Translation is the process of turning basic scientific research into therapies that cure disease, new sources of energy that heal the planet, and other things that move the world forward. The Translation podcast takes a deep dive into scientific advancements with a huge potential to improve society. We talk directly with the people advancing the science with their own hands and minds, and focus on how we can translate the science from the bench to the benefit of all.
Initially centered on biology and synthetic biology, we’ll talk with the most promising young scientists in the field. We aim to demystify the science for a general audience and to shine a light on how great science turns into great business. We hope these discussions will inspire scientists, entrepreneurs, and investors to help commercialize breakthrough research.
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Episode Contributors: Seth Bannon, Michael Chavez, Alex Teng, Nick Goldner
Episode Summary: Evolution is happening even at the cellular scale. Whether it's a virus, a bacterial pathogen, or a cancer cell, disease-causing agents are responding to the therapies we throw at them, updating their genes and molecular pathways to resist death. As a trained microbiologist, Nick Goldner and his co-founder Chris Bulow spent their years in grad school using -omics data to overcome antibiotic resistance in bacteria which led to their first company Viosera. As they struggled with the harsh realities of the antibiotics market, they stumbled upon the connection between bacterial and cancer resistance mechanisms. With this, they started resistanceBio which combines sophisticated tumoroids, intense patient sampling, and multi-omics to mimic the evolution of real tumors and ultimately find therapies that are irresistible.
About the Author
Nick Goldner is co-founder and CEO of resistanceBio, a company harnessing evolution to develop therapies that defeat treatment resistant tumors.
His interest in biotechnology was sparked by his own battle with treatment resistant bacteria.
Nick and his friend and labmate, Chris Bulow, knew they wanted to start a company and began Viosera to fight antibiotic resistant bacteria as graduate students.
Recognizing the inherent difficulty of bringing new antibiotics to market, they adapted their technology to cancer and spun-out resistanceBio.
Key Takeaways
Resistance is very similar in both cancer and bacteria – in response to a drug, both will change their phenotype in a way that reduces its efficacy.
Traditionally, we understand cancer resistance by growing cancer lines in a dish and evolving them over long periods in a way that is very different from what happens in the body.
Nick and his team developed ResCu, a method that cultures tumor cells as tumoroids that mimics how a tumor evolves during a patient's course of therapy.
Combining this with multi-omics, Nick and his team can untangle how the underlying resistance mechanism evolves over time.
The data that comes from this points resistanceBio toward therapies that will turn these resistances into vulnerabilities.
Translation
The drugs discovered through resistanceBio’s platform create cancer cures for people who currently have no options.
The data created through ResCu generate biomarkers ensuring that the right drugs are given to the right people.
With the foresight of how cancers evolve, resistanceBio could completely overcome the use of chemo and other non-targeted therapies that are hard on patients and instead have completely personalized therapies that are tailored to block all roads to resistance.
Company: resistanceBio
First Author: Susanna Elledge
Episode Summary: COVID-19 tests have become synonymous with jamming a swab up our nose to find out whether we have an active infection. But as we progress through this pandemic, a test that tells us whether people have antibodies against the virus will be massively important to creating public health initiatives and deciding who to vaccinate next. Unfortunately, these serology tests are exceedingly tedious to perform, inhibiting their widespread use. Realizing this problem, Susana talks us through how she utilized protein engineering to create a novel serology test that is massively easier and quicker than traditional methods. Importantly, this test can be used in resource low settings to help end the pandemic worldwide.
About the Author
Susanna’s scientist parents and love for the natural world drove her to research biology and chemistry.
Susanna is most excited about adding new dimensions to biomolecules through bioconjugation to enhance their function.
Key Takeaways
A serology test is used to see whether a person has antibodies against a specific pathogen.
Positive serology tests can tell us whether getting the disease led to immunity, whether a vaccine worked, or whether a person is protected from new variants.
This could be massively useful to help understand who is protected and who to vaccinate next to finally beat the SARS-CoV-2 pandemic.
Traditional serology tests use hard to scale and overly laborious methods that hinder their adoption, especially in a low resource setting.
Susanna used protein engineering and leveraged the shape of antibodies to develop an entirely new serology test.
She engineered protein fusions that when simply mixed with a human sample such as serum or saliva, will generate light if antibodies against COVID-19 are present.
This much easier test as well as the variety of human samples it can use as inputs make it a much more approachable option and enables its use in low-resource settings.
Translation
Susanna and her colleagues are working to make this test available for field studies by making the protein easier to ship and making a handheld device that can measure the readout.
Productizing this test will require more research in how to stabilize the components, incorporate controls, and most importantly, make it high-throughput.
Susanna hopes to leverage this technology to help us beat the variants of SARS-CoV-2 and eventually rapidly test for other infectious diseases and autoimmunity.
Paper: Engineering luminescent biosensors for point-of-care SARS-CoV-2 antibody detection
