The Neu* Bio Platform Playbook for Technobiology, Part 1 (Motifs)
Sharing NeuScience’s playbook on platform creation and evaluation for the “techbio” era.
Disclaimers and Overview
- I have no financial stake in any company mentioned, whether public or private. Assume no conflict of interest unless otherwise specified.
- All opinions herein should not be taken as investment advice on any company mentioned and are based solely on publicly-available information.
- Part 1 (Motifs) provides an overview of the attributes and opportunities that define modern biotech, as well as the many company types and terms seen in industry today.
- Part 2 (Maxims) shares NeuScience’s guiding principles for risk minimization and value maximization regarding techbio platform creation.
- Part 3 (Methods — coming soon) shares some of the methods we use at NeuScience to properly evaluate preclinical data and market opportunity ahead of clinical studies.
- Most of the information shared herein is discussed through the lens of cancer R&D and oncology company creation. While much is likely still applicable to other disease areas, some may not.
- The goal is to contribute to an evolving, public conversation around modern therapeutic company creation. I expect our own opinions and methods to evolve with time too, and we’re always open to hearing and learning from others’ opinions or best practices.
“Every sentence I utter must be understood not as an affirmation, but as a question.” — Niels Bohr
Preface
During my early days of cancer research, many things about our collective research enterprise puzzled me. I didn’t understand, for example, why so many follow-on drugs seemed to get pursued.¹ I wondered if folks really looked at a fifth “me too” drug and thought, “Surely, this is the one we cure cancer with!” I’d wonder too why there seemed to be such a steadfast commitment to struggling therapeutic strategies. Did we really believe, for example, that EGFRvIII drugs will cure EGFRvIII+ cancers when EGFR drugs have done no such thing for EGFR+ cancers?
Eventually, I understood that no one actually thinks these are cures. In fact, our general reluctance to call anything as such stems from our tacit admission of future defeat. We expect cancers to eventually outmaneuver our newest efforts and drugs du jour. We don’t dare say “cures” because we don’t want to give patients false hope. That would be irresponsible. Instead, we resort to more conservative descriptors such as “breakthrough drug” or “game-changing” because surely those won’t mislead patients or markets.
Indeed, the lamentable reality is that we don’t think in terms of cures. Instead, we think in percentages and probabilities. We’ll cleverly deduce, for example, that a 30% overall response rate (ORR) should be enough to edge out an FDA approval because of a “high unmet need.” We’ll conclude our programs have a higher probability of success because biomarker-driven approaches show greater FDA approval rates. We’ll even delude ourselves into thinking a crowded new market is a great bet because it’s already been “de-risked.” I get it: we don’t actually know what cancer cures look like, only what “good enough to get an approval” might. But, to think we’re minimizing investment risk by sticking to the same, overwhelmingly failing strategies is a collective self-delusion that turns our lack of cures into a self-perpetuating reality. To be clear, I’ve only had the pleasure of working with well-meaning, smart, dedicated people who truly want to find cancer cures. Our dearth of them isn’t due to some evil conspiracy: turns out systemic complacency will do. Furthermore, we scientists must also constantly contend with the sedative effects of group think on the one hand, and the seductive effects of funding trends on the other. Realizing this began to feel like a betrayal to why I went into cancer research in the first place.
My entry into cancer research can be traced to one fateful morning during my freshman year in college. It was the early hours of a cold February day in Baltimore when I awoke to the sounding vibrations of an incoming call. “Hey, it’s me. Something’s happened,” said a soft, familiar voice. “I’m so glad I caught you. The nurses will be here soon to prep me for surgery, but doctors say there’s a chance I don’t make it. I was scared I’d have to go in without getting a chance to say hi again, and I guess one last goodbye, just in case,” she paused for a moment, “…we may not get another chance.” Until then, I didn’t know words could punch the heart. It was my high school girlfriend, and although we weren’t together anymore, we’d enjoyed a very happy relationship throughout high school and had remained caring friends to the day. She explained a rushed visit to the ER the night before had revealed that an orange-sized brain tumor was starting to suffocate vital parts of her brain. To prevent permanent damage, doctors needed to go in and relieve some pressure.
Her surgery that day was a success, but it also delivered a grim diagnosis: her tumor was highly malignant. More surgeries and treatments would follow but at best they merely delayed the inevitable. She passed away the following year, at just 19 years old, surrounded by family. It was Thanksgiving Day when it happened, and though that may seem like cruel irony, I prefer to think it’s fitting given anyone who knew her is surely grateful they did. I certainly am, and always will be. What is perhaps ironic, however, is that that same year The Cancer Genome Atlas (TCGA) was launched as part of a national, multi-institutional effort to molecularly characterize thousands of primary cancers and matched normal tissue samples. Glioblastoma was the first indication to be prioritized by the TCGA initiative and over 600 glioblastomas have been fully sequenced since then shedding novel insights into our understanding of this dread disease. We’ve brought even greater granularity to our understanding of glioblastomas since as we’ve traversed the ‘omics hierarchies with single-cell precision. Nearly 20 years of cutting-edge glioblastoma research and about 1500 GBM clinical trials later, we’ve managed to improve median GBM survival outcomes by maybe a few months at best and mostly due to big improvements in surgical outcomes and post-operative care.
My main problem with thinking in percentages and probabilities — as industry still does today— is that it leaves out the most important part of the equation: patients. My problem with “good enough for an approval” type drugs is that they often take more from patients and their families than what they provide. For example, for Spring Break my freshman year I flew to Houston to visit my former girlfriend and her family while she was getting care there. We hadn’t spoken in over a week because a second surgery and the additional treatments that followed gave her a temporary but distressing loss of certain mental functions such as recognizing familiar faces or even remembering how to speak. When I arrived, her younger sister escorted me to her hospital room. Before I could go in, however, she grabbed me by the shoulder — now with a stern look on her face — and said, “Whatever you do, don’t cry. She hates it when people cry.” In retrospect, it’s silly I never imaged she’d look any different from the last time I’d seen her. As soon as I walked in though, I understood why I’d been warned. Her long, voluminous hair had been reduced to a pixie cut. Her skin was pale, like porcelain, and because her pretty face was still swollen from the treatments and surgical trauma, there was a cherubic complexion to her. I tried saying hi as I sat next to her but found my vocal cords had retreated. I felt involuntary tears forming next, my vision slowly blurring as they built up on my eyelids. With her sister’s warning playing in my head, I sat there as still and silent as could be in an effort to hold it together. She must’ve sensed my inner disarray and, presumably feeling sorry for this frozen fool, she reached out and placed her gentle hand over mine. I knew she recognized me too as a soft, silent smile spread across her face. Now I broke. By the time her family joined us, I was the soggy aftermath of a previously sobbing mess. If I had to guess, I must’ve looked similar to how, years later, I remember most movie goers looked with their puffy eyes and runny noses once the theater lights came at the end of Marley & Me.
Looking back, when my high school girlfriend first called me that fateful February morning, I was scared I’d never see her again. In retrospect, I never did — at least not the version of her I knew and who was speaking to me that morning. That’s the sort of stuff statistics won’t tell you. Sure, she survived longer than many with her type of cancer, but she didn’t exactly “live” life as she so deserved during much of that time either. Indeed, when it comes to cancer — including brain cancer — surgery is often the easy part for most patients and their families. It’s our current treatments, which very much include precision therapies, that truly alter patients’ quality of life.² Over time, she withdrew from the world, and we lost touch. The day of her funeral, I ended up listening to the whole mass from outside. Most people never knew I went. I don’t know if it was cowardice or selfishness that didn’t let me go in to pay my respect. Maybe it was both. What I do know is that I couldn’t let my last memory of her have her in a coffin.
Her memory — and the memory of what she went through as a patient— is why I became ever more frustrated with a system prioritizing drugs aimed at the bare minimum. This isn’t anyone’s fault. Our collective R&D system is a behemoth bound by convention on many fronts, and in fairness that convention usually helps keep thing afloat. Problems arise when the situation clearly calls for unconventional solutions instead, as it has in cancer. This is why I ultimately decided to leave cancer research and try my luck in tech 10 years ago or so. Since then, however, it’s amazing to see how much has changed — a collective change that I think is best categorized as “market timing.” Timing is why I’m back, why we’re building NeuScience, and why for the first time in a long time, I truly believe there’s a real opportunity to finally make a meaningful clinical difference in the near future for most cancer patients.
This is Part 1 of a 3-part series meant to provide a deeper dive into the unique opportunities unfolding before us in the life sciences, and how we at NeuScience think about what building and creating the next generation of iconic companies should look like. Hopefully, others building in our space will find this helpful too.
From Textiles to Technobiology
The success or failure of nearly every innovation or innovative business model is most often determined by timing. Timing, for example, is the difference between Pets.com and Chewy. Though these two companies are essentially the same business idea — to shop all your pet needs online — the former is a long-defunct paragon of the dot.com bubble while the latter is a company with a current market cap of ~$16B as of this writing.
The most magical timing moments arise, however, when two or more seemingly unrelated industries or technologies come together to unlock previously inaccessible opportunities. This is exactly what’s happening in bio today thanks to advances across laboratory automation, high throughput data generation, biological tools, cloud computing, and AI all occuring in tandem. What we’re seeing isn’t a scientific revolution, however. Although I’ve stated this point in a past post, we’ll discuss why and what this means further in Part 2. Instead, we’re in the midst of an industrial revolution, and that’s arguably better given industrial revolutions have always been the force behind life-saving innovations coming to fruition. It’s no coincidence that every era of rampant life science innovation happens to overlap with every industrial revolution in our history (see Figure 1). Indeed, the folks at NFX nailed it when they first identified our bio era as such a few years back.
Defining our Defining Times
At least once a year, a lively debate and seeming Twitter tradition erupts on the merits of “techbio vs biotech.” Often, confusion seems to arise over what the best terms are and when or how they’re appropriate if at all. In an effort to help set the record straight (or perhaps just annoy everyone on all sides of the debate), it felt pertinent to try to list and define what today’s commonly-heard terms mean.
Pharmaceutical (Pharma) Cos: Pharma companies are defined by the industrial use of chemical compounds in order to create, manufacture, and sell small molecule synthetic drugs. The first synthetic medicines such aspirin, morphine, codeine, quinine, and penicillin were a product of emerging 19th and 20th century pharmaceutical companies.
Biotechnology (Biotech) Cos: Biotech companies derive products from biological systems or living organisms rather than from chemical processes. Within therapeutics, biotechnology pertains to the discovery and development of biologics or large molecules (as opposed to small molecules) such as monoclonal antibodies, gene therapies, and CAR-Ts. The successful creation and commercial launch of synthetic human insulin using recombinant DNA technology marked the start of the biotech industry during the 1980's.
Tech Platform Model: The term “platform” refers to a type of business model that typically creates value by facilitating an exchange of goods our services between two or more groups such as consumers and producers. Importantly, many platform businesses don’t sell actual products like in the case of Uber or AirBnB.
Bio Platform Model: Some biotech companies have borrowed the tech term “platform” as a way to denote multi-indication potential based on some core computational (eg. AI) or biological (eg. CRISPR) technology. Platform companies have become a popular alternative to “single-asset” plays. It’s important to note, however, that bioplatforms actually have very few if any of the defining characteristics of true “platform” businesses as seen in tech. A defining feature of tech platforms, for example, is their ability to jump across unrelated industries. An example of this would be Uber going from ride sharing to food delivery. Bioplatforms of today are still limited in scope given they usually spread across related industries by, for example, going after the same drug target or using the same drug delivery method across multiple indications. As a hypothetical, this would be more akin to Uber expanding from the ride sharing business into the car rental business. As such, “bio platforms” resemble the economies of scale of “SaaS” companies more so than the business practices of “tech platforms.” One major advantage that therapeutic companies can have over technology platforms is the de-facto ability to monopolize a market. Any therapeutic company with a clear best-in-class therapeutic will reign supreme within that indication until its patent expires.
Digital Biotech: This term usually refers to biotech companies built with the necessary technical and operational frameworks needed for computation-heavy biotech companies to scale. It’s often used interchangeably with “BioPlatform” but for a deeper dive into it’s meaning and what building a “Digital Biotech” entails, I recommend this post by Jake Feala
Founder-Led: Historically, biotechnology companies are typically operated by hired C-Suite executives, often at the behest of their investors, though many scientific founders also typically prefer to keep their academic roles while retaining a scientific advisory role. “Founder-led” refers to the emerging trend of scientific founders wanting to helm the companies housing their IP or technology. From an investment philosophy, “Founder-led” prefers companies run by the scientific founders over companies run by hired executive teams. This mirrors the tech sector’s earlier “founder-led” movement that led to companies such as Facebook, Uber, Spotify, AirBnB, Dropbox, Box, Stripe, and many others. Examples of “founder led” therapeutic companies would be Absci, Exscientia, Recursion, and Twist Biosciences.
Techbio: The term “Techbio” has some overlap with “founder-led” and is often used interchangeably. However, Techbio is also meant to specify an “engineering-first” approach towards therapeutic company creation and scaling.
Technobiology: If biotechnology is defined by the application of biology-based techniques (eg. CRISPR, recombination) to drug discovery, then it seems fitting to designate technobiology as the application of technology-based approaches to drug discovery and biological modeling. After all, we’re going to need a name for whatever process I suspect will perfect the use of generative AI to “imagine” cell painting images that can then be used as synthetic data to train on (and perhaps even improve algos via adversarial approaches). Biotechnology doesn’t quite seem to work for such a use case. Looking into this, it seemed fitting to re-introduce the term “Technobiology” given it seems poised to become a necesssary part of our industry lexicon.
While “Technobio” would be the correct abbreviation for “technobiology,” the already popular “techbio” does seem to roll off the tongue better. And, as someone who also saw “techbio” as somewhat redundant, I stand corrected as “technobiology” or “techbio” are, by definition, indeed correct in terms of their noteworthy difference from “biotechnology” or “biotech.” For simplicity and consistency though, I’ll defer to “techbio” for the remainder of this post.
Another reason why I suspect the term techbio will become more relevant over time relates to business models. Below we’ll cover three examples of companies we believe embody true “Techbio Platform” potential and are well positioned to capitalize on the opportunities of our imminent digitized health and therapeutics future.
Potential Phenotypes of Future Technobiology Platforms
Although we’ll dive deeper into how we’re building NeuScience in Parts 2 and 3, below are three cases studies of companies we like and think embody the future we wish to see unfold for all of us.
The idea that advanced solid cancers can be successfully treated with monotherapy is about as antiquated as the idea that patients in remission or who’ve enjoyed a complete response need not take steps to live as healthy a life as possible. Although we still have a ways to go in order to understand what “healthy” means for each person, research hospitals like MD Anderson are taking the right steps to determine that by using science-based methods as evidenced by their Department of Integrative Medicine at MD Anderson.
I appreciate that Faeth Therapeutics appears to be pursuing similar science-based principles in order to use diet to keep cancer in check. Though I think we’re in the very early innings of fully understanding how to do so, I certainly think approaches such as Faeth’s are worth investing in and hold massive potential if proven true.
While Faeth Therapeutics currently pitches itself as a “pharma company,” it’s pitch deck shows it clearly (and rightfully) sees itself being able to merge maintenance therapy with digital health services. As such, it could help pioneer subscription-like business models such as those employed by tech companies today but for a therapeutics + digital health monitoring. Technobiology or “techbio” seems a more fitting description for a company like Faeth Therapeutics given biotechnology is not digital health and their business models will likely resemble those employed by technology companies.
Mammoth Biosciences is a CRISPR technology company that embodies the attributes of “tech platforms” more so than most “techbio” companies for its ability to cross different industries (eg. diagnostics to therapeutics). Since NFX has already done a deep dive into Mammoth Bio I’ll defer to their post here for more info on this promising company.
In my opinion, Tempus has been quietly building one of the most comprehensive precision medicine companies of today, and as such is well positioned to be one of the top companies of tomorrow. The reason it’s so well positioned is because of its wide cross-vertical integration. While most bio platforms today expand across disease indications at best, Tempus is expanding across both, disease indications and healthcare industries. For example, it’s scope of activities today include diagnostics, biomarker discovery, clinical data annotation and AI analysis, and even robust patient-derived organoid drug screening capabilities. The fact that this is a founder-led company (whose founder has had very large prior successes) is all the more reason not to underestimate Tempus’ potential and future ambitions.³
Conclusion: Welcome to the Revolution.
The modus operandi of drug R&D has long-been tailored to meeting the lowest bar necessary for an approval. Those of us working to build the next generation of therapeutics companies have a unique opportunity — in fact, a responsibility — to set a whole new bar for ourselves and our industry. It’s imperative that we hold and help each other to a new, higher standard as we build a better future for our patients and our loved ones. I want to emphasize, however, that it’s not the technologies of today that will ultimately determine whether we see this future or not. It’s we who must forge it, it’s we who must fight for it. When I look around today, however, I see exactly the type of people, from entrepreneurs, to investors, to scientists, to executives, that I believe will ultimately help build this new future.
What’s a revolution without revolutionaries, after all. Welcome to the 4th life science revolution.
¹ Having a few “me too” drugs available on the market is indeed important and beneficial given even slight differences in chemical compositions or formulations can benefit groups of patients more. It’s worth noting, however, that many of the purported attributes of “me too” drugs such as price reduction or greater efficacy/less toxicity issues rarely play out in the market or the clinic.
² The advent of precision drugs has pushed a pervasive narrative unto traditional chemo and radiation therapy as somehow less safe or more toxic than precision drugs. Chemo and radiation therapy are by far the most curative treatments available today, and because chemo and radiation are administered acutely, their side effects are often temporary (though sequelae can linger depending on dose). Precision drugs come with many side effects too, some of which can profoundly affect quality of life. This is actually more problematic with precision drugs because they are rarely curative and are instead intended for palliative or maintenance care. As such, patients on precision therapies must deal with the side effects until death or unless they discontinue treatment (it is not uncommon for patients to chose to do so).
³ Tempus founder Eric Lefkofsky is an LP in a fund that has invested in NeuScience. No other conflict of interest exists regarding Tempus.
