In 1993, I decided that I wanted to launch a biotech company.* Since then, my friends and I have brainstormed biotech-driven solutions in toothpaste, paint, energy, and medicine.
22 years later I have realized this dream. The path was by no means direct. But despite my sometimes seemingly random education and career path, I have found myself at the nucleolus of bioinnovation: the intersection of data science and biomedicine. Fortunately, my timing was impeccable. Technology is rapidly changing the rules for the life sciences and, like the many industries that it has already touched, software is finally enabling business models in the life sciences to scale to new heights.
I have posted previously about how biopharmas are shifting the burden of innovation to venture investors and startups and how, even at the POTUS-level, that data science is being recognized as the future of life science and healthcare.
More and more, we are seeing exciting examples of how biopharma companies are embracing big data, or at least confirming to the world that they need it, and how investors and startups are diving in to fill the gap.
Big pharma buys into big data
We were recently invited to speak at an internal Sanofi symposium called Convergence of Science, Technology, & Data Sciences – Impact on Pharma. We presented alongside leaders in the space including: Isaac Kohane (Co-Director of Harvard Medical School’s Biomedical Informatics Department), Vikram Bajaj (Chief Scientist, Google Life Sciences), and Avi Ma’ayan (Professor, Pharmacology and Systems Therapeutics at Mount Sinai).
Frankly, I was pleasantly surprised to experience how open to innovation large pharma companies like Sanofi are; I was also excited by how enthusiastically they embraced the opportunity to explore how data science-driven approaches can augment the drug development process from discovery to clinical trials.
As #databio nerds, we were stoked by the interesting approaches others in the computational biology space were taking; from the business side, we were equally pleased to hear a very clear message best summarized by a quote from the former NIH Director and Sanofi President of R&D, Elias Zerhouni: “Big Data and the way we approach it is going to be determinant for the long-term success as an R&D Organization.”
And this sentiment is being echoed throughout the industry:
“We have to build a data-analytics capability that we don’t have today. We’re also going to have to create partnerships and think about different types of people that we need to bring into our company so that we can take full advantage of that part of healthcare.” – Joe Jimenez, CEO, Novartis
“Data will also help more efficiently develop medicines and better define which patients will most benefit.” – Geno Germano, Group President, Global Innovative Pharma Business, Pfizer
“The [combined] role of health care and technology is going to be critical” – Alex Gorsky, CEO, Johnson & Johnson
An example of pharma action in this space recently is the doubling down on genomics. A number of large biopharma companies have publically announced their efforts to utilize big genomic data in both companion diagnostic and new drug development.
“Dramatic breakthroughs in understanding how the human genome functions are still in their infancy in terms of how they can be applied to drug discovery, but we can see their potential to transform the process. This is not an incremental change. We are aiming for transformative outcomes that could improve our ability to bring innovative and more effective new medicines to patients.” – Lon Cardon, Senior Vice President of Alternative Discovery and Development at GSK
“The acquisition of Bina is a significant step for Roche to enable the promise of personalized healthcare by delivering the highest quality genomic data possible.” – Dan Zabrowski, Head of Roche Sequencing
Where there’s scale, there is VC…
a16z’s recent podcast, When Bio Meets Computer Science, discusses “how everything changes when software eats biology” and captures why life science businesses can now scale.
“These new startups have potential to have the kind of economics profile and the kind of financing needs of a software startup as compared to a pharma startup?
These new startups remind me a lot of software startups in 2005 when we see cloud computing start to realize. That’s sort of what we are starting to see now and because they have software at their heart, either literally or in terms of how they think about things, that they are organizing themselves in a cloud-like biology way, this would be very much on the Moore’s Law curve of things. And in a sense you could use this differentiate traditional biotech from this new crop of companies. That traditional biotech is governed by Eroom’s Law and these are governed much more by Moore’s Law”
Life science investors have questioned the value of platform companies in the recent past but the venture community is starting to warm up to them. VC’s recognize that the life science investment model of betting the farm on a potential billion dollar drug (unicorn drugs, anyone?) with a binary outcome is dying. Computation-driven platform companies coupled with the established CRO industry enable life science companies to look and act much more like software companies yielding stepwise, milestone-driven returns and valuations, with much smaller investments and shorter runways.
In a recent post on YC’s move in to the biotech realm, Atlas’ Bruce Booth commented:
“Although the math may be different, virtual biotechs doing drug discovery today are leveraging a similar trend: remove the heavy fixed costs of building out your own laboratory, purchasing expensive lab equipment, and then having to “feed” the system, and move to a lower cost virtual model of renting lab capabilities via a global network of CROs and collaborators. Others have already commented about the decreasing cost of DNA sequencing… but same holds for other aspects of drug discovery, like computer-aided drug design and structural biology. It’s easier to start a scientifically credible biotech today than ever before, and entrepreneurs can make real progress in validating a thesis on seed capital.”
Some examples of investments in the data science-driven drug development space today include Data Collective’s investments in Mousera and Atomwise and Atlas Venture Life Science’s investments in Nimbus Therapeutics and Numerate.
Software has enabled these dreams to become reality
I wish I was prescient enough in 1993 to have predicted the role of technology in transforming biopharma. Honestly, I was really just a kid eager to learn about biology and technology and explore the potential futuristic and off-the-wall applications. Applications that at the time seemed wacky, but today are becoming a reality.
At twoXAR, we use data science to accelerate the identification and validation of drug candidates for complex diseases. I hadn’t thought that this was possible until Andrew introduced me to the DUMA platform and demonstrated how, in minutes, we can identify new treatments for a disease. Since then we’ve translated our in silico results to the physical world and continue to do so through a growing list of exciting collaborations with commercial and academic discovery organizations.
Over the last 12 months, I have heard a variety of reactions to the speed and scalability that computational biology enables (and to the fact that both twoXAR founders are named Andrew Radin). But, it’s clear that as software continues to penetrate all industries it will also keep altering the landscape of drug discovery and the life sciences. And, it’s refreshing to see executives and investors acknowledge the power of computation-based approaches and how they speed up discovery and validation of therapies and enable a new era of software-like scalability in the biopharmaceutical industry.
*feel free to ask me why!