Kiersten Stead, Managing Partner of DCVC Bio, on Deep TechBio company creation

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Dr. Kiersten Stead, Managing Partner of DCVC Bio

To kick off our second season, we spoke with Dr. Kiersten Stead, Managing Director of DCVC Bio.

DCVC is a venture capital firm based in the West Coast that focuses on deep tech investing, and has over $3B in assets under management. DCVC Bio is the bio focused fund of DCVC, where Kiersten invests in life science companies that have a deep-tech advantage, principally in therapeutics, agriculture and synthetic biology.

Kiersten has been closely involved with several leading biotechs including Abcellera, Umoja, Creyon Bio, and Plexium. Previously, Kiersten was the Investment Director of Monsanto Growth Ventures, and received her Ph.D. and scientific training in the University of Alberta, an MBA in Finance from the University of Alberta, and a bachelors degree from the University of Calgary.

In our conversation, Kiersten and I chatted about:

• Her career journey and advice to budding bioentrepreneurs
• The nature of computational drug discovery, and how to differentiate it from traditional biotech
• What investing in computational drug discovery looks like, including the differing strategy and capital needs of such platforms
• What company creation and building looks like for Deep Techbio platforms, with a focus on the attributes of great start-up teams

[If you’re familiar with TechBio, feel free to skip this section. If not, read on! Traditional drug development has long timelines, high failure rates, and high costs, where drugs are serendipitously discovered by examining if compounds stick to a target or if knocking out genes impact biology. Tech-Bio is an engineering-led approach of computational drug discovery, using computation and often machine learning to systemically identify new targets and design new drugs. This means better predictability, reproducibility, and productivity of your R&D — finding new medicines better, faster, and cheaper. To learn more, please consult this post and this article.]

3:00 to 5:10: From aspiring fighter pilot to leading VC

• On her path to scientific training: Growing up, Kiersten dreamed of becoming a fighter pilot, but re-evaluated as she started pursuing science in high school, college, and ultimately a Ph.D. in molecular biology. During her training, she was exposed to company creation from scratch, and realized she wanted to get deeper into research development. She then picked up an MBA in Finance to deepen her skills in commercializing academic lab spin-outs.
• On straddling scientific operations and VC: While her initial goal was to lead scientific teams in a start-up, Kiersten was recruited by a top VC. She found new joy in the broad lens afforded by VC investing, given its unique blend of an expansive view on cutting-edge technologies combined with an understanding of the business side of biotechnology.

5:10 to 15:20: What is Deep TechBio (computational drug discovery) investing? What does company build look like for these platform companies?

• On computational drug discovery investing: Kiersten looks for “swing for the fences” approaches. On one hand, that could be pipelines of products anchored in truly novel biology (e.g., Umoja Biopharma for in-vivo CAR-T treatments). Alternatively, it could be platforms combining physics, AI, computation, and engineering to develop medicines faster, better, and cheaper.

“What we look for are teams who have a deep insight on a computational or engineering approach that is fundamental to the modality, that can help out predict out some feature of drug development that adds considerable value.”

• On building TechBio platforms: Kiersten explains that such computational-bio platforms require highly diverse skill sets from the beginning, translating to hiring plans focused on more technical roles. From a capital standpoint, she has seen such companies license in additional technologies to build the full suite of capabilities. And from a development perspective, Kiersten expects TechBio start-ups to dedicate time and resources to prove out, validate, and benchmark the platform prior to pursuing lead candidates.
• On the implications of TechBio build on capital needs: The trade-off that Kiersten acknowledges is the greater front-end investment of both time and capital needed to build out the platform and requisite expertise (versus a more traditional product-focused biotech). In turn, this can mean longer development time to market. Kiersten also warns of the “parasitization problem” faced by platform companies, where there is a risk of funneling all capital into lead candidates which can short-change the continued development of the platform.

“Platform companies can become parasitized by their own lead programs.”

• On an example computational drug discovery company: Kiersten highlights Creyon Bio, which generate effective and non-toxic oligonucleotide-based medicines (OBMs) to treat ultra-rare diseases. When first investing, Kiersten realized the upfront build would be dedicated to generate a purpose-built, computational platform that can develop non-toxic OBMs, a key hurdle in this therapeutic modality. Rather than develop efficacious OBMs and then having to test them in humans to understand their toxicity, Creyon Bio can design both safe and effective with their initial candidates, greatly reducing downstream cost and timeline of therapeutic testing. In this way, Creyon is seeking an FDA approval for not only the therapeutic OBMs that their platform generates, but also an approval of the platform itself!

15:22 to 28:49: The team and approach behind computational drug discovery, platform-centric company building

• On separating software from true computational biotechs: Large pharma companies, Kiersten feels, can sometimes still convolute software from true computational platforms. She strongly believes such platforms cannot simply be in-licensed and add it to the existing R&D infrastructure.

“Computational biotech companies are a full systems-approach, from in-vivo studies to algorithms to data-cleaning to internal data-handling systems to their people — [these elements] cannot be uncoupled from one another.”

• On what experience these company teams have: Kiersten looks for teams that have a “native” knowledge of engineering, computation, and biology in its approach, with all ingredients in place right from the start. For example, Creyon Bio’s co-founders were an OBM specialist and a computational physicist. She also searches for people who have “an insight that they have earned” and can execute on that insight. That typically means the founding team members have worked at a large biopharma beforehand, and are repeat entrepreneurs who have seen the full commercialization journey and know “what good looks like”.
• On the qualities Kiersten looks for in a team: Beyond their previous background, Kiersten also seeks key attributes of a sterling team. Are they trustworthy? Are they coachable? Are they honest and transparent? Given the complexity and risk of biotechnology, she wants to be surrounded by people whom she trusts and who are open about their journeys, mistakes, and triumphs.
• On what first-time academic founders underappreciate about company formation: The first underappreciated challenge Kiersten highlights is the difference between academic lab discovery and commercial product development. What it takes to put a therapeutic into a person — with the appropriate manufacturing, protocol, regulation, and translational biology — diverges significantly from a series of Nature publications. As such, she highly values the prior experience of building commercial products. The second challenge she witnesses is recruiting the required leadership and expertise. Someone spinning out of a lab may lack the requisite connections to build a robust team, whereas repeat entrepreneurs can rely on a VC network and previous roadmap to acquire needed capabilities during company build.

[Company build] can be extremely difficult to do if academics are trying to be academics, which is incredibly demanding, and also start a company, which is also incredibly demanding. It’s important for academics to realize the demands of doing [company creation] correctly.

• On how to approach ML and computation to drug discovery: Kiersten is adamant that AI/ML are not “panaceas” to solving every step of drug discovery. Instead, she contends that computation must be purpose-built for the task at hand to drive the the cost of something in the discovery process to zero. Tactically, this means identifying the specific data needed, generating this data via integrated wet lab (i.e., in vivo and in vitro) studies, and piling the experimental output back to improve algorithmic prediction. Creyon Bio, for example, is driving the cost of predicting toxicities in OBMs to zero — which in turn transforms the rest of the drug development process. Such approaches, Kiersten feels, require an incredibly focused application of computation and data handling right from the start of drug development.

“Computational biotech is NOT building a biotech company and sprinkling some algorithms on top and hoping that makes drug discovery better. It is not software that can be licensed, bought, sold, or used by others. And it is almost impossible for large companies to institute this kind of bottom-up approach. These are the most common misconceptions about what it means to be a computational drug discovery company.”

28:50 to End: Advice to budding bioentrepreneurs

• On being prepared and saying yes: Kiersten believes opportunity favors the prepared. She recommends aspiring bioentrepreneurs to follow their curiosity, and to be prepared as opportunities arise. In that vein, she also recommends saying yes to as many opportunities as possible early on!