By: Arooba Ahmed (CC '23)
Could you describe what your company does?
We are a technology company that uses AI based methods to discover novel biologic drugs, specifically biologic therapies. Basically, we use AI to discover, design and develop antibodies.
How does the algorithm that your technology uses work?
At what stage is the coronavirus research in your company?
We are in the laboratory right now, and we have designed our own antibodies in house. We have optimized them computationally and sent them to the lab to be made and tested, and we anticipate having those results in the next few weeks. Based upon that, we will move on to laboratory testing in humans and animals. At that point hopefully the drugs will be ready to go into what is called “IND filing,” which stands for Investigational New Drug filing. That is the document that you file to get permission from the Food and Drug Administration to conduct human studies. If I were to guess, we are about 5-6 months away from doing human studies. We are sort of looking broadly at the antibodies themselves to target different mechanisms that the coronavirus utilizes to both infect and procreate. The coronavirus has a lifecycle, so we have to consider where in the life cycle we want to impact it.
Do these studies depend on lab research?
For the coronavirus, it does heavily rely on the research on the structure of both the coronavirus and its target in the body. It also relies on huge amounts of data. We are relying on the order of about a billion sequences, within which we use selective amounts of data to train our models. And that changes from antibody to antibody and biologic to biologic. We also are actually not just working on coronavirus projects but also oncology projects and botanical medicines.
How does computational testing and development of the antibodies make the process of drug development easier?
This is a significant drop, where it is reduced to less than a quarter of the time. But we still have to go into the labs and make the antibodies as opposed to just designing them, which is nuanced. Once you make them you would still have to do the same tests as when you discover them. But the way we work is that we try to get rid of the ones which will have higher chance of failure downstream.
Drug development has many steps, like discovery, optimization, looking to see whether it's safe, conducting human studies and looking into efficacy. Any one of these could lead to failure so when we are designing, we try to think about all of these parts and not just the very beginning. We still have to go into the laboratory and make it optimizable using animal and human studies. But the idea is that we are trying to fail in the computer as much as possible so we fail less in the laboratory and make the process faster overall.
Has the closing of labs and social distancing impacted the efficiency of your company?
It hasn’t impacted us at all really. If anything it has made us busier because lots of companies and organizations can’t operate in the lab now. We are starting to see some changes as they are reopening. Lots of labs that were open for a short period of time were the ones focused on coronavirus research.
How has the recent drop in regulatory restrictions impacted drug development?
You don’t want a coronavirus test that’s 60% effective, but 99% accurate. The standard for the FDA is usually really high but they dropped that to get as many tests out there as possible.
With the turn towards technology in this time, have you seen a change in the role of data science in research related to the coronavirus (and science in general)?
I think that the pharmaceutical industry has been doing data science for a really long time. Actually, I think that the first major data visualizations were in the 1700s, around epidemiology, centered around trying to figure out where Cholera in London came from. If you think of epidemiology as part of the medical industry in terms of determining drugs and infectious diseases, data science has been impacting it for a really long time. Especially for biostatisticians, or people who design clinical trials. I think that what we are seeing now is more utilization of machine learning in this space. I think it has to do with the fact that machine learning and data science onto itself has come to a forefront in a way that it had not until about 15 years ago. Mostly because of our computational capabilities. And a couple weeks ago, a major pharmaceutical company got a new head of research who is probably the best computational biologist out there. So we are starting to see a huge shift in the utilization of data science and I think it's going to keep growing as companies start to realize not only the value of the data they have but the value of extracting knowledge and action from the data itself.