Four kinds of biotech innovation
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If you missed our first three posts, you can find them here:
Our last Perspectives explored the emergence of our modern biotech innovation ecosystem by examining three questions that animated the science policy debates of the 20th century.
Who should fund research?
Where should it be conducted?
Who should own its outputs?
The fixtures of biotech innovation that many of us take as given - research universities, VCs, tech transfer offices, the modern PhD system, big pharma, - are very recent phenomena that arose directly from how policy makers answered these questions during the 2nd half of the 20th century. In case it needs saying: they haven’t been around for long, nor will they be around forever!
In addition to these three questions, I’ll use this shorter-than-usual post to explore a fourth question - one that remains unsettled well into the 21st century - and propose a framework for thinking about it.
What kind of research should it be?
What’s the difference between “R” and “D”?
The ampersand in R&D suggests that innovation is composed of two types of related activities, research and development. In an academic setting, the two parts of this diad are more often referred to as 1) “basic” or “fundamental” research and 2) “applied” or “translational” research. The former conceived of as the search for discoveries, for discovery’s sake. The stereotypical image is of Robert Boyle sleeplessly interrogating the properties of fluids, Rosalind Franklin patiently compiling x-ray crystallographs, or, more recently, teams of scientists pouring over diffraction patterns of energized particle collisions in search of the Higgs boson. In each of these cases the researcher is driven, at least in our imaginations, by an unalloyed desire to know: why is the world the way that it is? how does it work?
The latter is about applying the fruits of discovery toward solving everyday problems, be those treating diseases, winning wars, improving industrial processes, or just making life easier. Stereotypical images are of Edison sleeping under his desk beside his 999 light-bulb filaments, or of the dozens of scientists gathered in the New Mexico desert to see if their nuclear contraption will really explode. These researchers are driven, again at least in our imaginations, by the desire to solve: how might we create light without oil? how might we end the war without my friends having to die invading Japan?
In practice, however, the distinction between basic and applied research is rarely so neat. Was the Human Genome Project basic or translational research? Was Jennifer Doudna’s lab conducting basic or applied research when they interrogated bacterial immune systems and discovered Cas9? Were their motivations to know or to solve? Does it matter? When thinking about the nature of innovation ecosystems, the distinction between basic and applied research can obscure as much as it illuminates.
Four Questions
A better distinction, perhaps, is between the types of questions that research can address, and the different types of innovations that result. The first two are those most often associated with the modern research university and national labs:
Knowledge Questions: These involve knowledge gaps, puzzles, or incongruities within a particular field of knowledge. How do different non-coding RNAs modulate DNA transcription? What role does oxygen play in a tumor? Why don’t the observations from our newest telescope align with predictions from the Standard Model? The answers to these types of questions are scientific discoveries.
Technical Questions: These concern technical challenges that 1) might flow from scientific discoveries or, alternatively, 2) might need to be overcome in order to test theories & make discoveries in the first place. How might we “image” a protein? How might we deliver a nucleic acid into a cellular organelle and detect whether it got there? How might we manufacture, align, and cool superconducting electromagnets? The answers to these types of questions are novel technologies.
Two other sets of questions are often conceived as not pertaining to scientific research at all; but if a goal of the university is to not just generate discoveries but to facilitate their translation into solutions for real human problems, they are just as important to consider and get right.
Customer/Problem Questions: These start with specific customer problems and work backward to technologies that might solve them. The most obvious place to find these at a university is in an engineering college or hospital-adjacent department. How might we desensitize children to food allergies? How might we switch tumors from cold to hot? How might we decrease the negative side-effects of radiation therapy? How might we reduce the amount of greenhouse gas that heavy manufacturing generates? The answers to these questions are potential new products.
Commercial Questions: These include the host of practical questions related to founding, capitalizing, and operating a new venture. These are the questions asked in business schools and MBA programs. How do I build and protect a sustainable business model around a therapeutics platform? How do I raise money for a device company? How do I attract an experienced CEO to a seed-stage company? The answers to these questions, when answered together, are new ventures.
To be able to effectively ask and answer questions in any of these categories - scientific, technical, customer/problem, and commercial - requires a combination of tools, talent, and training unique to its type. Scientific questions in the biological sciences, for example, require wet labs, complex instrumentation, cell lines, engineered animal lineages, and teams of highly-skilled scientists and technicians. These, in turn, require substantial investments in both physical and intellectual infrastructure that can take decades to tune and mature. All that is to say that very few geographies, let alone universities, have the resources to be able to tackle all four sets of questions well. Like in the natural world, most ecosystems rely on other ecosystems to supply some important input or take in an output. And that is not necessarily a bad thing. It is only in climax communities that the components needed to answer all four types of questions are, more or less, present.
The goal of this series is not to cover innovation from soup to nuts, but from “Aha” to Series A: the earliest stages. Much of this time is spent on campus, so our next post will take a closer look at universities and, just beyond them, at investors and incubators that specialize in transitioning ventures from university research labs into labs of their own. Stay tuned.
What we’re reading:
How long does it take to go from science to technology? - TLDR: 20 Years
SVBs early-stage lifescience trends to watch in 2023 - TLDR: “Emerging hubs rise as established hubs plateau”