Thrive in Science | AI, Automation and Authentic Leadership with Melanie Leveridge

Join hosts Ginger Cooper and Madeline Farley as they sit down with Melanie Leveridge, Vice President of Assays, Profiling & Cell Sciences at AstraZeneca and SLAS Board Member. Melanie shares how embracing innovation, leading through change and building strong relationships have shaped her career, while offering her perspective on the future of AI, automation and what it truly means to thrive in science.

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Transcript

Ginger C (00:34)
Hey everyone, and welcome to Thrive in Science, a new matter podcast series from SLAS.

Madeline F (00:39)
We are your hosts, Ginger Cooper and Madeline Farley. Today we are joined by Melanie Leveridge from AstraZeneca to talk about their wins and how they're thriving in science. Mel, we're so excited to have you on the podcast today. In addition to your deep background in assay development and screening, you've dedicated a great deal of time to supporting the broader scientific community, including your current service on the SLAS Board of Directors. So before we jump in, could you share a little bit about yourself and your career journey?

Mel L (01:06)
So first of all, thanks, Madeline, thanks, Ginger, for having me on the podcast. It's absolutely fantastic to be here. So yeah, I'm Mel Leveridge. My role is as vice president within our Discovery Sciences organization at AstraZeneca. And there I lead a global team supporting our discovery projects across all of our therapy areas in AstraZeneca. I've been with AstraZeneca for just over two years.

Prior to that, I spent 19 years at GSK in a variety of roles, most recently as Senior Director of Screening, Profiling and Mechanistic Biology and looking after the Global Sample Management Group. So I've had a number of different roles, but always been in the kind of early discovery space. How I ended up in pharma and spending over 20 years between GSK and AstraZeneca is a fairly straightforward story. So I did...

an industrial placement year, actually before I did my undergraduate studies with Merck Sharp & Dohme (MSD) when they had their neuroscience research center in the UK. I was in the cell culture group, I used lots of automation and to be honest I just absolutely got the bug in that year for working in industry. The intersection of biology with technology and automation

which is very relevant still obviously in my role today and was just something I absolutely fell in love with. It was a great group of people as well and so from there I really wanted to get back into industry and I joined GSK into a graduate role after my studies and that was really the end of that. And as you say also outside of my day job really passionate about working with the broader life sciences community. It's an absolute pleasure to be on the board of SLAS.

and I've been involved with SLAS for a number of years and also other other board roles supporting organizations like SLAS that support education and innovation in the life sciences.

Madeline F (03:08)
Thank you. That's fantastic. So ⁓ for the podcast today, let's start with a win. So what is something you've accomplished in your career that you're especially proud of? So something that's really meaningful to you?

Mel L (03:21)
going to go back a little way in my career for this one. So I'm going to go back to around 2010, 2011. I was, I think, a senior scientist at the time in the GSK Screening and Compound Profiling group. And I was asked to lead on the evaluation of a new technology, high throughput mass spectrometry. It was a new technology at the time. We were looking at that technology.

because we had a number of new targets, more challenging targets. pigenetic targets were becoming common in the portfolio at that time. And they weren't really suitable for kind of traditional assay technologies. For example, we didn't necessarily have good antibodies for those targets. And so we needed a new technology to help unlock those targets. And so we were evaluating this high-throughput mass spectrometry approach.

And so I led a matrix team evaluating that technology. We very quickly realized that this was going to be a very impactful technology. And then over the course of the next year or two, we brought that technology in and really embedded it into the whole of the GSK discovery portfolio. So the reason I picked that example, the reason that I'm proud of that work from a professional point of view is the way that we

really quickly evaluated that technology and then embedded it and it really became business as usual really very quickly. Personally, the reason that I like that example and that I'm proud of that work is it gave me a number of firsts, which I think then really laid the foundations for a number of roles and many of the leadership roles that I had in the time since. So it was my first Matrix leadership role, working with people from different backgrounds.

I had not done any mass spectrometry when I started on that work. So I was really reliant on the experts in the analytical sciences teams, for example, to help with bringing their mass spec expertise. And then I and others in the assay and screening groups could bring our kind of application expertise. So that was my first real experience of that broad matrix leadership and also of really having to learn something.

something new from scratch and being comfortable with not being the expert in the area, which is definitely a theme that then has continued in many of the roles, especially as having broader leadership roles, that becomes more and more of a theme, I think. And then thirdly, as well as the Matrix leadership being out of my comfort zone, the third thing that this gave me was really the beginnings of building my external network, including my engagement with SLAS.

So this work gave me the opportunity to publish, it gave me the opportunity to speak at international conferences like the SLAS conference, and really started to kind of widen my network ⁓ outside of the organization. And of course that has then been incredibly, a key part of my career development since then. So yeah, that example really does stick with me, I both professionally and personally.

⁓ And of course now, high throughput mass spectrometry now is absolutely, you know, embedded. We use it for many different approaches across the discovery world. it doesn't seem as new now, but at the time it was really a first for us.

Ginger C (06:41)
I remember that time. when high throughput mass spec was like, What are you talking about? Yeah, absolutely. Also, I can't wait to dive into the questions because corporate and quickly don't usually go together. And so

Madeline F (06:53)
I was immediately gonna go there. Like what really happened behind the scenes? ⁓

Mel L (06:55)
Look at that.

Madeline F (06:58)
So yeah, so you said the implementation was pretty quick and it and it went well. Were there technical challenges that were resolved well or was it really smooth sailing? or I I know you mentioned the reliance on the experts there. Was it really leaning into that that helped the efficiency? I'm curious.

Mel L (07:13)
Yeah, absolutely. think you nailed it with that. really was. So mass spectrometry is a tricky technique, as I think anyone that works closely with mass spectrometers would agree with. So having those experts around us was absolutely critical. And it really was a fantastic cross-disciplinary team that really made the work happen. I think the other aspect of being able to move quickly, and again, this is something

which is absolutely as relevant in my role today as it was in this example, is bringing people along with us on a change. So in this case, it was a change in assay technology. But of course, sometimes it's different types of change at the moment. We're obviously thinking about how we embrace AI in everything that we're doing. And that's another change that we're currently going through. And so the same things apply. But bringing people along with us as we went, so not just presenting.

final data set, but having project teams engaged with us on that journey as we were looking at the new technology, I think that that was a key part in moving quickly.

Madeline F (08:16)
Yeah, I I I think that's a theme that we've talked about in in previous conversations, Ginger and I as well, is sort of really understanding people's motivations and making sure they're connected, they're in the loop and and sort of walking through the process with them. ⁓ with that also being one of your first more matrix style leadership roles, are there any lessons you learn there from like leading people through change

Mel L (08:36)
think the early engagement, as you say, is absolutely critical. And, you know, as I say, I wasn't, you know, wasn't an expert in mass spectrometry, but I had to lean on the experts to teach me such that I could articulate to people really what the impact of the technology was going to be. And I think if people understand the why, then they're more likely to get behind the change. So I think that's, you know, early engagement, explaining the why. And then of course, you know,

showing data, we're scientists, we like data. So, you know, making sure that you're presenting data as you go to help people see the potential and get excited about the new capability.

Madeline F (09:16)
So this is a little bit of a pivot, you mentioned that this was one of your first sort of external networking opportunities and sort of how you became engaged with SLAS. So for someone that's sort of new to that, do you have any advice on how to make that like how to sort of ease into that networking and how to make those external relationships feel a little easier to navigate?

Mel L (09:36)
Of course, happy to share some thoughts on that. To be honest, think for me, it was having the opportunity to present at the meeting or to have a job to do, if you like, at an external meeting. think personally, just going into a room called to network is a little uncomfortable. That's, know, some people, a comfortable place. That's not the case for me.

So having a role, standing up and giving a presentation or taking part in a panel session or a discussion topic, it gives you a reason to be in the room. And then it also means that people come up afterwards after you've given your talk and start engaging with you. And then that very naturally leads on to a kind of broader conversation. They introduce you to other people. You start to build that network. So for me, that worked well because it, as I say, that work gave me.

a topic that I could go out and talk about and that in turn led to those conversations which in turn led to the network and as I say the engagement with the organization. And of course by being a speaker at an event you also get to engage with the SLAS team so then you get to know those folks and that then leads to conversations around how you might contribute. So it really starts with putting yourself out there a little bit and being willing to...

to have a role and a contribution in the external events.

Madeline F (11:01)
Yeah, I think that that intentional engagement, like you said, is really helpful.

Mel L (11:05)
Yeah.

Ginger C (11:08)
So I have a question for you. So, you know, you first of all, I'm really excited that I actually finally got to meet you at the Thrive and Science event in ⁓ in Boston because I've heard your name for years, ⁓ you know, at my previous companies. And ⁓ and I was like, ⁓ you're Mel. So immediately to finally putting a face with a name. you know, you talked about like

Obviously, like bringing people along for the ride, and that you weren't the mass spec expert and you're changing the way people do science, which can be really ⁓ sensitive, right? People people like to do things in the way that they like to do them, the way that they've done them for a long time. So I'm just wondering, like using this particular example, would you do anything differently if you had to do it again? Or are there things that you you learn to, you know, to make you stronger for the next time? Right. Like,

Things seemed to go really great, like corporate and quickly, like we talked about. ⁓ you know, and and maybe it was the right time for, you know, the innovation and the change in in the high-throughput mass spec. But I'm just really curious how you were able to get scientists on board so quickly with doing things differently or or if you would do it differently again. Yeah.

Mel L (12:21)
I think that, you know, we're scientists, as I say, and so we're very data driven. So the data in this case, was an easier change because the data was strong. And as I mentioned, we were enabling work that perhaps couldn't be enabled via other approaches. So I think that's important. It's not just identifying a technology for technology's sake, but thinking about what

gap or what challenge that technology can help you ⁓ overcome. And that's, you know, that again, that's something that we do all the time in my current role is we have a technology that perhaps is for one purpose, but then thinking about where else it could be applied. So, you know, in that example, mass spectrometry hadn't historically been applied in the screening paradigm. But of course, it wasn't a new technology per se. But we identified a problem that it could help us overcome.

And so I think that helps people really get behind it because they see how it can help solve that problem. So I think that that's a really relevant learning that, know, tools and technologies developed for one purpose can absolutely be applied in another area. We're doing that at the moment in AstraZeneca with our automation capabilities, for example. We have a big automation hub in our Discovery Center in Cambridge, and that was really built for high throughput screening.

of our, know, the AstraZeneca compound collection. But actually we're now seeing that we can use that automation in many different ways and solve different problems with it. I think, you know, that's a key learning. Keep spotting opportunities where one technology can, you know, have multiple applications and where people see it solving a problem, then they get behind it.

Ginger C (14:00)
Hmm. Would would you have done anything differently? Like did did you bump up against something in the particular example that we're talking about, right? ⁓ from, you know, your 2010, 2011 days, where you were like, you were like, ⁓ I I won't do that again, or I won't approach it in that way again. You know, it's some something that's made you a stronger matrix leader, you know, from that experience.

Mel L (14:25)
honestly, for that example, there isn't a specific thing that jumps out. And I think because the whole thing was a learning journey for me, because it was that first time of having that Matrix leadership role. It was a new learning on the technology. So I'm sure if I were to take on that equivalent project now, I would probably do it quite differently with another 15 years of having done the Matrix leadership role and so on. But actually, at the time, think...

those little mistakes you make along the way, you know, or maybe we didn't have that meeting, go quite how we wanted it to. Actually, those are the things that became the learning experience for me. I can't, yeah, I can't think of, I'm not probably answering that very well. I can't think of one single thing, but I think that's because it was all new to me at the time. it just, whole thing was a good learning opportunity.

Madeline F (15:16)
Yeah. I I love that because I think in hindsight, you we don't grow and we don't learn from our successes as much as we do from and not that this was a failure by any means, but it's going through the process and it being new is really where we're learning. And so like there was probably a lot of value in you in there for you, you know, even if with the 15 more years under your belt you might do things differently. I can appreciate.

Ginger C (15:39)
Yeah. But like I would have wished that in I had learned the bringing people along for the ride lesson in twenty ten instead of tw in twenty nineteen when I did.

Mel L (15:46)
been.

And, you know, and if I, I'm sure it took me longer at the time to work through that, then, you know, then it might now, right. But, know, it's, as I say, it's a great learning opportunity.

Madeline F (16:00)
That's awesome. So through your career journey and through life, is there anyone specifically that has influenced you or helped contribute to the way you lead or the way you approach science?

Mel L (16:14)
to say it's no single person for me but more a combination of a number of mentors and role models that I've had throughout my career and they've come in different guises. So I've had formal mentoring relationships and coaching relationships for much of my time in industry and those have been really valuable when I've had a particular challenge to overcome or

if I've just really needed a sounding board or working through a particular scenario. But if I'm honest, it's really the informal mentoring relationships that have probably had the most impact on shaping me as a leader and shaping my career. And there's probably a handful of leaders, managers that I've had throughout my career and I won't name them, they know who they are.

who have really influenced life. I've taken little gems of when I've seen them in a particular, whether it's standing up in a town hall and how they handle that, or whether it's seeing how they manage through change. And those little gems, that's really role modeling a behavior, a way of working that resonates with me. And whilst you of course need to be authentic and lead in your own way, I think you can incorporate those little gems from people into the way that you lead.

And one particular example that literally did change the course of my career was a really informal, wouldn't necessarily call it a mentoring interaction. I was just having lunch with one of those leaders, those role models who was as a leader in GSK at the time. And we were just having a lunch conversation and they asked me if I would be applying for a department head role that had become available. And I very quickly answered no, I had no intention of doing that.

⁓ And then they very informally and very casually just teased apart why I was thinking that I wasn't going to apply for that and just nudged me enough without telling me, without pushing me, but just seeded the thoughts that perhaps I might want to think about doing that. And of course, then you go home and you reflect on that interaction. And of course, the story ends that I did apply for the role and I did get the role. And that was really my first kind of big...

department leadership role back in 2015. So that literally changed the course of my career. And as I say, it wasn't a formal mentoring, but just that support network, I guess, that someone's willing to sit down and have lunch and just give you the little nudge when you need it. I think that for me has been the most valuable type of mentoring and inspiration that I've had.

Madeline F (18:51)
Yeah. That it makes sense. And it's almost like this outside perspective, I think, for both of both of your responses there. It's when you see someone effectively do something and you're like, that went over really well. What what was it? How can I distill out the way they approach that and handled that? And also that outside perspective ⁓ that just maybe challenges your point of view. I think both of those are so important. And you I almost wish I had a notebook sometimes just to write down. When you see something like that, is it really successful, you're like, how did they do that?

Mel L (19:21)
Yeah.

Ginger C (19:21)
I I feel like when you when you come across leaders that resonate with you, it's great. Right. Especially as like because when I when I come across them, I'm like, wow, I love their leadership style. And also in the con like in the opposite, because I think experiencing leadership styles, either directly or indirectly, that don't resonate with you and don't work for you help you understand as well that it's not a one size fits all. There is not one approach, and one approach to leadership doesn't work for everybody.

You know, and I know like it it's interesting when you talk about not a single person. Madeline, I feel like this might be a theme throughout our ⁓ conversations where it takes a village, you know, and there's not, you know, one single person that maybe stands out, but it's it's these these people along the way who have the conversations, who show up in a different way, who give you an aha moment without purposely setting out to give you an aha moment.

They that usually have the strongest impact.

Mel L (20:21)
And sometimes you don't realise that you've had that aha moment until later. You just, you you absorb, see them, you know, in action as a leader. And it's only afterwards when either you find yourself in a similar situation or just when you have time to reflect and you think, wow, they did that really well. That's how I would like to show up if I'm in a similar situation. But you don't always realise that in the moment, I don't think.

Madeline F (20:43)
Yeah, I I think it's so important to and it's hard to carve out time for that sort of reflection so we can have those takeaways. ⁓ but yeah, I think that's really important. So moving on to another topic. I'm curious because I know you mentioned the application of automation at AstraZeneca and then the sort of repurposing of it in in different areas. and then we also mentioned AI, which is obviously like

You know, everyone's talking about it. It's we're trying to incorporate into almost every aspect of life science and drug discovery, right? So do you want to speak a little bit to maybe where you see the industry going next? It could be the context of maybe what what you're working on or in the broader context of lab automation and and drug discovery.

Mel L (21:22)
Absolutely. So I've touched a little bit, as you said, on this already. I genuinely do think we are at an incredibly exciting inflection point in the discovery sciences. And of course, AI and then AI coupled with automation to generate large foundational data sets is enabling us to accelerate our science in ways that we wouldn't have thought possible a couple of years ago. We've seen

you know, just recently some, some really incredible developments in AI specifically for bioscience applications and the idea of having essentially an AI, you know, collaborator embedded in our scientific workflows, not, replacing the scientists, but augmenting the scientists and really augmenting what, they can do, you know, helping generate hypotheses, helping analyze complex data sets. And I say that coupled them with

automation to generate large scale, really high quality data sets that can then feed into prediction tools is just really, really powerful. And we're going to have scientific insights through that that we just simply don't have today. So of course, I think that's a really big, big area of focus for me and for my team. Beyond AI and automation, think the other area that's worth mentioning

is the new modality space or the explosion in the number of potential modalities that we can use to ⁓ intervene in disease. So for example, if we think about antibody drug conjugates and radio conjugates potentially replacing backbone chemotherapy and radiotherapy, and that's incredibly exciting to target those therapies in a much more precise way. And then through

some of the AI insights, we're then unlocking biological insights that might enable us to diagnose and treat disease much, much earlier. So this paradigm that we talk about moving from sick care to healthcare, where we intervene before disease really starts to progress, again, that's going to be really transformative. So, you know, at the moment we are really seeing this unprecedented pace of technology development. you know, so for me and my team, it's around

maximizing how we're using those technologies to essentially find molecules against increasingly novel targets and then be able to develop those molecules and optimize them at pace so that we can hopefully deliver the next generation of molecules into the clinic.

Ginger C (23:57)
Did you ever think because I'm always I'm fascinated by where we are with AI, right? You know, and how it's finally being incorporated, you know, into the sciences. you know, I've been in like the digital lab arena since 2016, you know, where robotic cloud labs were talking about closing the loop. And I was like, what are you talking about? You know, ⁓ you know, and you've been around automation and in the like the like automating sciences for a long time. Did you ever think that we would get here?

Mel L (24:26)
I said automation has obviously been a feature of my work really throughout my time in industry. I mentioned that one of my inspirations in that year, that placement year at Merck in the early 2000s was getting to apply automation to biology. So I think that the principle of automating and scaling biology, I think has always been a feature. But the difference now I think is the

the flexibility in that automation and the connection between the automation and the software orchestration and AI. And honestly, I don't think if you'd have asked me, I was gonna say 10 years ago, maybe even five years ago, I think I would have imagined that we would be where we are today with that. So as I say, I think that the pace of that technology development at the moment is incredible.

probably beyond what we could have predicted.

Ginger C (25:25)
Amazing.

Madeline F (25:26)
Yeah, I think the acceleration and pace is fascinating. and and you almost have to wonder, like thinking ahead to five or 10 years, like where are we going to be at that point? and and not that we need to get into the details of it, but it's so much of because earlier we're talking about getting people's buy in early and making sure they're they're on board for the process. And so I think that's a really important part to where we are right now is making sure that those workflows and the integration of AI and the

data that's backing up all of that work that that we have that buy-in that people truly understand what's happening and it's not this black box. so I'll be really curious to see how it develops and and where we are in five years. But

Ginger C (26:02)
Yeah. You know, I have to say though, I when you started out, Madeline, with you know, we're we're incorporating AI into everything we do. I saw a meme just the other day that's like, I feel more pressure to use AI in my everyday life than I ever did as a teenager, like with peer pressure. You know, like and it and it's and it's true in a way, but I think, you know, Mel, what you're talking about and Madeline, what you just said, you know, where there's data to back it up in the sciences, right?

And Mel, I think you said something that resonated with me is that it's it's augmenting, right? It's more like the assistant, it's the it's the helper, not not the scientist, right? You know, though maybe there are ways, right? I don't want to get into, you know, I that that's probably a larger debate around that. But I think enabling the speed and taking some of the more error prone steps.

you know, along the way is really the benefit of AI. You know, ⁓ it can help in a lot of ways, but I think in the in the sciences in particular, it's that, right. And I think Mel, when you said augmenting science and and using it for, I don't know, particular things, not everything.

Mel L (27:19)
Absolutely. think that, first of all, you mentioned the critical importance of the underlying data. of course, that's where the automation comes in and can really help us build those incredibly large scale, but also high quality data sets. So that's really, really important. And we talked earlier about bringing people along with change in technology and that really when people see

the technology solving a problem, they see it accelerating, it makes their lives easier, then that change becomes much easier to adopt. I think that's where we are now with AI. People have it in their hands and they see that it can, as you say, accelerate what they're doing, means they can spend more time doing the parts of their role that they enjoy.

because it takes on some of the administrative burden. So as you say, working together with the technology is really an opportunity that we've got right now.

Ginger C (28:15)
I feel like I want to like coin you like our chief change leader because you're really good at driving change. so we, you know, the at the the podcast is Thrive in Science, you know, we're we're focused on, you know, that the topic and and really, you know, around women in science. But you know, we're curious and we're asking all of our guests this. you know, what does like thriving in science mean?

To you.

Mel L (28:41)
First of all, I love that question. It's a great question. And it really made me think, what does thriving mean to me, let alone thriving in science? And what I thought about it, really, for me, it comes down to the people around me and the impact that we're having together collectively. So for me, it's about working with a fantastic

team which you know I absolutely do really fortunate that work with a brilliant brilliant team and also having that great support network you know the example I gave earlier of that one conversation that can change the course of my career you know having a support network around me both inside and outside of work working with people who will celebrate successes and encourage each other have some fun along the way as well.

but also people who will support, you science is challenging and things don't always work and making sure that the people around me can support when things don't go well as well. I think that's really important. So that's one piece, think having, you know, being surrounded by great people who will lift each other up and also, you know, celebrate not only the successes, but also the failures and learn from them. Also for me thriving.

in science specifically is about being in an environment where we're really encouraged to innovate and to push boundaries, know, especially in discovery sciences, I think we have to keep doing that every day. And, you know, again, I'm very fortunate that I'm in an environment that really does encourage that innovation. So I think, for me, from a...

Work and a science point of view, it's around the people, it's around the innovation and being able to explore new areas and see the impact of those. But also I should say, I think to really thrive, it's not just about work either, it's also about thriving outside of work and finding time for the things that you enjoy. So for me that's spending time with my husband Matt and my dog Roxy.

and finding time for horse riding when I can and for some non-work related travel. you know, think having space for those things as well makes me a better leader, a scientist. if I had to sum up what does Thrive in Science mean for me, I would say being surrounded by brilliant people, working on problems that really matter, and then having the space to really be myself while I'm doing that.

Ginger C (31:03)
I love that. And I think ⁓ you know, your your innovation, the environment right where you're encouraged to explore new. it sounds like you've really been you've you've been in that space almost your whole career, which I think is amazing. You know, to to find the, you know, the I think the comfort in in the new and the innovation and the change is fantastic.

Mel L (31:28)
Yeah, it's a space I really enjoy working in. I think in the discovery sciences, everything changes very quickly. Science does, I say especially at the moment, the pace of innovation and technology change. I think you have to enjoy that to thrive in that environment. But yeah, it's certainly something I really enjoy about my role.

Ginger C (31:52)
Yeah.

Madeline F (31:53)
I think that was a beautiful answer. And I think it really summed up the conversation that we've had today because it centers really on the innovation and the change, but then that community that's so important. both in like supporting oneself, but also in supporting that community through the change and through those technological advances and innovation. And so yeah, I think that really sum like summarizes kind of where we ended up today in that conversation, which I I I enjoyed.

Well, thank you so much, Mel, for joining us today. I think this was a great conversation. and thank you to our listeners for joining us. if you enjoy Thrive in Science, please be sure to follow along the podcast for future episodes and we'll see you next time.