Prof Michael Vynnycky is a professor of applied mathematics in the Dept of Maths & Statistics at the University of Limerick’s Faculty of Science & Engineering. In this interview he discusses his role in a collaboration between Research Ireland centre for pharmaceuticals SSPC, The Applications Consortium for Science and Industry (MACSI), and Varda Space Industries that could change our understanding of how pharmaceuticals can be manufactured in space.
Tell us a little about your career to date
I did my undergraduate and postgraduate degrees at Oxford. My original PhD is in Applied Mathematics. I’ve been in UL for five years in my current position but I was also here from 2008-2014 when MACSI started. At other times I’ve been in different places like Brazil and Japan.
Generally, I’ve been trying to develop mathematical models for various processes that occur in natural science and technology. That’s how this project became one of the things I was involved with.
I’ve been on a lot of projects where industry has been involved, but I’ve tended to stay on the academic side of the fence.
I had two periods working in Sweden, it was actually more towards material science and engineering. I have had some experience that hasn’t been completely from my original background, because a lot of these projects need you to be able to see it from the from the engineering point of view, where the mathematics is a tool.
Has an appreciation of the engineering point of you helped your work?
Having spoken to a lot engineers, I think it helps. When one goes into a meeting with a new potential industry or research partner, I think it helps to have the slightly wider picture. So not just not the equations, but what the equations are going to be used for, and how they are going to be useful for someone.
One of things with applied mathematics, is that as we go along our journey we learn to think more on our feet. What’s common for us is that we may see an idea that we use in an engineering context, which may be useful or may prove useful in another, whereas the people who are actually doing just the engineering might not see that link.
I might have equations used from an old project, which I’ll pick up as part of something I’ll need for this new pharmaceutical application.
Tell us more about your current work with Varda Space Industries.
Varda Space Systems called SSPC and they seemed clear they wanted somebody to get involved on the mathematical side. There is a fair amount of literature on crystallisation processes, but it’s just this combination of crystallisation and in this microgravity environment, and what’s called ‘polymorphism’ – the possibility that when crystallisation occurs, it could be in different forms. It was this combination of things which, I thought, we could probably do something mathematically with. Then it turned out that they were interested in going further.
You were making the point about using material on other projects. Could this work be used for something else?
I didn’t have a particular application in mind for what we were doing now. On one the one hand, maybe academic curiosity. There doesn’t seem to be many applied mathematicians working on crystallisation in general. I thought, if we start on this, we’ll learn something, and maybe further down the line we may get other industrial interest.
I don’t think Varda is the only company who have this idea of making pharmaceuticals in space. I think it was maybe like investing in the future, that if we teach ourselves this stuff, it could come back in some other way that might help us. There have been people doing material science experiments in space since the 70s and 80s. So in this sense, it’s not a new thing to be doing, but it’s the context of doing it with pharma that is.
Are there any other areas that are interesting to you right now?
Everybody talks about artificial intelligence (AI). I am, probably from a slightly older school, so I prefer the methods that we’ve that we’ve always used. And I think there is a place for those, but just the fact that this, AI in principle, could be applied to multiple regions and it’s being applied even at the moment.
We’re not making use of it in this particular project. AI still feels like we haven’t really understood it in the same way as I feel when I bury myself in equations and then and then and then come out. You know, so, so that I, and I think for me personally, that that’s yeah, it, it will always feel inferior to that, to this learning process but there’s no doubt that that it seems very useful.
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