Debunking biotech misconceptions

Biotech and technology don't mix. At least, it is the common opinion, which contributed to the early 2000s split between the tech and biotech industries. Both were employed by venture capital firms that either divided into two practices or closed one. In fact, there wasn't really a compelling reason for them to be together in the first place, therefore there was a valid justification for this. Investors in the tech and biotech industries did not need to speak the same language. Debuggers weren't particularly useful to biotechs, and technology didn't really need to understand DNA. More importantly, the characteristics of the investments themselves—the amount of capital needed, the risk/return profiles, the ways in which companies are built, and the ways in which they are scaled—were so dissimilar that knowledge and experience were difficult to transfer between the biotech and the tech industries.

But since then, things have changed. Data and program biology are flooded in today's life science companies. Across the whole healthcare value chain, from biological sciences to care delivery, tech companies provide data pipelines, fundamental infrastructure, and connective tissue. The tried-and-true playbooks are no longer relevant since the boundaries between biology, computer science, and engineering are becoming increasingly hazy throughout the whole healthcare sector. This new bio + tech mix is referred to as "bio."

Why do venture capitalists in Silicon Valley fund biotech is still a topic of debate. However, that is not the right question to ask. Is the next generation of startups emerging in this new bio age going to be supported and evaluated by established biotech or tech enterprises? That is the actual question.

1. Biotech VCs create biotech businesses

VCs have established an entire generation of biotech businesses, and these businesses have actually benefited patients. But at the nexus of biology, engineering, and computers, a new breed of bio entrepreneur has evolved. Instead of turning the company over to VCs, these founders are building it themselves. Therefore, the bio VC's function must also change—to support rather than direct, to accelerate rather than to develop.

2. CEOs and scientific founders aren't the same things

According to conventional biotech wisdom, experienced drug hunters are required to lead biotech businesses. Yes, experience is important, but as technology continues to change how we identify and create drugs—and even what a medicine is—a profound understanding of how to use these new, powerful technologies is equally important. Leading professors and recent PhDs in cutting-edge, multidisciplinary topics like artificial intelligence or biological engineering are already, by definition, the foremost authorities in those domains. And in many instances, these scientific pioneers are in a better position than business veterans from earlier conflicts to advance their own discoveries.

3. Platforms are used to build biotech enterprises

Not typically. In the biotech industry, the word "platform" is excessively used to represent what is really significant expertise in a particular area. This deviates from the traditional understanding of a "platform" in technology, which is a foundation of technologies upon which other applications are built. The new breed of biotech firms is based on platforms for computation and engineering that employ proprietary technologies to see what others cannot, or to perform actions that others are unable to, such as creating immune cells to combat cancer. In other words, a bio platform gives you an advantage in knowledge or capacity that is supported by technology, ideally both, and serves as the cornerstone for many potential new drugs. This is a significant advancement above the majority of conventional custom biotech platforms, which produce few, if any, novel medications.

4. AI is overrated

Speaking of platforms, few have elicited the same level of suspicion from the traditional biotech community as artificial intelligence. Like any instrument, AI is by no means a panacea. However, it is particularly ideally adapted to assist us in making connections in biology's unfathomably intricate network that we humans could not make on our own. A swarm of new AI startups have appeared, some of AI's most well-known experts are concentrating their efforts in the healthcare sector, and even big pharmaceutical businesses now consider themselves data science companies and are staffing up accordingly. Though we're still in the early stages, AI is already removing some of the uncertainty from clinical diagnosis and medication research, even though expectation can occasionally transcend over into hype.

5. Your invention comes too soon

Years before they have any real effects on patients, biotech advances are the focus of feverish headlines—where time is short but optimism is plenty. However, because biotechnology innovation cycles are speeding up, the promise->potential->practice cycle is now occurring in record time. The price of DNA sequencing decreased by 1000 times in less than ten years, far more quickly than Moore's Law (a phenomenon commonly referred to as "Flatley's Law"), and it is now a common tool for research and development and diagnostics. With only five years since its development, CRISPR for altering human cells is already revolutionizing how we find, identify, and even cure disease. In the US, the first human clinical trials began in 2019, and there are many more planned.

6. It's too late for your innovation

The process of traditional biotech drug discovery is well known. It's a race to find the molecule after a unique illness target has been verified. If you don't arrive first, you had better be the best in your field when you do. There isn't much that can be carried over from one endeavor to the next if you fail (and tragically, many people do). However, with engineered biology, you may produce technological generations, similar to software versions, that build on both the advantages and disadvantages of earlier iterations. The first generation of immune system cells is being built upon by the second. Additionally, since the common foundation of these medicines can be reused and repurposed, developing new uses will be simpler.

7. Instead of a biotech revolution, we are in a biotech evolution

Some claim that rather than entering a new age, we are only seeing the biotech industry's ongoing upward trajectory. However, when they get sufficiently extreme, evolutions transform into revolutions, and steady advancement gives way to sea shifts. We have recently reached the era of programmable medicines, in which living things can be modified to act as gene and cell therapies. Our team has also programmed digital therapies. The FDA is devoted to modernization and reform in order to keep up with things, and it has even provided guidelines for medical AI that learns and develops over time.

All options are on the table as entrepreneurs tired of the current quo reimagine the market and juggle tried-and-true business methods. Bio also has effects that go beyond human health. From food to fashion, engineered biology is revolutionizing a wide range of industries and has the potential to impact everything.

8. A biotech company cannot be built in the same way as a tech company

Companies in the bio industry combine traditional and biotech elements. They have dry labs for data and design and wet labs for the slick, gooey materials. Building a biotechnology company necessitates the capacity to combine disciplines that have historically operated in isolation—engineering a multidisciplinary culture that keeps the tech attitude of relentless iteration that promotes continuous development. Moore's Law wasn't a forecast; it was an aim.

Infrastructure like shared lab facilities and AWS have already significantly decreased the cost and time required to launch a bio business, just like it did for the tech industry.

9. Science cannot be engineered to find new things

We are at a distinct disadvantage when dealing with biology since we are operating in a system that was not created by us. But significant developments have altered the game:

• Platforms that allow for a new level of resolution and breadth in biology.
• Sophisticated AI to industrialize discovery and methodically unravel biological complexity.
• Biological engineering, which is most significant for directly programming biological systems.

All of this moves us closer to biological design with a purpose and into a world where scientific discovery can and should be engineered.

10. Regulatory risk has two sides to it

The 90% clinical trial failure rate is widely known, and it is crucial to ensure that any new treatment is both safe and effective since once something is ingested into a human body, all bets are off. However, what the business refers to as regulatory risk—that the FDA won't approve your drug—is essentially a misnomer; it serves as a substitute for science risk, which is the risk that your drug won't work. This type of risk can resemble flipping a coin; either you get it right or you don't in a world where it's unknown whether your target affects disease and/or if your molecule adequately and exclusively reaches that target. However, a manufactured cure is less black-and-white if the disease's root cause is fully understood, as with hereditary disease. Component by component, you can iterate your way to success. Once you understand how to transport one gene to a specific cell type for a particular disease, it becomes simpler to deliver a different gene to a different cell type for the subsequent sickness. 

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