought on a former Genentech science boss as its Chief Science Officer and Head of Therapeutic Development to build a team of "drug hunters" that poured over the company's trove of genomic data.
Such hires with bench-to-clinic experience have often earned that experience the hard way, through past failures, which means they can more easily and quickly answer questions that traveling through a tech-centric idea maze alone can't: What is a commercially interesting/viable area for the company? Is the data good enough? How do we design a great clinical trial? Will anyone pay for this drug? The experience also helps them to understand when to bring in additional expertise from physicians, key opinion leaders, or patients. While leaders at the intersection of tech and bio are becoming more common, the expertise remains important. So be sure to bring on expert drug hunters that want to make use of technology, otherwise it will be a recipe for culture clash.
First-time founders in this space often hope they can raise a big round with biotech investors based solely on a technology platform, without actually developing assets. In biotech parlance, assets are chemical, biological, or cellular entities that are candidates for further development and potential registration as a new regulatorily-approved treatment. But despite the real excitement around AI-driven drug discovery and other areas, the vast majority of biotech industry partnerships and venture rounds still have at least some assets in their pipeline. If startups do raise significant amounts prior to developing assets, it will typically be because the company is based on very well-understood biology, comes with a leadership team of biotech industry veterans, or both. Pharma partners are similarly accustomed to seeing such assets. Even if they fully appreciate the underlying technology and resulting data, they already know how to value a company with assets.
For a company to create useful drugs, its in silico predictions — predictions made by computers — must eventually be proven out in biology, too. Furthermore, to understand the risk of an asset, potential pharma partners will want to know the primary biological processes that the drug affects. This is so they can better understand and help mitigate the risk around safety and efficacy that otherwise leads to extremely expensive drug failures in the clinic. Understanding the biology deeply can also help optimize a drug for treatment, find biomarkers that allow better patient selection, and suggest plausible new drug combinations.
Wet lab work (i.e., biochemical or cellular experiments) must therefore validate the technology's biological predictions and provide the insight that helps answer "how it works" — especially for black box AI techniques — at least until there is robust evidence in humans that these predictions are consistently accurate. For founders, this means putting in the work to understand: (1) the mechanism of action — that is, the specific biochemical interaction by which a drug has its effect; and (2) the drug's target — the biological molecule that the drug binds in vivo to have its pharmacological effect.
An old adage in tech is that customers don't buy technology — they buy solutions to their problems. For pharma partners, investors, and ultimately, insurance companies and patients, a drug is a product that solves a very specific problem. This is the indication: the label that pinpoints which disease and population a drug seeks regulatory approval to treat.
Because first-time founders in this space love to envision their technology as a broadly applicable platform — a very common mindset in tech — they are often late to choosing an indication. Yet having a specific indication is a key part of the go-to-market strategy of a drug asset; it helps frame the package of evidence needed to advance an asset forward. Furthermore, the plan developed around an asset as a whole not only demonstrates the commercial attractiveness of that asset, but also reveals the business savvy of the team: Has the team been thoughtful about market size, unmet need, competition, coverage, tiering, and so on? A common mistake here is picking an obviously "attractive" indication and letting the indication guide the biology, rather than having the biology guide the indication. Founders should figure out what the advantage of their platform is, and then pick the indications that play to those strengths instead.
A company could identify an indication with great unmet need, and even deeply understand the biology involved — but that doesn't mean much if the company can't also formulate a compound that can become an actual drug. Does your compound have a molecular structure that is compatible with being an effective drug? What might that structure mean for the method of delivery (e.g., pill or injection)? Founders need to know that it gets to where it needs to go; that it will selectively affect the target (not everything else); that it stays long enough to have an effect; and that it leaves when it needs to.
In vitro experiments — those conducted in controlled conditions outside a living organism — can tell you quite a bit about whether a compound is working. But they have limited power to predict what will happen when that compound is administered to a living system. A good use of in vitro experiments is to figure out: Can you get delivery into the right part of the cell even in an assay? Does the hypothesis even show signs of working in the relevant context? Can you rule out some of the obvious alternative explanations? The answers to these questions will give partners or investors some initial proof that the asset might plausibly work, but they are not a substitute for in vivo studies.
Compared to in vitro data, in vivo data is the place to make a compelling early argument that an asset might have efficacy because it is obtained inside living organisms, usually from animal studies (mostly mice but sometimes larger animals). It provides the compelling data that pushes a company further away from bench to clinic — and closer to pharma partnerships or at least smart funding.
Being able to show in vivo data also demonstrates a company's expertise around experimental design. How sophisticated is the animal model you're using? How well do you understand the translational value of your animal model to humans? Does your animal model even exist for your indication, and if so, how predictive is it? Have you shown a dose-response relationship? These questions require real scientific depth to answer well.
Toxicology studies test the damage that a new compound can do to cells or living organisms prior to first-in-man clinical testing. Such tox data is critical in evaluating a new asset, and can take many forms — ranging from the in vitro effect of a compound on cells, to the formal 28-day tox studies governed by FDA Good Laboratory Practices guidelines. These are quality guidelines for enabling investigational new drug (IND) applications, which are necessary to bring a drug to human trial.
The successful completion of formal tox studies is a common milestone in tranched funding, where investment or partnership funding might be released upon achieving defined milestones. Sometimes assets or companies will be funded prior to conducting formal 28-day IND-enabling studies, but early animal tox data can still be helpful for understanding more than just the safety of a drug. The goal is to have some hypotheses about what side effects you might see based on what you've learned about the biology, and where the drug could have off-target effects based on what you've learned about the chemistry and drug metabolism.
We've cured many mice of cancer and yet are still working to defeat cancer in humans; to state the wildly obvious, mice are not humans, and their biology doesn't necessarily translate drugs developed on them to human success. So, if a company is aiming to enter the clinic for their asset — rather than partnering or selling it — then a development plan laying out clinical-trial design in humans is key to demonstrating the viability of the product (not to mention the sophistication of management).
Investors and partners don't want to see that you've outsourced this or assumed your partner will do it — they want to see your own thinking and planning. Quality clinical trial design shows off a thoughtful selection of inclusion/exclusion criteria; clinically relevant (and approvable) endpoints; patient recruitment; and a robust design with appropriate statistical power. It shows that the company and founders can recruit the right level of talent to do a very hard thing right.
For many tech businesses, value creation is somewhat continuous: You have some revenue. You get some more revenue. That revenue grows over time and so does the business's value. In biotech, however, value creation is very discontinuous and is tied to even more particular milestones: strong in vivo data, GLP tox, IND, Phase I data, clinical proof of concept. A company won't get much value from "almost" seeing the data from these milestones.
A successful venture-backed company must therefore be able to articulate the sources of value creation, and when they expect them to occur (especially around fundraising events). These milestones could include, among other things, the filing of an IND, a new partnership, or a clinical read-out for a trial. Companies should show reasonable plans and assumptions about possible deal terms and timelines, because those assumptions reveal a team that knows what it's doing.
Many tech companies may not need to go as deep in intellectual property because they successfully sell a license to their software (or rely on network effects as the moat for their product). When you interact with pharma, however, you are ultimately selling them a product — a drug — and they are paying for the right to manufacture it and sell it worldwide. Without strong IP, the drug's value diminishes greatly. A good IP lawyer can help build a portfolio that protects the chemistry, manufacturing, and controls (CMC) of the compound as well as any novel mechanisms of action or clinical uses. Freedom-to-operate analyses are essential. Weak IP creates existential risk at the point when you most need leverage.
Some things in biotech are relatively capital-efficient: computational work, more in vitro studies, and even GLP tox. Other things are much more capital intensive: running a clinical trial, or marketing a drug.
The capital plan should closely follow the timeline and sources of value creation, and founders should therefore raise enough money to hit the value-creating milestones they've promised, with some cushion. Pharma partners and investors will look to make sure that the capital needs behind achieving key milestones — such as IND-enabling studies, IND, Phase I, and clinical proof-of-concept — are reasonable given the company's plan, indication, and therapeutic modality. The other thing to watch out for is raising too much, leading to unnecessary dilution if it doesn't also put new valuable milestones within reach.
One of the key ways biotech startups earn credibility with pharma partners and investors is by crafting sustainable deals that reflect a company's stage, while allowing the company to grow: not so meager as to undervalue the company, but not so rich as to be unrealistic. A common mistake is agreeing to a deal with a big total value — and a tiny upfront — only to find that the company can't survive the years it takes to hit the milestone payments, and the royalties will only come with a successfully commercialized drug.
Successful founders therefore need to craft deals that not only generate reasonable upfronts but also include realistic milestones and royalties. Companies can continue to build up the magnitude of these payments and royalties as they build up their credibility, too.
Given the capital intensity of biotech investments, syndicates — groups of venture investors — often join together to fund a company in order to diversify their risk and ensure more capital will be available. These syndicates are not random, though, and can reveal a lot about the company itself: the scientific expertise, further capital sources, connections, and other advantages. When potential pharma partners evaluate an opportunity, the quality of investors can be a signal for the strength of the backing pushing the science and the business forward.
The size of a syndicate should also be calibrated to the size of the company; for example, an expensive "big science" platform may require more deep pockets and could benefit by also including crossover investors that specialize in funding private companies in rounds prior to an IPO. Founders should always pick and grow their partners based on who can help create the most value for them and their company.
Many traditional biotech investors and strategic pharma partners criticize startups at the intersection of tech and bio for their valuations, sometimes due to initial rounds done by pure tech investors. This becomes an issue when those startups seek additional capital from those investors/partners. The best advice here is that valuation should be a function of progress.
There's also a common perception from tech entrepreneurs that biotech VCs are less founder-friendly than tech VCs. In reality, the two can work together very well — as evidenced by the growing number of companies sitting at the intersection of tech and bio. But this requires an understanding on all sides. The lesson: focus on the milestones that will create the most value, and let valuation be a reflection of that progress.
When it comes to human lives, every drug must pass the unforgiving gauntlet of human clinical trials to become a successful product, and every company will have to navigate many pitfalls to get there. Biology can humble even the best drug hunters out there, let alone the most leading-edge technologists, in their attempts to create therapeutics that save and improve lives. Avoiding many of these common pitfalls may help bridge the gap for startups straddling these worlds of tech and bio therapeutics. And while some technologies may allow traditional steps to be sidestepped, many of those rules can and should still apply; in this sense, bio is more of an art… and the new masters will emerge from students of the old.