Putting the Tech in Biotech: Why Now is the Time to Build Tech-Enabled Life Science Companies

A-Alpha Bio
A-Alpha Bio Co-founders David Younger and Randolph Lopez.

Over the last six years, Madrona has been investing in founders building companies at the intersections of life (biological and chemical) and data sciences – something we call the “Intersections of Innovation,” or “IoI” for short. Companies integrating the increasingly high throughput and complex life science datasets with the abundant and pervasive computational and data science resources are becoming a core part of Madrona’s investment focus. While we have written extensively about our “Intersections of Innovation” portfolio companies, it is time to share more about the overarching themes guiding our investments in this space.

Applied machine/deep learning and the life sciences are coming together to transform how the world understands and improves human life and health. We believe the intersections of these previously tangential fields will define breakthroughs in therapeutic research, diagnostics, clinical processes, preventions, and cures. And we are partnering with world-class teams focused on building wet/dry lab platforms and novel datasets to disrupt traditional drug discovery processes and mindsets while helping to solve the next generation of challenges in biology across areas such as proteomics, gene editing, synthetic biology, and more.

Why now

Madrona has been the vanguard of myriad technological paradigm shifts over the last 25+ years — from cloud computing to the explosion of open-source software to the emergence of intelligent applications. We believe we are on the cusp of such a shift in the life sciences. The status quo in life science is no longer enough. Today, drug discovery is a slow, painful process. Technologies to profile individual patients with unique “-omic signatures” can now deliver on the promise of personalized healthcare, but the data analysis and infrastructure to deliver personalized insights lags. On the therapeutic side, it can take $1-3 billion and more than 10 years to take a drug to market — and more than 90% of drug candidates fail along the way. That is not the way forward. Drug development can be accelerated through better target selection, low latency, biologically relevant screening, targeted clinical trials, and pharmacogenomics drug selection.

In recent years, biology, chemistry, and data modeling advancements have begun enabling teams to discover, validate, and advance science faster than ever before, but the sheer scale of data generated has become a bottleneck. Key innovations in lab automation, microfluidics, and translational models, for example, have enabled a much faster throughput for wet lab experimentation. Similarly, the technological revolutions in single-cell and spatial biology are generating complex datasets at an unprecedented rate These wet lab advancements create acute pain points that data science and computational approaches are uniquely qualified to solve: too much data, too fast. Only by applying machine learning to these biological data problems will the industry be able to leverage the appropriate speed and scale to realize the promise of personalized medicine, yielding a new standard of care — one designed for an individual.

What we’re excited about

We are beginning to see the impact of sophisticated machine learning models on crucial biological processes that are revolutionizing drug design, but the stage is set for so much more. With digitized data flowing faster than ever, innovative teams can use modern cloud computing resources to process, store, and analyze that data. They can then use this data to train a machine learning model to start predicting behavior — a protein molecule’s binding affinity to another molecule or structure-function relationship. That then enables more targeted wet lab experimentation that, in turn, improves the results and the whole discovery process. This creates an iterative loop that will improve both the traditionally manual wet lab process AND refine the ML model, accelerating drug discovery by many orders of magnitude.

ML will transform the life sciences

As the fields of machine and deep learning improve, we can begin to see how the once brittle and simplistic computational models of biological systems become more sophisticated and biologically relevant. Running these processes at scale enables companies to generate novel datasets that rapidly improve prioritization of which potential candidates on biological targets of interest are worthwhile to advance to additional research or clinical trials.

For example, A-Alpha Bio, one of our portfolio companies, screens and analyzes hundreds of millions of protein interactions and then uses that data to computationally predict binding behavior. This process previously required scientists to screen two proteins in a single experiment and attempt to measure whether they bind to each other or not — possibly having to go through thousands of one-to-one experiments to find a match. A-Alpha’s ability to screen proteins at high throughput in silico (computationally) and in the wet lab has the potential to reduce the time, cost, and risks around the drug-discovery journey. This new, data-rich computational model paired with innovative wet lab screening can advance drug discovery in diverse fields ranging from antibodies to small molecule molecular glues. Computational models are not limited to drug discovery. Opportunities abound to deliver on the promise of next-generation human health in clinical trials, companion diagnostics, consumer health, and more.

Why the Pacific Northwest?

The next generation of intersections of innovation companies need talent that spans both the life science and computer/data science worlds. As the home of world-class technical talent in both areas, the Pacific Northwest is an extremely exciting place for entrepreneurs and a place we’re proud to call home.

Seattle is home to incredible talent in the life sciences ecosystem. The University of Washington hosts the world-renowned Institute for Protein Design (IPD), the Institute of Stem Cell and Regenerative Medicine, and the Molecular Information Systems Lab. Additionally, the Fred Hutchinson Cancer Center, a research and clinical facility recognized as one of the best in the country, has more than 100 labs under its roof and is home to multiple cutting-edge research efforts in machine learning.

Scientists driving innovation in their wet labs is not new, but the speed of discoveries that data science techniques introduce means that, as David Baker, who leads the IPD, articulated recently, “brilliant students … used to all want to leave the lab and become professors…now, they all want to start companies.” The combination of science, data science, and personal ambition to turn academic work into something that touches lives creates opportunities to build companies – providing researchers an opportunity to be at the forefront of both discovery and implementation.

On the computational side, the Seattle area is home to Amazon and Microsoft and tech hubs for Google, Meta, and many others. We’re in the cloud capital of the world, which produces no shortage of new companies applying machine learning in innovative ways and no shortage of tech talent. The Allen Institute for AI also attracts technologists and scientists looking to build companies, providing them access to researchers in artificial intelligence working at the top of their fields.

We’re seeing more technologists and scientists working across disciplines to be a part of an early movement of applying various engineering disciplines to the life sciences. We’re seeing software engineers increasingly drawn beyond the traditional tech industry and into the life sciences by way of the real opportunity to make impactful improvements in human lives and health outcomes.

Why Madrona?

Madrona has invested in ten intersections of innovation companies since our first investment in 2016 in Accolade. Several years later, we invested in Nautilus Biotechnology and began actively leaning into the intersection of life and data sciences. Today, our portfolio companies like Nautilus, A-Alpha Bio, Ozette, Terray Therapeutics, Envisagenics, and others are paving the way for the discovery of new disease treatments through the intersections of these disciplines. But, in concert with the field, we are just getting started.

Madrona is in the unique position of living in both the tech and life science worlds. We’ve spent 25 years working with SaaS, cloud computing, and intelligent application companies. We understand machine learning and how it applies to cutting-edge science. Applying our experiences to help life sciences innovatively combine wet and dry lab experiences and transform how the world understands and improves human life.

There is enormous potential in this space, and we are excited to back teams applying machine learning to the life sciences in innovative ways that unlock novel insights and solve problems across the life science and healthcare spectrum. We are always looking for eager entrepreneurs who embrace innovative new ideas – if that is you, please reach out to Matt McIlwain, Chris Picardo, Joe Horsman.

Our Investment in TwinStrand Biosciences, Leveraging Big Data And The Cloud To Improve Genome Sequencing Accuracy By 10,000x

Today, we’re excited to announce that Madrona has led the $16M Series A investment in TwinStrand Biosciences, a Seattle genomics company with the potential to profoundly impact all of us. TwinStrand’s technology will help detect cancer earlier when it can be most effectively treated, will help identify the most effective personalized therapies, and will help to recognize carcinogens quickly thereby lowering the development cost and time-to-market of powerful new drugs. We’ve previously discussed the incredible intersection of life sciences and computer science in our region – and TwinStrand is at the forefront of this amazing innovation opportunity.

When I first met Jesse, the founder and CEO of TwinStrand, he was discussing the technology in exclusively life science terms. However, as I listened, it was incredible how so many of the concepts had direct analogs to my experience with high scale software. TwinStrand’s “Duplex Sequencing” technology uniquely tags each strand of billions of individual DNA molecules with a chemical GUID. The DNA is then replicated to enable sequencing on a standard genome sequencer – resulting in up to 6 TB of data per run – then imported to the TwinStrand cloud where error correction algorithms are employed. The result is a high-resolution reading of the DNA sequence, 10,000x more accurate than standard sequencing. Duplex Sequencing reduces today’s DNA sequencing error rate of ~1% to below 0.0001%. This biochemical error correction approach reminded me of error correction techniques employed in high scale storage arrays in cloud datacenters.

Researchers are actively exploring how to use this level of precision to detect DNA mutations caused by chemicals (a market known as “genetic toxicology”). Today it can take more than 2 years to determine if a chemical is a carcinogen, as large tumors need to be given time to develop in lab animals. With Duplex Sequencing’s breakthrough accuracy, the resulting mutations can be detected as very small tumors within weeks – saving time, money, and the number of animals required. This testing is a critical step in the drug development process, but also is used to test the safety of agricultural chemicals, food contaminants, and even to examine the effect of space radiation.

When I talked with leaders in the clinical cancer community, a common response I heard was that this level of precision was amazing and insightful – but that today’s diagnostics don’t need that level of accuracy. This response reminded me of so many of the skeptics of 64-bit computing 15 years ago – who would ever need that much memory on any computer? With our investment, we are making the bet that new diagnostics, therapies and even information storage technologies will be developed to leverage this new precision, just like software has always found great new ways to leverage new system performance. It’s very exciting to see the future through the eyes of the TwinStrand team and invest in making it possible.

Jesse created Duplex Sequencing through his MD/PhD research with colleagues at the University of Washington. The TwinStrand team consists of half biochemists, and half software developers and bioinformaticians. Together, they have built an incredible foundation—contributing to more than 15 peer-reviewed scientific articles leveraging Duplex Sequencing and developing a portfolio of over 50 patents. To learn more, I’d suggest these three great recent articles:

TwinStrand’s product will be launching soon, and I look forward to seeing what scientists all over the world will create with it.


P.S. Out of humility, Jesse doesn’t often share that he is the grandson of Jonas Salk, the scientist who discovered the vaccine for polio, definitively changing our world for the better. It’s pretty incredible to think that TwinStrand may have the same potential.

Standing Ovation – Cloud Based Clinical Informatics

Pictured l-r S. Somasegar, Ted Kummert, Chris Picardo, Barry Wark (seated) Winston Brasor

We are excited to announce today our investment in Ovation.io, a company that is building the next-generation suite of cloud-based clinical informatics tools for the genomic and molecular testing industry. The company was founded by Barry Wark and Winston Brasor, building off of software that Barry originally built while a graduate student at the University of Washington in Seattle. Ovation’s mission is to provide modern tools to molecular testing labs, allowing them to automate their operations and workflow while simultaneously unlocking the opportunity within their data.

For the past couple years, Madrona has been thinking deeply about the intersection of the life sciences and computer science. We believe that there is a significant amount of innovation waiting to be realized when modern cloud infrastructure and data analytics capabilities meet the vast amount of data and research within the life science and biotech industries. In parallel, significant innovation in the speed and cost of genome sequencing technology has allowed researchers to acquire vast amounts of valuable data and use this to greatly accelerate research and drug development. Existing areas such as diagnostics and new areas such as precision medicine are both rapidly developing due to the proliferation and increasing usability of clinical and genomic data. Activities such as clinical trial recruitment, collecting real world data (“real world evidence” in FDA phrasing), and understanding exogenous health factors are also being re-conceived because of the huge amount of data being generated by the healthcare system. And at the end of the day, easing friction on these activities via modern software will lead to much better health outcomes for patients and data collection and management is at the heart of this process.

One of Ovation’s most important observations was that medical testing labs (and molecular labs in specific) are underserved by modern software. These labs are responsible for conducting the vast amount of tests that care providers order while treating patients. Large names such as Quest and LabCorp may be familiar but there are many independent testing labs taking on the bulk of this workload. Furthermore, independent labs also conduct the majority of molecular and genetic testing for patient diagnosis. Yet many of these labs are still run on legacy software systems that are cumbersome and inefficient and exist as a major point of friction in lab operations.

Ovation has built a modern SaaS solution targeted directly at the needs of these labs. Their product is a modern cloud-based clinical informatics system that handles all major functions: from patient registry, to managing sample workflows, to storing and organizing the data, and finally to managing the revenue and billing process. By implementing true vertical software, labs are much more efficient in their operations and can quickly measure and analyze their workflows and internal data to provide better services and care to patients. And most importantly, Ovation software offers labs the ability to utilize their clinical and genomic data to improve patient diagnostics and long term outcomes. Ovation is also continuing to build intelligence into their software – learning from workflows in order to continuously help automate the testing process for labs. At Madrona we call these types of software “intelligent applications” and Ovation is a prime example in a vertical that needs modern SaaS options.

We met Barry here in Seattle through Mike Self, of StagedotO, a new seed stage venture partnership and were delighted to find some of the themes we had been discussing internally to be at the crux of Ovation’s business. We could not be more excited to be partnering with Barry and Winston for the next step of the Ovation journey and we are thrilled to help them achieve their vision of unlocking genomic data and clinical informatics in the medical world.

Innovation where Life Sciences and Computer Science Meet

The Pacific Northwest is a major hub of tech innovation. It is also a hub for life sciences research, biotech and healthcare innovation. The past several years have brought increasing convergence of these disciplines, most notably the nexus of life science, computer science and data science. This combination has been a driver of new breakthroughs — i.e. use of machine learning in discovery, diagnostics and therapeutics.

Our region is home to two of the top market cap companies, Amazon and Microsoft, who are both leaders in cloud technologies. These companies are defining and building the scale infrastructure and platforms, including major advancements in Machine Learning (ML) and Artificial Intelligence (AI), for next generation applications. Major research institutions such as the Fred Hutchinson Cancer Research Center, Allen Institute for AI (Ai2), Allen Institute for Brain Science and a growing ecosystem of companies (e.g., Adaptive) are starting to leverage the power of data, algorithms and computing power to develop breakthrough research and products driving critical improvements in healthcare and global health.

The convergence is enabling new opportunities in the broader healthcare and life sciences markets – spanning from traditional healthcare IT to digital health to diagnostics to next generation therapeutics and automated scientific discovery. We have already invested in several companies in this area – Saykara which is bringing NLP and AI to the world of medical scribes, Accolade which helps employees get the most out of their healthcare plan using software intelligence and people, and Envisagenics, the recipient of the Madrona/Microsoft AI prize which is applying AI and high-performance computing to uncover novel cures in RNA sequencing data.

In working with entrepreneurs and the local industry, we’ve looked at the broad market, divided it into “more healthcare” and “more life sciences” and identified areas of specific interest where we see substantial opportunity for software and data-science/AI driven innovation and are within our expertise. Our map of this intersection is below and we will highlight a couple areas of particular interest.

Diagnostics: In the area of diagnostics, ML and AI techniques are already empowering next generation clinical decision support services into the market. The application of computer vision to radiology and pathology is one example. Companies such as Zebra, Viz.AI, Imagen, and others have had AI/ML based medical diagnostics achieve regulatory approval in areas such as stroke diagnosis, atrial fibrillation detection, fracture diagnosis, and others. In the area of cancer diagnosis, new companies such as PAIGE and PathAI are making major strides. In the past year, we’ve seen an increase in new AI-powered offerings achieving regulatory approval in a broad range of diagnostics from stroke to wrist fracture to heart & lung related diagnostics and others.

Infrastructure: To support research and development of new drugs fueled by an understanding of genomics data, there are several important infrastructure categories. One thing we’ve noted over the past year is that our software and infrastructure companies are seeing growth in this vertical. One of these is Qumulo, which provides next generation file storage for institutions like the Carnegie Institution for Science which works with terabyte-size data sets alongside millions of tiny sequencing files.

Analytics: On the more traditional IT end of things, we see an opportunity for analytics that overlay systems for running labs, processes, healthcare systems and more to provide better insights and help drive operational efficiency and improved care. KenSci is a good example of a company working on analytics for large hospital systems.

Data: And, underlying each of these categories is a significant need for data. Data is what will power diagnostic services development, drug discovery, clinical trial matching and many more clinical and research applications. There is a need and opportunity for data providers and ecosystems to leverage the data to drive the innovations we all foresee. Existing players such as Prognos, Patients Like Me, Tempus, and RDMD are all working on this space and we are excited to see the next wave of innovation in data acquisition and management.

As 2019 unfolds we will continue to share our thoughts and if these areas are of interest to you, please engage us.