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Fifteen years and $2.5 billion dollars is too much to get a drug to market that many cannot even ... [+] afford. Solution: put big pharma in the cloud, and make drug discovery possible from a coffee shop.

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, its easy to argue that big pharma isnt doing much innovation these days.

Whats 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 youve made, Mark Fischer-Colbrie tells me. Hes the President and CEO of Strateos, and his company is taking all the processes, instruments, and robotics youd 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 usersfrom big pharma through to synthetic biology and academiathe 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 anothers 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 youve 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 youd 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 analysesit 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 youve 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. Theyre 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.

Its 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 platforms ability to rapidly accelerate experiments and to optimize conditions for gene editing. Its 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. Were excited about how this will have an impact across the board.

Follow me on twitter at @johncumbers and @synbiobeta. Subscribe to my weekly newsletters in synthetic biology and space settlement.

Thank you to Peter Bickerton for additional research and reporting in this article. Im the founder of SynBioBeta, and some of the companies that I write aboutincluding Strateosare sponsors of the SynBioBeta conference and weekly digest heres the full list of SynBioBeta sponsors.

Excerpt from:
Running Your Pharma Company Out Of A Starbucks: Drug Discovery Moves To The Cloud - Forbes

This entry was posted on March 14, 2020 at 7:46 pm and is filed under Personalized Medicine. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed. |