When I started Telescope, it was to encourage myself to build my network — to connect with industry leaders in STEM and to share the knowledge and advice I acquire to future generations. This week I had the pleasure of talking with Alison McGuigan, a professor at the University of Toronto working with tissue engineering to innovate an accelerated method of drug discovery.
It takes on average 12 years to develop one drug. Which is just absolutely insane considering that every three minutes, two people in the U.S. will lose their lives to cancer. Not only does it take forever too, but it’s incredibly expensive. $6.6 billion expensive.
The current method of drug discovery often starts in 2D plastic dishes where compounds that kill cancer cells can be found. This is often the cheapest and quickest part of the process. This then moves into test tumors in animal models and later in extensive human clinical trials, where high failure rates push up costs and time.
With this problem at hand Professor McGuigan asked if there were methods where failing earlier was possible — in that initial plastic dish. In the past, these plates couldn’t predict how the drugs would react in real working environments, as cells on a 2D surface behave differently than in 3D living organisms.
I first heard about the project through a TedTalk back in 2015. The idea to reduce time and cost was to add an extra step, where the concept of the plastic 2D plate and the clinical trials would be merged. The 3D environment would exist, yet the cells would be able to be easily analyzed like in the dish.
And this would be done with engineered tumors, a method known as the “Swiss Roll.” By rolling and unrolling a sheet where the experiments were conducted on, this need was met, where both a 3D and 2D structure could be provided at the same time.
The interview below delves more into this ingenious innovation, where it’s at today, and many of Professor McGuigan’s experiences along the way.
The development of medicine is a difficult one. Tell us more about why finding new treatment for diseases like cancer is such a tricky business, and how this problem inspired the concept of the “Swiss roll.”
These diseases like cancer are very complicated and are system level diseases, where the whole biological tissue is changing. It involves many different cell types and the way that it’s progressing might change over time. And there’s also going to be different baseline gene expressions from person to person. So when finding molecules that are going to do things like kill a cancer cell where there are different subpopulations within a tumor, these drugs might work on some but not others. Thus, finding some combination of drugs or even a molecule that affects those cells and not other cells in the body is a very hard thing to do.
One way to go about it is to look at lots of molecules in a simple system so that it’s easy to scale and observe for an effect. However, while those types of systems will capture certain biologies, they will not capture the more complicated ones. So from there you would have to move onto a 3D culture model, then a mouse which captures more and more biology and gives a better indication of the behavior of the drug in a person.
So really, the Swiss Roll is another part of that tool kit to help researchers prioritize which drugs are going to have an effect in a human. When you put something in a person, doing those experiments are very expensive and you want to do no harm to the person.
Another aspect is that medicine is trialed at population levels, maybe in some subsets of patients, but to take that to an individualized level is an even harder problem.
You first introduced this idea back in a 2015 TedTalk (which I loved by the way). What is your team working on now and what advancements have been made to the concept since then?
Our original culture model was done with cell lines, which were established cells bought from a company, and were adjusted to grow well in dishes. And because they had been optimized to grow in dishes, they were generally not a good representation of the cells from a tumor.
So what we do now is we partner with colleagues at the hospitals so that we can put patient derived cells into our system and add in immune cells.
Another aspect is ensuring our manufacturing is very consistent. This is so that when we make these models, we know that if we see a difference, it’s because of the compound we treated something with versus a problem with the manufactured product we’re using. And this is very important as these are all very complicated structures we’re working with and building.
Inventing an improved method of drug discovery is hard; it’s never been fully achieved. What were times when a wall was right in front of you and you just felt stuck? And what do you tell yourself when things get discouraging?
In this sense, when you feel stuck isn’t like feeling stuck when looking at a problem set and you don’t really know how to approach the problem. I think what we worry more about is if our system is robust enough to copy what’s going on in the body. And usually it’s very confusing when there is a lot of data.
So what I usually encourage people to do during these moments is to organize that data, to step back and have it systematically laid out. Also, talking to someone else to get an outsider perspective, checking that we’re not thinking about it the wrong way, is very helpful. And then if there does seem to be an issue, is there any way to pivot or change the scope on which the model might be useful? Is there a different problem that takes advantage of the solution?
If you don’t have periods associated with feeling “stuck” it may mean the problem’s not hard enough or has been solved already. And I think that feeling of being disoriented is a big part of research, and learning to manage that disorientation and being able to make an action plan is a huge part about growth.
I see you as someone who radiates with passion towards biomedical engineering. For younger readers who aspire to become involved in the medical field, what was the most rewarding aspect of taking this path?
What’s really rewarding is to be able to connect and discover things with teams of people. A big part of biomedical engineering is working and collaborating with other groups of individuals, and getting to share discoveries is something truly exciting to be able to do. And hopefully in the long term, the most rewarding thing you’ve done is something that has had an impact on these problems and improved the lives of other people.
What is one thing that gets you excited to get out of bed in the morning?
I would say something very similar — getting to interact with people who are very excited with what they are doing. That’s something you certainly don’t get in every community and a very privileged thing to have. And again, knowing that you get to work on problems that will have an impact and make the lives of others better is an incredibly exciting aspect.
When was a time in your life when you took an unconventional path? What were your initial gut feelings, and how did it influence the person you are today?
I find this question a bit more difficult because technically all paths are unconventional. A sole conventional path doesn’t really exist. There’s always going to be points in your career, in your professional and scientific development, where you’re not sure if you’re making the right decision or not. And you have these gut feelings, but you’re not sure if it’s a hundred percent the right thing to do. Typically in these situations, this is where mentors and perspective from others is very helpful — to help you understand which path might be the most rewarding for you at that particular time in your life.
Most people probably feel their path was unconventional and if you have good mentorship, then you’re going to be able to navigate that feeling more confidently, knowing what to value at each decision point. And so for how this influenced me today, my encouragement to others is that if they stumble upon a point when they know their decision is not reversible (although most decisions are actually reversible), those are the best moments to reach out to their network and understand how to make and evaluate that decision.
And to end everything off, what is a book, video, quote, movie, or anything else that you would like to leave our readers with today that is one you still think about?
The Alchemist was a very good book and the one about networking called the Tipping Point was also a very interesting read. There’s also the Outsider.
A quote I still think about today is “You have to know when to jump off the sinking ship.” It’s the hardest thing to do. I think it’s quite good advice. If you’re going to experiment or try something out then you have to know when an idea is not worth pursuing anymore. And from there try to collect what you’re learning from it and find a way to repackage it so you can still get something out of it. Remember for every good idea, there also has to be bad ones, so if you don’t have any bad ideas, statistically it means you might not stumble upon the good ones either.
Note: This interview was edited for clarity only.
