Over the past 80 years, we’ve made amazing strides in biotechnology, curing some of the world’s worst diseases. But just like our drugs have been improving so are the diseases we have to face. This includes not only new antibiotic-resistant superbugs, but cancers which are actually killing more and more people every year. Chemical drugs aren’t keeping up anymore so we’ve turned to things like genetics and immunotherapy but even then, there needs to be a drastic improvement in the way we discover and design drugs.
These improvements come in the form of:
1) Drug Personalization
2) Faster Discovery time
Immune 2.0 is a platform that aims to leverage machine learning and immunotherapy as a means of speeding up and personalizing the drug discovery process. By looking at how disease interact with our immune system on the molecular level, we can make accurate predictions about future interactions. These predictions can then be used to derive the perfect receptor or antibody clonotype to fight a specific newly diagnosed virus, bacterium or cancer invading a patient. With simultaneous strides in CAR T-cell therapy and genetic editing, these technologies can be used to design the perfect cell, derived fully from the patient’s body.
Using Immune 2.0, we can replace outdated technology used in discovery and developing immunotherapies (e.g ELISA) which rely heavily on trial and error methodology and instead, help increase the development speed of more accessible, personalized drugs.
You can learn more about Immune 2.0 using the links below:
What inspired you (or your team)?
I got into genomics a few years back after reading Jennifer Doudna’s “A crack in Creation,” which as a kid obsessed with science, makes you really excited about the future. But then I realized that even with all these new technologies, drug development is far from it’s maximum potential. Drugs are still taking years to develop leaving people to die and it was becoming harder and harder to find new molecules to fight new diseases.
This led me down two paths, the first is immunology, which was a mind blowing field of study. The intricate complexity of how your immune system fit together was captivating. I started looking more and more into things like monoclonal antibodies and CAR T-Cell therapies, which were great treatments, far better than chemical drugs. But then again, they were plagued by slow development processes which raised costs making them unaffordable.
The second path, was software, specifically machine learning. I talked to a lot of people working with traditional drug discovery using things like GANs to decrease the time it took to find viable molecules and ligands. The technology was great, but I didn’t meet anyone working on leveraging the software to help with immunotherapy drugs specifically.
Seeing the potential in a tool that aided in the development of already great therapies and on top of that, providing patient personalization, I got started working on Immune 2.0