Meet the Researcher: Nicola McCarthy, Milner Therapeutics Institute

DDW’s Megan Thomas talks to Nicola McCarthy, Head of Research at the Milner Therapeutics Institute. 

MT: Could you start by giving me your name, your job title and what you do? 

NM: I’m Head of Research at the Milner Therapeutics Institute, which is part of the University of Cambridge, and I oversee both wet and dry labs of the institute.

MT: Can you provide a top-line summary of your research? 

NM: So, what we’re interested in the Milner is basically partnering between pharma and academia. We have a big consortium agreement that, at the moment, involves 14 pharma companies. We then work to partner them with academics working at the University of Cambridge – Babraham Institute and Sanger – to see where they’ve got coalescing ideas and interests. It’s a way of pharma being able to dip their toes in new areas and de-risking from that point of view. From the groups that I actually oversee in the wet labs, we’re developing new models for arrayed CRISPR screening. So we’re looking to work with primarily human material as much as we possibly can. Then in the dry labs, which is headed up by a bioinformatician, we’re looking at big datasets – patient derived RNA seq data sets, for example – to see whether they can be mined to try and identify new potential targets.

MT: Where do you think the biggest opportunities are in this field? 

NM: I think it’s moving into primary tissue, certainly for the wet lab work that we do on the arrayed functional genomics. It is very much a case of moving away from the cell line models, moving away from 2D models, and moving into complex co-cultures with human tissue or organoids, for example – patient derived or IPSC derived. So, working out ways of using all the information that’s been gathered from working in the cancer cell lines to see whether you can make those models scalable and robust enough to translate them into the pharma pipeline. So that’s kind of where we’re  working with that and I think, again, the ability to bring deep machine learning and AI into the big datasets is one of the [opportunities].

MT: What are the challenges in taking this to market? 

NM: I think really it’s the scalability and the robustness in terms of the wet lab work. It’s trying to get those models so that they can at least handle a substantial gene knockout set. Also that for every biological replicate, you get enough reproducibility well by well, and plate by plate. I think those models are the real areas that we need to solve at the moment. In terms of the big data from the dry lab side, I think it’s really coming up with the right training sets and the right way to address biological questions or patient derived questions, such as ‘Can you identify patients that are going to respond to drug X or Y?’ I think we’ve made a really good start in that area. But I think there’s a lot more work to do.

MT: What has been the highlight of your career so far? 

NM: I think it’s variety. I’ve worked in academia, publishing, biotech, and then back into academia and I think meeting lots of different people with lots of different ideas and being able to learn throughout my career, no matter what I’m doing, has always been the major highlight for me.

MT: Which industry-wide drug discovery breakthrough has been most impactful to you? 

NM: I think probably it’s big data. When I was working with Nature Reviews Cancer, we saw a lot of the development of big, deep learning to sift through all of the omics data that was coming out in the cancer research field. I think in terms of wet lab, for me in the last 10 years, it has really been CRISPR. That’s been the big mover.

MT: What is the best piece career advice you’ve received?

NM: Always do what you love, what you have a passion for, because you’re going to be doing it for a long time. You need to enjoy it.

MT: What advice would you offer someone looking to follow in your footsteps?

NM: Do not allow yourself to be pigeonholed. I think that for anybody with a scientific background – or even any background that they’ve got a deep knowledge in – you pick up all sorts of different tools. You can apply them anywhere. So really, you can work wherever you choose to work, and you shouldn’t let people say ‘you’re not quite right for this’.

MT: Has that got better in recent years, or is that still an issue? 

NM: I think it’s got better. I think when I was first working, it was very much a case of you were either an academic or you worked in industry, and never the twain shall meet. I think that’s changed massively now.

MT: If you could make everyone read one book, article or academic paper in this field, what would it be and why?

NM: This was one of the questions I found really difficult to answer, because I think things that inspire you are very individual to your journey. I would just say to remember the papers that you read, maybe when you were an undergraduate, and and thought, ‘wow, that’s really fascinating’. For me, it was a lot of the work of Paul Nurse and the yeasr biology work that he did, and reading Richard Dawkins’ Selfish Gene that was inspirational to me. I think it’s about remembering that passion that was ignited at that point.

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