Through our interactions with biotech founders and teams, something we’ve heard often at Kaleidoscope is how challenging fundraising can be. This is especially true in markets and climates like today, where early-stage deals are being scrutinized more closely. Knowing what investors look for at various stages is not always clear, which presents challenges for both aspiring founders looking to get going as well as early stage teams looking to grow. However, innovation shouldn’t slow during tough markets, especially in a field like biotech where new discovery means new cures and technologies that benefit human and planet health. And for companies that can find a way to progress, tough markets can also be a powerful forcing function to sharpen thinking and drive efficiency.
To help bring some visibility to the process of biotech fundraising, we decided to crowdsource insights from the other side of the table. We asked four active investors in the therapeutics space – Adam Goulburn of Dimension Capital, Pablo Lubroth of Hummingbird Ventures, Patrick Malone of KdT Ventures, and Amee Kapadia of Cantos — what they look for in companies across different fundraising stages, and the common issues that arise when they’re doing diligence. For context, these investors have backed companies like Enveda Bio, Eikon Therapeutics, Kallyope, Terray Therapeutics, Tavros Therapeutics, NewLimit, Kernal Bio, Azitra and Sixfold Bio.
Here, we cover both milestones that investors look for by stage (and for platform vs asset companies), as well as common issues that arise during due diligence. We hope this post can serve as a quick reference guide for biotech teams building in the space, and provide insights into some of the main frameworks that investors use to analyze investment opportunities.
Seed-stage expectations (~<$15m)
Having a data moat
Adam from Dimension outlines their firm’s approach to assessing data from various angles, with increasing levels of sophistication across these categories over time:
"We're a firm and fund that invests at the intersection of technology and life sciences. When we invest in "biotech" we focus on platform companies that are computationally driven with a technological and data moat. The biotechs we partner with are created with biologists, chemists and software engineers from the earliest stages of ideation. With that context, some of the factors, but not exhaustive, we review on the data side are:
The data itself: what modalities, which are proprietary or defensible moats, how is it generated.
The data team: how is the team organized and who leads, what are the team's collective skillsets, and which are missing.
The infrastructure stack: internally vs externally built; if externally, which tools and platforms were selected and why. Examples include data storage, sharing, pipelining, analyzing etc, the cost of the data stack, and how it scales with the organization.
How data is used to drive decisions throughout the entire organization, not just on the R&D side. With respect to the R&D side, data-driven KPIs and metrics will be very company-specific. Some examples include: number of experiments per week, cost per experiment, cost per x parameter, cost per screen, amount of data generated, cost to IND, predicted value per asset, NPV for existing pipeline, predicted productivity of the platform.
We diligence these factors consistently regardless of whether the opportunity is a new incubation, a Seed, Series A or a later stage company. Measuring the maturation and sophistication of each of these factors helps us understand the technology and data risk we're underwriting for that stage of investment.”
Amee from Cantos offers some words of caution to founders trying to predict how much information they need to generate or should share:
“Therapeutics is tricky because there's never enough data and there's never going to be enough data. Even if you have positive validation from human in vitro models and in vivo animal models, there's always something more that would be nice to see.”
Platform validation — specific to platform companies
Pablo (Hummingbird) and Patrick (KdT) both mention platform validation and benchmarking; that is, the ability to demonstrate both known and novel biology:
“We usually look for validation of the platform. Which entails having run it at least once. For instance, if the company is optimizing viral vectors, we'd like to see that i) the company has built its proprietary in silico methodology and HT wet lab assay to screen vectors, ii) have run it and iii) those runs have yielded something that wouldn't have been possible without the platform. In this case, it could be that they found a capsid that has better tropism for a specific cell type. The metric by which success is measured will depend on the platform's objective (e.g. % higher tropism for a particular cell type if its a delivery platform, % higher affinity for a small molecule or % better bioavailability)” — Pablo from Hummingbird
“[Some key things to aim to include are] validation/benchmarking data for the platform; ability to validate known biology; data demonstrating why this platform should exist and why it is able to deliver unique insight for developing or delivering therapeutics with better safety and efficacy profiles; [and a] first pilot/LOI with a biopharma partner.” — Patrick from KdT
Lead and target identification — specific to asset companies
It may sound simple, but Patrick from KdT mentions the importance of identifying your lead indication and target:
“[The primary things to strive to show are] identification of lead indication and target, preclinical data generated demonstrating target involvement in disease, and ability to modulate target to improve disease phenotype. The goal of Seed financing is to get a development candidate for the lead program before the Series A.”
Team and opportunity
Amee from Cantos mentions that, apart from the technical side, there are the core components of team x opportunity (as with any early stage company, biotech or otherwise):
“In the earliest stages, there isn't a whole lot to dig into. The big question is "is this a world-class team working on a massive problem with the right motivations and tech foundation" There's technical diligence to do on whether the approach to solving the problem makes sense, doesn't break laws of physics, and can scale but a lot comes down to the founders, their ability to hire and execute, and the market. We'll get into the specifications and economics of the technology and the go-to-market plan but at pre-seed and early seed, those things are almost always going to change so those questions are more to get a sense for how founders think about the opportunity than something to base all of diligence on.”
Series A expectations (~$30-50m)
Knowing what indications to target — specific to platform companies
Pablo from Hummingbird notes that the Seed-Series A shift for platform companies should correlate with increased clarity on target indications:
“When raising an A, I expect [platform] companies to have a robust idea on what indications they will target and the strategic rationale as to why to prosecute those specific ones (unmet need, lack of competition, platform-disease fit.. etc). All companies we've backed at this stage also have in vivo data showcasing efficacy and safety in one or more animal models for more than one indication.
We’d also look for: clear indication that at least some of the assets originated due to the proprietary platform - and that finding new ones is replicable - and FTO in all IP (above and new chemical matter and additional platforms)”
Delivering value to pharma — specific to platform companies
Beyond indication clarity, Patrick from KdT calls out the importance of starting pharma conversions:
“[We’re looking for a] track record of delivering valuable insights to pharma partners, ideally converting one pilot to larger partnership with attractive economics (i.e., not just fee-for-service).”
Development candidate, competition, and commercial attractiveness — specific to asset companies
In contrast, Patrick remarks that asset companies should be raising their A to back a lead program development candidate:
“[In the ideal scenario, you would have a] development candidate for [your] lead program. The goal of a Series A is to complete IND-enabling studies and get to clinic. In addition to evaluating the quality of preclinical data, investors will focus on competitive positioning of lead assets relative to standard of care and clinical pipeline, as well as the commercial attractiveness of the lead program.”
Series B expectations (~$60-100m)
In vivo data — specific to platform companies
Pablo from Hummingbird comments on the importance of having multiple shots on goal, if you’re a platform company:
“[What we’re looking for at this later stage is] multiple programmes with in vivo data in several animal models with at least one in the final stages of IND-enabling studies or at IND stage.”
Partnerships pipeline — specific to platform companies
Patrick from KdT notes that a more mature stage of funding means more mature expectations on partnership deal size and economics:
“[You should aim for] at least one biobucks partnership: sizable up-fronts, milestones, even royalties, [as well as showing progress towards a] robust pipeline of additional partners in late-stage negotiations or pilots that are likely to convert to larger partnerships. [Lastly,] data demonstrating platform expansion to additional indications, modalities, etc to expand partnership TAM.”
Clinical data — specific to asset companies
When asked about asset companies, Patrick kept it short and to the point:
“It’s all about clinical data. Safety and efficacy.”
Common issues that arise during diligence
The diligence process is a founders’ opportunity to instill confidence in investors. This is the chance to go deep: into the data, the team, the vision. Here, knowing what not to do is just as important as knowing what benchmarks investors want to see.
Data inaccessibility and lack of context
Lack of data organization can be a major point of friction in the fundraising process. For Adam from Dimension, this is not only a blocker in the diligence process, but a signal that there’s likely a bumpy road ahead for the company more generally:
“I think any biotech today that has a muddy data strategy where the data isn't easily or readily accessible is playing catch up or will eventually run into roadblocks. It isn't just for investor diligence but more importantly for key decision making internally. If we experienced something like that it would not only be challenging to evaluate the company but it would no doubt mean that the company would struggle to put bat on ball with any of the data diligence questions below.”
Pablo from Hummingbird points out that a lack of thoughtfulness around data accessibility, a lack of willingness to share data, or a lack of context behind the data all create problems:
“Documents are typically scattered across a data room, usually in powerpoints which are hard to follow without verbal context. There is also no temporal context for experimental read outs: which experiment led to the next one for that specific asset.”
Prioritization of development and partnerships
Patrick from KdT frames it in the context of asset vs platform, and remarks on the importance of being thoughtful about balance and timing:
“The #1 question and issue I run into when evaluating or working with therapeutics companies is the question of how to prioritize platform development and partnerships with internal programs. [The two extremes are] single asset company with no partnership strategy vs platform company with partnerships and no internal programs, but obviously most companies will be some combination of the two. The relevant milestones for a given company will depend on the exact combination. In my opinion, the best value capture mechanism for biotech is still wholly-owned assets. For platform companies, the biggest issue I've seen is that companies discover too late the importance of an internal pipeline strategy, By the time they start building out an indication/pipeline strategy at the Series A/B, it can make fundraising challenging with some investors that expect to see preclinical or even clinical data for lead programs.”
Technical risk profile
While not necessarily a mistake that founders are making, Amee from Cantos notes how uncertainty combined with more binary outcomes affects risk profile/appetite:
“When developing a new therapeutic, you're not just scaling a validated sales motion, you're introducing uncertainty and complexity at every step that could potentially be program-killing. And reviving a project when dealing with biology and the timeline of live cells and regulatory processes is not trivial. That being said, the upside is massive though the risk profile is very cognizant of that fact.”
Disagreements on milestones
Patrick from KdT speaks to the importance of knowing your audience: founders should understand how different investor profiles think about technical milestones:
“There is often disagreement amongst tech/techbio and traditional therapeutics investors about milestones. Obviously tech investors skew more platform and traditional therapeutics lean more internal programs. We at KdT fall somewhere in the middle.”
As with everything in the venture landscape, each therapeutics investor is different, with their own thesis, benchmarks, and frustrations during the diligence process. We hope that outlining these concrete examples from Adam, Pablo, Patrick, and Amee brings a little more transparency to the process and provides some food for thought for any early-stage therapeutics teams looking to raise their next round.
If you’re interested in finding out more about how Kaleidoscope helps biotechs with some of these pieces, such as tracking and communicating data-backed decisions, let’s chat!
A big thank you to Adam, Pablo, Patrick, and Amee for their contributions!
If you want to chat more about anything we wrote, or you’re interested in finding a way to work together, let us know!