Bioengineering, once viewed primarily as an academic discipline, is growing up.

Our ability to engineer biology is on the verge of changing the landscape of health and health care. Tools and treatments that are engineered, not discovered — CAR-T therapies for cancer, CRISPR for gene editing, stem cell therapies, and more — are now making their way not just into new startups but into established industry. Just look at the first-generation CAR-T companies that have been acquired by major biopharma companies, like Bristol-Myers Squibb/Celgene acquiring Juno or Gilead acquiring Kite.

The acquirers, massive organizations built on the foundations of discovery, are now ingesting companies built with engineering DNA. These are two extremely different mindsets. For decades, biopharma companies essentially used scientists to build products, because there was no means to engineer them. The intersection of these worlds is driving us into the future.

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Here come the culture clashes.

These emerge every time major new technologies disrupt an industry. Think of the early days of oil and the evolution as the industry matured from wildcatters — one man, one rig, one highly risky and unreliable process — to Rockefellers and Rothschilds and a highly sophisticated engineering and discovery process that used every advanced technology and enterprise tool available.

In biopharma and health care today, the “old” culture of discovery — the idea that science is driven by discovering new knowledge (hypothesis —> test —> repeat) — is clashing with the “new” culture of engineering (design —> test —> iterate). This clash encompasses how everything is handled, from identifying biological targets to designing clinical trials and even to how we access health care.

Knowing that these clashes are coming will help smooth the way as the biopharma industry integrates bioengineering deeper and more broadly. I see four key clashes worth noting.

Clash #1: Hype vs. reality

We can’t yet take for granted a common understanding that we can engineer biology. In spite of bioengineering departments flourishing at revered institutions like Harvard and MIT and Stanford and Berkeley, in spite of the success of new tools like CAR-T and CRISPR, some think that bioengineering is either hype or a passing fad. That’s OK; every new field struggles with the old guard.

Bioengineering still needs to come from a place of clearly and repeatedly explaining its worth — with evidence. Let’s just get used to this. That said, naysayers are predisposed to cling to old-school approaches. After all, that’s the value they have to offer. Ultimately, they will need to adapt to an engineering approach or get engineered out of the process.

Clash #2: Eureka vs. Lego

The culture of discovery and the culture of engineering value progress differently. Discovery, for example, prioritizes the “Eureka!” moment above all. One of the most challenging aspects of drug development is that you can’t establish a key performance indicator for such moments. Pure discovery is a lottery ticket business that exists in the biopharma industry only because of its incredibly high value and the potential to save millions of lives.

Engineering, on the other hand, values a repeatable process, one that can be stacked or adjusted the way we build with Legos, aiming for compounding results — a consistent fractional improvement year over year, leading to exponential improvement over time.

Health care needs both approaches. Today’s great discovery will be engineerable tomorrow (OK, maybe 50 years from now). CRISPR, for example, began with discoveries in Haloferax mediterranei, a species of salt-tolerant bacteria. That was pure scientific discovery. But using CRISPR — as a tool, as a therapeutic, or as a platform for future innovations — is squarely in the world of bioengineering.

Clash #3: Should I stay or should I go now?

The choice of staying or leaving is now at the heart of the debate about what engineering can or can’t handle, and what should remain pure, unfettered empirical discovery.

The truth is that engineering can handle empirical approaches. For example, is an A/B test discovery or is it engineering? It’s actually both — discovery done via an engineering process with iteration. Because biology is so incredibly sophisticated and complex, there will always be discovery risk — the risk that some heretofore unknown aspect of biology will lead to failure. But part of engineering is, and should be, handling discovery and failure, and discovery can and should be engineered.

Tools like artificial intelligence and machine learning allow us to introduce to the world of discovery faster throughput, faster iteration, and greater reproducibility. We need to know how and where to apply engineering and discovery frameworks, and where the two worlds meet. Is your discovery risk one where you must wait for serendipity, or can you improve your odds by engineering some part of it?

Clash #4: What do you even mean by that?

The discovery and engineering cultures speak something that sounds like the same language, but really isn’t. Words like “discovery” and “platform” mean very different things in science than they do in engineering. Even “success” doesn’t directly translate: Does it work and we know how we got there, or did we get there in a repeatable process we can tweak?

Getting lucky with a serendipitous discovery is not success in an engineering discipline, nor is getting unlucky a failure in engineering, since you can learn something valuable from failure with which to tweak the process. Like any language issue, we need to recognize the different meanings in those core concepts and know when to use which depending on the world you are in.

Integrating these cultures requires each side to understand core tools, language, and mindsets in both worlds, and knowing where to leverage the differences. Where can discovery yield new empirical information for an engineered process? Where can an engineered process increase the odds of success over a more traditional discovery route?

In health care, as more and more products and tools become engineerable, the world of discovery will need to transition toward integrating engineering. This will be bumpy and uncomfortable and let’s be real, there will be blood. But there will also be many bright spots. There will be people trained in science who, when introduced to engineering, feel they can tap into a new world of possibility. There will be engineers who maybe started their careers because as kids they dreamed of engineering trains who suddenly feel they have the potential to help cure cancer.

The audacious dream of engineering biology on a molecular scale is finally being realized not just in practice but commercially — if we can surmount the culture clashes.

Vijay Pande, Ph.D., is a general partner at Andreessen Horowitz, a Silicon Valley venture capital firm, and an adjunct professor of bioengineering at Stanford University. He serves on the boards of Apeel Sciences, Asimov, BioAge, Ciitizen, Devoted Health, Freenome, Insitro, Omada, and PatientPing.

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  • Great read! Some of this is already happening: Genome-wide association studies is a field where discovery has become “engineerable “, protein structure elucidation is another.

  • He just described the new age of engineering therapies from DNA. He’s only about 40 years behind when this started with recombinant antibody engineering. Maybe he needs a history lesson

  • It looks like Pande feels the need to push his vision of the world, at least once a year. Probably to justify his paycheck as consultant. And so, I am repeating myself once a year.
    Pande’s argument has the same fallacies of the:”if cars would have improved like computers they would cost a fraction and be thousands times more capable”, that is they are not equivalent problems to solve. They are different realities with different rules and constraints.
    BTW, how does it feel to consult for a fake PhD, like Freenome’s CEO Otte? Asking for a friend

    • Pande points out that engineering is already a successful approach in biology, not a speculation. Each year, biology becomes more of an informatic science and reaps the attendant compounding improvements to technique. Do you not see it?

      PS- I’ll take a fee to consult for a fake doctor any day. The PhD is a farcical pyramid scheme anyways…

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