Earlier in my career, in the late 90s and early 2000s, I had the opportunity to work in the “global e-business” groups at two large pharmaceutical companies. At that time, e-business was a new noun – a word widely used across industries but with so many different and emerging meanings. In healthcare, e-business was the start of a new era of data collection and information delivery across the industry.
Nine months ago, my peers and I marveled at how far we’ve come in 20 years with the convergence of biotech and tech, the genomic revolution, the way in which data is helping segment patient populations for more targeted therapies, and the emergence of machine learning to advance research and discovery in healthcare. “E-business” is no longer a noun used anywhere, but it is very much part of everything we do in business and in healthcare.
We are now in the midst of another transformation of the use of big data in healthcare. Collaborative efforts throughout the industry, often propelled by technology like AI, have accelerated our ability to obtain information about the biology of the SARS-CoV-2 virus and how people across the globe are responding to it. This is fundamentally altering the way therapeutics and vaccines are researched and developed – in record time – so that patients can get the care they need.
At Adaptive, we are focused on how people across the globe are responding to the virus. By decoding the adaptive immune response at the individual and population level, we aim to make sense of the interaction between the virus and the human body from exposure to infection to recovery. This is a very hard but solvable big data problem if you have the right technology and the right minds behind it.
In 2018, Microsoft and Adaptive forged a partnership to bring together MSFT’s machine learning, AI and cloud computing to our immune medicine platform – accelerating our ability to map the trillions of T-cell receptors to the millions of clinically-relevant disease antigens to which they bind. As my colleague, and partner, Peter Lee at Microsoft, once said, “the adaptive immune system presents an extremely large but beautiful machine learning problem.”
Why? Because our immune cells have evolved to be massively diverse and dynamic to protect us from millions of different signals of disease that our bodies encounter every day. And each person’s immune system is different, which is why people are reacting so differently to this novel coronavirus. Every single person presents the virus a little bit differently to their own immune system, and we have evolved this way as humans to ensure that no virus or germ can completely eradicate the human race.
Since the immune system sees SARS-CoV-2 just like it would any other virus, we were able to quickly mobilize our platform to map the T cell response across thousands of people from around the world. All of the T cell response data that we’ve collected so far is being made freely available to the scientific and public health communities to help accelerate solutions.
At the same time that we have been generating and releasing this T cell mapping data, researchers around the world have been publishing studies showing that antibodies don’t tell the whole story about the immune response to the virus. We now have the ability to broaden the definition of immune response to include the T cell response at scale to inform more accurate and effective diagnostics, therapeutics and vaccines.
The pandemic has spurred many novel partnerships and innovations to expedite and make drug discovery more efficient, breaking down barriers as we band together as an industry to advance solutions as fast as technology can glean new data. Take for instance, the global COVID R&D Alliance that is bringing together 20 of the most experienced life science and drug development companies – like Schrödinger, Amgen, Pfizer and others – to identify, study, and accelerate promising treatments for COVID-19. Recently, Google Cloud joined this alliance, donating over 16 million hours of high-computing technology to advance solutions.
Thanks to AI and other novel technologies, today we can harness data to crack the code of disease and the immune response to disease in ways unimaginable, until now. As data continues to emerge at an unprecedented rate about the SARS-CoV-2 virus and the immune response, it’s clear that there is no one-size-fits-all approach. Only by blending the best minds with the best of science and tech can we continue to make a true difference in how this and future pandemics are defeated.