Strateos’ Robotic Cloud Lab Epitomizes The Bioindustrial Revolution That Will Fast Track Life-Saving Discovery
Small biotech start-ups accounted for 63% of all new prescription drug approvals in the last five years. And the way that many big drug companies are establishing their own venture capital funds to invest in small, innovative start-ups, it’s easy to argue that big pharma isn’t doing much innovation these days.
What’s going on here?
“You can literally sit at a Starbucks, design a compound, have the robots assemble that compound and go through the purification and analysis steps to validate what you’ve made,” Mark Fischer-Colbrie tells me. He’s the President and CEO of Strateos, and his company is taking all the processes, instruments, and robotics you’d find in a big pharmaceutical R&D facility and making them accessible to anybody with a laptop and a good idea.
This is the lab of the future, where automated drug discovery can be done from the comfort of a coffee shop. The capital investments associated with traditional pharmaceutical research and development are gone. And perhaps most importantly for Fischer-Colbrie, this is “the foundation biology needs to become industrialized.”
Combining automation in biology and chemical synthesis while leveraging big data and machine learning, Strateos’ Robotic Cloud Lab is a platform for biological discovery at unprecedented speed, reproducibility, and cost-effectiveness.
Combining forces with Eli Lilly and Company, Strateos powers a robotic cloud laboratory that can compress a three-and-a-half year drug discovery cycle into 12 months. Open to a wide range of users—from big pharma through to synthetic biology and academia—the company has triggered a high-throughput revolution in life science.
“If you look broadly across life sciences, I would estimate more than 90% of the workflows are manual, with uncertain data capture,” say Fischer-Colbrie, reflecting on the status quo of most lab research today. “In order to advance discovery, all of this needs to get industrialized, which means automation, it means repeatability.”
A reproducible platform for better drug discovery
Therein lies a huge benefit for companies and consumers: the drastically improved reproducibility of Strateos’ automated workflows. Science is in the grips of a replication crisis. A Nature report not too long ago showed that 70% of academics had tried and failed to reproduce another’s experiment. One study of cancer research showed that the rate of converting preclinical cancer research to successful treatments was as low as 11%. The rate for drugs, in general, has previously been reported at somewhere around 25%.
The result of this? A long, wasteful, and expensive drug discovery process, with small numbers of expensive therapies available to patients.
“To be in an environment that takes 15 years and $2.5 billion dollars to get a drug to market that no-one can pay for is a broken model that needs to be rectified,” Fischer-Colbrie laments. And though there are various reasons for the lack of translation of science findings, reproducibility of the method is a huge component. Strateos’ platform provides the robust, automated design-make-test-analyze technology that can turn things around.
Fischer-Colbrie tells me that after you’ve designed a drug from the corner coffee shop, “You then have the whole biological testing piece looking at dose-response curves, and all the other criteria you’d need to make first level assessments of whether that compound might make a good therapy or not.”
Fast-track cancer therapies
Strateos is a merger of Transcriptic and 3Scan. The former has a focus on high-throughput biology, and the latter focuses on making tissue biology and histopathology into data science. Combining these competencies within Strateos means the company well-suited to applying its technology platform for cancer.
Instead of spending the painstaking hours to prepare samples manually, you can take samples from a patient, slice them into micron-thin slices and deposit them automatically on a tape. You can then look at your 3D image and run a range of different analyses—it might be some transcriptomics on slice 18, or immunohistochemistry on slice 19.
“Tissue handling is a huge bottleneck currently, but this is a new way of getting data in a totally different manner,” Fischer-Colbrie explains. “The 3scan offering has the benefit of being able to generate new datasets that in turn you can then use the San Diego lab to come up with compounds that might work against what you’ve found in those tissue samples.”
Focus on the concept
Strateos has created an entire life sciences discovery foundry, and one which is providing the necessary step to turn laboratories into data generation engines – launching biology as an information science.
Fischer-Colbrie enthusiastically stresses that it “really allows scientists to focus on concept. They’re not thinking about how to maintain equipment, or which company they have to negotiate complicated contracts with. Scientists can focus on their hypotheses and experiments and not the infrastructure or day to day worries in the lab.”
It’s a game-changer, and one that improves the quality of hypothesis-driven research in general.
“You can watch experiments happen online, get the data rapidly, and feed into machine learning models that provide whole new hypotheses overall,” notes Fischer-Colbrie, along with another crucial point. “These data, importantly, also include metadata such as environmental conditions and the status of the equipment. So, if you get an anomalous result, you can go back and understand what was going on at the time.”
A range of industries set to reap the rewards
In the short term, Strateos’ platform will be open to a range of potential uses across the life sciences, from big pharma through to personalized medicine and even work in large molecules such as antibodies.
In synthetic biology, in particular, Fischer-Colbrie is excited about the platform’s ability to rapidly accelerate experiments and to optimize conditions for gene editing. “It’s stunning in the context of the ability here to turn ideas into data. We believe in some cases this can happen in as little as 48 hours. This will have a significant improvement in the cycle time of experimentation and design.”
“The world is gradually shifting from standalone instruments to automated work cells, and now we really have to think about data generation and how to analyze that data.” He concludes. “We’re excited about how this will have an impact across the board.”
Thank you to Peter Bickerton for additional research and reporting in this article. I’m the founder of SynBioBeta, and some of the companies that I write about—including Strateos—are sponsors of the SynBioBeta conference and weekly digest — here’s the full list of SynBioBeta sponsors.1