Ashwin Gopinath and Anu Thubagere
This past year has been a singularly unique period in all our lives. Never before have so many individuals actively cared about their health or sort out information about different healthcare technologies. While this experience might have been somewhat daunting for most individuals, it wasn’t something new for our family. Not a day has gone by over the last 7 years when we haven’t thought about health or had a conversation about how every choice we made impacted our health. This is because one of us was diagnosed with leukemia around 7 years ago, almost to this very week. And, we have been constantly living with the reality of this diagnosis, tackling the side effects of targeted chemo, complications of the condition/treatment as well as several other associated issues.
One of the consequences of going through such an experience while being a family of engineers/scientists was that we started obsessively trying to understand various health issues and proposing new healthcare technologies to mitigate what we saw as open problems. Initially, this took the form of trying to understand the detailed biology of the particular leukemia; however, as the chemo started showing results our interest shifted to exploring methods that could enable acquisition of a rapid, high-resolution, overview of an individual’s health. This interest stemmed from the multitude of side effects of the targeted chemo as well as the general shock of a seemingly healthy, young adult getting diagnosed with a life threatening condition. Most of our professional efforts have also been driven by this same motivation, i.e. developing technologies that could form parts of a larger system that enables individuals to have a comprehensive snapshot of their health without the need for centralized facilities and interpretation of overburdened healthcare professionals.
What we have been exploring isn’t necessarily new or novel, several companies and researchers have been chasing similar goals with varying degrees of success. Talking about such an approach, especially in a non-academic setting, is however fraught with risks as the mere mention of this vision elicited the comparison to Theranos. Yes, that company headed by the Wannabe Steve Jobs that is currently being tried for fraud. Theranos’s technology, for making actionable health information accessible to everyone, involved miniaturization and automation of standard lab tests, like ELISA, into a single tabletop box. While we can talk at length about the drawbacks, issues, with their approach as well as their fraudulent activities, we think it’s more fruitful to discuss their compelling vision. A vision that is arguably much more relevant today than it was during the heyday of Theranos.
Personally, we often wonder what our journey through the leukemia diagnosis/treatment would have looked like had the theranos technology actually worked. On a larger level, we think it’s worth exploring the questions, “Would 2020 have looked the same had Theranos delivered on it’s proposed vision?”, “Would the severity of the pandemic have been the same if every individual could economically track the detailed state of their health on a weekly, daily, basis?”. We are not about to make the claim that a single piece of technology could have eliminated the pandemic. However, we most definitely believe that a radically decentralized, high-quality, platform for conducting comprehensive health screening is essential as we head into a post covid world. Such an approach would completely transform healthcare the same way smartphones changed how we communicated with each other. This also brings us to the question of, “Are there “real” technologies on the horizon (5–7 year time frame) that can enable this envisioned decentralized healthcare?”. We believe there is and it is firmly rooted in the emerging field of next-gen proteomics.
Proteomics as a field involving large-scale studies of proteins, strictly speaking, has existed for nearly 45 years and has been slowly gaining momentum. While the information within the proteome, the entire set of proteins, is extremely rich and actionable, this proteomic data hasn’t been in vogue in the public sphere like genomic data due to technical difficulties associated with its acquisition. It has only been in the last 5–10 years that the advances in biochemistry, machine-learning, automation and miniaturization have all come together to make truly large-scale study of the proteome a reality.
This emerging field of next-gen proteomics is, in our opinion, right at the precipice (next 1–2 years) of explosive growth that will fundamentally change how we study biology as well as healthcare. A great sign in support of this hypothesis is the growing number of peer-reviewed publications that use these techniques. Further, there have been several companies that have gone public over the last few months in this field like Seer., Nautilus, O-link, Quantum Si and perhaps soon Somalogic. There are also a few established companies like Quanterix as well as several companies in stealth modes that are slowly maturing with larger investments while pushing technological boundaries.
Over the foreseeable future we are going to try and put out 1–2 weekly article exploring this space deeper with focus on core technologies, existing companies, and application areas. Our aim is highlighting the unique challenges and opportunities that will open up with the maturation of next-gen proteomic technologies.
Disclaimer: AT builds Moonshots at X (former Google [X]); AG is part of the faculty at MIT and also has engagements with a few startups as co-founder or consultant. The views and opinions expressed here belong solely to the authors and shouldn’t be attributed to any organizations we work for or interact with.