Latest / Lab to Lives / Scaling Science Without Losing The Plot w/ Jennifer Man

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Scaling Science Without Losing The Plot w/ Jennifer Man · Lab to Lives

• 0:09
  SPEAKER_01: Welcome to Lab to Lives, the podcast where we ask, how do we get great ideas out of the lab and into people's lives?
• 0:20
  SPEAKER_01: Your hosts are Alexander Buto, Chiara Kianizer, and Ivana Osten del Ucido.
• 0:28
  SPEAKER_01: We are improv comedians who also work in lifestyles.
• 0:33
  SPEAKER_01: On this show, we invite guests from the industry to give us their bold take on how we could get more health with less waste.
• 0:44
  SPEAKER_01: So whether you're developing a next generation drug, innovating in MedTech, or just curious about how healthcare moves from discovery to impact, welcome to Lab2Lives.
• 1:00
  SPEAKER_03: Hello everybody, and welcome to another episode of Lab2Lives.
• 1:05
  SPEAKER_03: It's certainly our favourite podcast.
• 1:07
  SPEAKER_03: We hope it's your favorite podcast too.
• 1:09
  SPEAKER_03: Today we have online Ivana, as usual.
• 1:13
  SPEAKER_03: Hello.
• 1:14
  SPEAKER_03: And also our guest for today, who we're very excited about.
• 1:18
  SPEAKER_03: It's Jennifer Mann from the UK Biocentre.
• 1:21
  SPEAKER_03: Jennifer, how are you?
• 1:22
  SPEAKER_00: Good, thank you.
• 1:23
  SPEAKER_00: Thank you so much for inviting me on the podcast today.
• 1:27
  SPEAKER_03: No, thank you very much for being here.
• 1:29
  SPEAKER_03: It's an absolute pleasure to have you on board.
• 1:32
  SPEAKER_03: For our listeners who haven't had the benefit of all our previous conversation, why don't you start by just giving an introduction to who you are and uh how you fit into this life science journey that we're all on?
• 1:45
  SPEAKER_00: Yeah, well, hello everyone listening.
• 1:48
  SPEAKER_00: My name's Jennifer Mann.
• 1:49
  SPEAKER_00: I'm the scientific director at UK BioCenter based in Milton Keynes in the UK.
• 1:56
  SPEAKER_00: I've been working in life sciences now for over 20 years, starting right from the bench and now managing teams.
• 2:05
  SPEAKER_00: And I would say it's been a little bit of a convoluted route to my journey here, but it's all worthwhile.
• 2:15
  SPEAKER_00: It was based on skill set and acquiring those new experiences that have really led me to where I am now today.
• 2:26
  SPEAKER_03: Amazing.
• 2:27
  SPEAKER_03: No, great.
• 2:27
  SPEAKER_03: And I definitely want to dig into this convoluted journey.
• 2:31
  SPEAKER_03: Actually, that's already a phrase that that uh picks my ears up here.
• 2:36
  SPEAKER_03: But first things first, what we do on this podcast is to place ourselves also into one or more of five pillars, one of which is the research and development pillar, another is manufacturing/slash production, the third is compliance, the fourth is commercialization, and the fifth is corporate.
• 2:58
  SPEAKER_03: Jennifer, of those pillars, which ones fit to you or appeal to you in any way?
• 3:04
  SPEAKER_03: Can I choose two?
• 3:06
  SPEAKER_03: You absolutely can.
• 3:06
  SPEAKER_03: We we all have multiple here.
• 3:08
  SPEAKER_00: Yeah.
• 3:09
  SPEAKER_00: So UK biosensor really straddles between the research and development and also the compliance piece.
• 3:17
  SPEAKER_00: But there's also an air of innovation here as well.
• 3:22
  SPEAKER_00: So, you know, we straddle quite a few lanes, so to speak.
• 3:27
  SPEAKER_01: That is interesting, and also it leads us back a little bit to the convoluted ways in which we make our ways into life sciences, but also how life sciences uh intertwine.
• 3:39
  SPEAKER_01: Yes.
• 3:40
  SPEAKER_00: It's it's never a linear journey, right?
• 3:43
  SPEAKER_00: And and I think a lot of scientists will all also say that their path to where they are now has been somewhat of a convoluted route.
• 3:55
  SPEAKER_00: And here at UK BioCenter, our services seemingly are quite simple, but they serve a lot of purposes to a lot of institutions, and so that's why I say, okay, we straddle quite a few swim lanes.
• 4:13
  SPEAKER_00: We are compliant to many regulations, licenses, and accreditations because actually our partners require it, but we also have to be innovative at the same time at bringing in best practices, doing more with less, serving larger quantities of samples.
• 4:36
  SPEAKER_00: And so we have to stay ahead of the times as well.
• 4:39
  SPEAKER_03: Yes, yes, all of which very very true for all of us.
• 4:42
  SPEAKER_03: And I like the swim lanes metaphor.
• 4:45
  SPEAKER_03: You're getting ahead of us actually.
• 4:47
  SPEAKER_03: We're big on metaphors here.
• 4:49
  SPEAKER_03: So I'm gonna I'm gonna dig into that one a little bit later.
• 4:53
  SPEAKER_03: But to start with and to get the conversation going, usually we also ask, are there any big thoughts on your mind right now in the industry?
• 5:03
  SPEAKER_03: Any big questions in terms of how to bridge this lab to lives journey, either Jennifer or Ivana, to to kick things off.
• 5:12
  SPEAKER_03: What have we been thinking about this week?
• 5:15
  SPEAKER_01: If I may go first, Jennifer, because I I do have a big question that I have been pondering in the in the past week.
• 5:22
  SPEAKER_01: So last last week I was out to dinner with a former collaboration partner, and he was telling me that his next big uh assignment was to do a uh a transformation in a large bank where they would want to reorganize themselves according to AI.
• 5:44
  SPEAKER_01: And to me, like I was I was taken aback because why would AI have anything to do with how we organize our organizations?
• 5:55
  SPEAKER_01: What is the link there?
• 5:56
  SPEAKER_01: But as he was talking me through it, suddenly I started to wonder, like, well, how how do we organize ourselves in pharma in the first place?
• 6:08
  SPEAKER_01: And how would AI impact that?
• 6:10
  SPEAKER_01: I do not have any solutions to this at this point in time.
• 6:13
  SPEAKER_01: It is kind of a like a research rabbit hole that I have gone down figuring out like, what is is this a phenomenon that I should be considering and taking into account?
• 6:22
  SPEAKER_01: But that has been on my mind in the past week.
• 6:26
  SPEAKER_00: That's really interesting, the adoption of AI, and it's something that we that are heavily influenced by technologies and high throughput equipment is how to build it in without compromising data protection, the integrity of the samples themselves, etc.
• 6:50
  SPEAKER_00: etc.
• 6:51
  SPEAKER_00: But it's also a development opportunity as well for some of the team here, which is great.
• 6:58
  SPEAKER_00: But yeah, it is a conundrum.
• 7:01
  SPEAKER_00: And I think my my challenges at the moment, and I was only saying this last week to my team, is it's funny how little science I do nowadays, it's more operational firefighting.
• 7:18
  SPEAKER_00: Um and getting prepared, making sure that we are ready to deliver the best service that we can, whilst ensuring that there's standardization throughout the data is protected and patient samples are protected.
• 7:39
  SPEAKER_00: And so for me, I've been thinking a lot about the operational readiness part.
• 7:46
  SPEAKER_00: How do we ensure our customers are happy?
• 7:51
  SPEAKER_00: You know, they at the end of the day, they have to organize multiple, I guess, tasks for their research.
• 8:00
  SPEAKER_00: They have to acquire the samples, they have to get the samples on site.
• 8:06
  SPEAKER_00: We seemingly seemingly only have that middle chunk, which is to process and to store them.
• 8:11
  SPEAKER_00: But actually, that comes with training, being prepared in in terms of building limbs, you know, making sure that data can be uploaded, ingested, and then delivered back, and that everyone knows what they're doing.
• 8:28
  SPEAKER_00: The equipment is ready, people is people are ready.
• 8:32
  SPEAKER_00: And that for me was it was just like, oh wow, okay, I'm I'm taking a step back now.
• 8:39
  SPEAKER_00: It's it's it's less about the science, it's more making sure it's smooth running from here on in.
• 8:46
  SPEAKER_00: So yeah, that's that's been playing on my mind for the for the past couple of weeks is do I miss being on the bench?
• 8:57
  SPEAKER_00: Am I okay?
• 8:58
  SPEAKER_00: Am I okay at this state of stage of the journey where I'm just you know putting all the pieces together?
• 9:05
  SPEAKER_00: You know, before it was really just how to plan an experiment and and making sure I have the the right controls in place.
• 9:12
  SPEAKER_00: And it's now kind of more so are the people ready?
• 9:17
  SPEAKER_00: You know, is the equipment ready?
• 9:19
  SPEAKER_00: Everything communicated out?
• 9:23
  SPEAKER_00: Yeah.
• 9:24
  SPEAKER_00: So that that's been my my challenges for the past couple of weeks.
• 9:28
  SPEAKER_01: It's kind of like a different like zoom level.
• 9:31
  SPEAKER_01: Like when you're at the bench, you zoom into the process that is about the specific experiment that you're doing.
• 9:38
  SPEAKER_01: And like your experiment is now the whole organization.
• 9:42
  SPEAKER_00: No, you're com you're sorry, you're you're completely right.
• 9:46
  SPEAKER_00: And and it's less it's less now about that uh that finite piece of an RD project, and it's more that translational part, you know, making sure we have those workflows to ensure that samples are derived from the clinics and patients, that it is scalable and that data is usable as well.
• 10:11
  SPEAKER_00: And and this is a really nice topic, I think, to delve into.
• 10:15
  SPEAKER_00: One of I think one of the issues that we are having from the lab to actually uh to the clinic or to to medicines is about that scalability and and making sure we have the data that is required to enable that acceleration forward into medicines.
• 10:42
  SPEAKER_03: Yeah, and it's an interesting transition, this actually, from doing things to ensuring that things are done.
• 10:51
  SPEAKER_03: And and this is where I think some of these different points tie up, which is quite nice, because part of that to Ivana's point is the organizational structure.
• 11:03
  SPEAKER_03: The the way that we build our organizations and and structure them directly impacts what what they're able to do, right?
• 11:11
  SPEAKER_03: And the default tends to be, has always tended to be for a long time, these top-down hierarchical pyramids.
• 11:18
  SPEAKER_03: But I think one of the transitions that we're seeing right now is that uh that's not necessarily the optimal structure for for getting things done in the complex shifting world of of healthcare.
• 11:30
  SPEAKER_03: And then similarly for people's roles, uh there's for a long time we had a very deep specialist orientation.
• 11:40
  SPEAKER_03: But it it feels like over the past 20 years in particular, the the importance of breadth has changed versus depth, and and that's also an uh another improvement, I think, that we're making in the industry, but another change to sort of adopt and and get on top of.
• 11:56
  SPEAKER_01: I I want to circle back, Jennifer, to like to be able to actually oversee like the highest zoom level that you're at now.
• 12:05
  SPEAKER_01: What was your convoluted journey into that space?
• 12:09
  SPEAKER_01: How did you get the experience from all the different components to be able to see how they fit together?
• 12:15
  SPEAKER_00: I think it's just experience.
• 12:17
  SPEAKER_00: And quite honestly, I fell into many of the roles that I have had.
• 12:26
  SPEAKER_00: I was there at the right time, right place, right time.
• 12:30
  SPEAKER_00: And a lot of the learnings are because I've been there and I've done that.
• 12:38
  SPEAKER_00: I've made the mistakes and I've learned from them.
• 12:41
  SPEAKER_00: And to be able to troubleshoot in the position I'm in now, it's about having that experience of seeing the bigger picture.
• 12:50
  SPEAKER_00: It's seeing not only a week, a month, a year down the road, it's understanding what the risks are as well if if we don't comply, if we don't change things up and use innovation, etc.
• 13:06
  SPEAKER_00: etc.
• 13:07
  SPEAKER_00: And I think the best part of it is being able to communicate with either our existing customers or potentially new customers, is have you thought about X, Y, and Z?
• 13:20
  SPEAKER_00: It's not just I've seen it here, perhaps it's being on the other side and being on the bench and and you know, understanding, oh, perhaps something so small like manually pipetting something, there's risks involved, you could use perhaps automation.
• 13:42
  SPEAKER_00: You know, little things like that, I think, is is quite meaningful and impactful, especially to quite a young team that we have here.
• 13:54
  SPEAKER_00: And a lot of the team here are maybe first job out of university, perhaps a second or third.
• 14:04
  SPEAKER_00: It's being able to relate to some of the work that they do day in, day out as well.
• 14:14
  SPEAKER_00: But turning it around and my own career path, I came out of university not quite knowing what I wanted to do.
• 14:23
  SPEAKER_00: I knew I loved science, I knew I loved research, I didn't want to go into the pharma world, and I just wanted to play science on the bench.
• 14:33
  SPEAKER_00: That's all I wanted to do, but I didn't know what field either.
• 14:37
  SPEAKER_00: So those kind of I guess the first 10 years coming out of university, I tried a little bit of everything.
• 14:45
  SPEAKER_00: I went for fixed-term contracts for a year, and I would move from team to team, university to university.
• 14:55
  SPEAKER_00: And I remember in one job interview I had, and they said, is that correct that you've done all these things?
• 15:02
  SPEAKER_00: And I said, Yeah, yeah.
• 15:04
  SPEAKER_00: Yeah.
• 15:05
  SPEAKER_00: Some people might see a CV that that you know has many roles, uh, maybe only you know a year at a time, as something negative.
• 15:16
  SPEAKER_00: But honestly, I think I wouldn't be here in my role today if I hadn't have done that.
• 15:24
  SPEAKER_00: So I'm very thankful that I was quite adventurous at that age.
• 15:29
  SPEAKER_00: But now, you know, I'd rather stay a little while longer, but um, just picking up those skills anywhere and everywhere, and being able to apply it in different industries.
• 15:41
  SPEAKER_00: So I I completed a master's in forensic Egyptology.
• 15:48
  SPEAKER_00: Whoa, what does you know, what does that have to do with what I do now?
• 15:54
  SPEAKER_00: Well, it's it's planning ahead and and you know, all those bench skills and how fragile ancient samples are when you've got samples that are potentially 3,000 years old and you're, you know, it's it's a one-off experiment.
• 16:13
  SPEAKER_00: You can't repeat it.
• 16:14
  SPEAKER_00: You know, that there's a there's a sensitivity there that I really learned.
• 16:20
  SPEAKER_00: And then going into the public sector and understanding about regulatory compliance and cell therapies, and then bringing it back to startup companies and having to wear multiple hats, they these are all learnings I can I can take and really I guess disseminate here in my current role.
• 16:44
  SPEAKER_03: It's interesting because uh there's the point in there, of course, about picking up individual skills and and and being able to apply them.
• 16:53
  SPEAKER_03: Do you also feel that uh there is an inherent advantage for for what you're doing now and where you ended up in having this unconventional path in terms of you've seen things that maybe don't quite exactly fit, but you're gonna translate into new situations or understand situations better.
• 17:14
  SPEAKER_03: Is this do you feel there's this kind of higher order advantage to to that journey you've had?
• 17:19
  SPEAKER_00: I think so.
• 17:20
  SPEAKER_00: It's it's applying really the same themes to different scenarios, and I guess a lot of my day-to-day work is being able to resolve every everyday problems, and it may not be technical or it may not be scientific, it may be resource, it may be capacity planning, it may be development opportunities, but I think maybe about 80% of the time, I'll in my head go back to a time where perhaps I experienced those things or it's impacted me in some way, regardless, I think, of the the institution, the environment, and being able to pluck those experiences from every corner of my career and and kind of amalgamate it into what I do day-to-day here.
• 18:26
  SPEAKER_00: So yes.
• 18:29
  SPEAKER_01: I have a question for you, Alex.
• 18:32
  SPEAKER_01: Have you ever been at a workbench doing lab science?
• 18:38
  SPEAKER_03: I mean, yes, short answer, yes.
• 18:40
  SPEAKER_03: Long answer, I mean, I did a lot of I guess what's the opposite of wet lab dry lab stuff for a long, long time.
• 18:48
  SPEAKER_03: I mean, I was in what I mean, my PhD, I was in a lab on my own building a system and testing it.
• 18:55
  SPEAKER_03: In my first startup, I did also get roped into doing a very tiny bit of wet lab stuff.
• 19:01
  SPEAKER_03: That was mostly on the basis of no one else had any time, and I was alive body in the room that had hands and and could do some bits and pieces.
• 19:10
  SPEAKER_03: But that's that's it.
• 19:11
  SPEAKER_03: That's the that's the extent of my wet lab experience.
• 19:14
  SPEAKER_03: It's not very much.
• 19:16
  SPEAKER_03: But it was very useful.
• 19:18
  SPEAKER_03: I I miss I miss building and and experimenting, yeah.
• 19:24
  SPEAKER_03: That was always very fun, right?
• 19:26
  SPEAKER_03: That that satisfaction that comes from designing something and assembling it and pulling it together, and then well, it doesn't work, but then you fiddle with it, and then it doesn't work again, and then you fiddle with it some more, and then it does work, and then you can run all of these experiments.
• 19:41
  SPEAKER_03: That was always very satisfying to me, actually, I have to say, and I do I do miss that.
• 19:47
  SPEAKER_03: But there are joys in in operating at this higher level of abstraction, right?
• 19:53
  SPEAKER_03: In in terms of again, this enabling things to happen is also very satisfying and seeing these odd connections and for sure together.
• 20:04
  SPEAKER_00: And I think that's that's the greatest joy is being able to see success through the eyes of others and um and for the team to actually listen, making their own mistakes, but actually listen to some of the some of the hurdles and far, whatever you want to call it, you know, advice giving, taking it on board, whether they take it on board or not, but still learning from that.
• 20:35
  SPEAKER_00: And I think that's that's the greatest joy is is being that enabler, being able to make them see the bigger picture, whether it's career, whether it's a certain experiment.
• 20:49
  SPEAKER_00: I love it.
• 20:50
  SPEAKER_00: It is actually so rewarding.
• 20:53
  SPEAKER_03: Yeah, and and needed, I think.
• 20:56
  SPEAKER_03: One of the challenges in working in in an industry like this is there's always this tension between what what's what's been done in the past and and and the new ways of doing things.
• 21:09
  SPEAKER_03: It's very obvious right now that we're in a phase of of this industry where there's a lot of new stuff that needs to be developed.
• 21:15
  SPEAKER_03: You know, 20 years ago we were mostly small molecules, peptides, that kind of thing.
• 21:20
  SPEAKER_03: And now as you've mentioned, you know, it's cell and gene therapies, it's it's biologics, it's all of these new ways of creating therapies and and benefit for patients that have these wonderful new advantages but also come with challenges, and those challenges need new ways of thinking and and translation, right?
• 21:40
  SPEAKER_03: Maybe Egyptian mummies or or ancient Egyptology can tell us new things that we need to be doing in cell and gene therapy development, for instance.
• 21:51
  SPEAKER_00: Yeah, for sure.
• 21:53
  SPEAKER_00: And I think there's also an aspect of a level playing field or or even a cat.
• 22:01
  SPEAKER_00: With so much innovation within science that the technology also has to catch up, and of course, technology comes at a price as well.
• 22:13
  SPEAKER_00: And so I think both fields really need to start collaborating more.
• 22:23
  SPEAKER_00: If you've got the idea, what's the solution?
• 22:25
  SPEAKER_00: You know, some of the work that I carried out in cell and gene therapy, fantastic ideas worked biologically, but how do you scale that up?
• 22:34
  SPEAKER_00: And that's where you have those those challenges.
• 22:40
  SPEAKER_00: How do you make the idea come from the lab to a scalable operation that can go into patients at a large scale?
• 22:52
  SPEAKER_03: Yeah, absolutely.
• 22:53
  SPEAKER_03: Well, and this is the big challenge in selling gene therapies in particular.
• 22:57
  SPEAKER_03: Right now, we've seen I'll get the organizations right.
• 23:01
  SPEAKER_03: I think Teva were all pulled back quite recently and a couple of others, because not because the they can't produce the therapies in that that work and are clinically relevant, but the manufacturing side is is so difficult right now.
• 23:13
  SPEAKER_00: It is very difficult.
• 23:15
  SPEAKER_00: Very difficult.
• 23:17
  SPEAKER_03: How about you, Ivana?
• 23:18
  SPEAKER_03: So I didn't ask.
• 23:19
  SPEAKER_03: Were you ever in a wet lab?
• 23:21
  SPEAKER_03: Are you missing any wet lab experience?
• 23:23
  SPEAKER_03: Or uh what what was that like for you?
• 23:26
  SPEAKER_01: I think the closest I have to any sort of lab experience is like back in high school.
• 23:32
  SPEAKER_01: Because I I am an economist by by degree.
• 23:36
  SPEAKER_01: So we we I our lab was like I'm a behavioral economist.
• 23:41
  SPEAKER_01: So our labs was like the pop the big populations that we can interact with either through surveys or through observations or collecting data on the internet of what people are searching for.
• 23:55
  SPEAKER_01: It's a different different kind of lab.
• 23:58
  SPEAKER_01: So I I actually I'm pretty ignorant about what occurs in the wet lab.
• 24:02
  SPEAKER_01: So really curious whenever we uh we have these conversations.
• 24:06
  SPEAKER_01: That being said, like right now, as part of my my day job, I am responsible for at least the technology side of one of our lab sites.
• 24:17
  SPEAKER_01: It's always so cool to me to see what actually happens, because a lot of the other areas in life sciences are so abstract, especially when we collaborate with partners and don't do our own manufacturing.
• 24:28
  SPEAKER_01: But in the lab, it's very tangible.
• 24:30
  SPEAKER_01: It's like, well, it's this sample right here, and it's it's pretty binary.
• 24:36
  SPEAKER_01: Like either this sample is working for us and we can learn something from it, or we discard it, or like it's it's so much more specific, and and that's great.
• 24:49
  SPEAKER_00: Yeah, the scale of the operation here, and I remember stepping foot in this building now almost two years ago for for my interview here and looking at the scale of the operation.
• 25:03
  SPEAKER_00: I'm as a cell biologist, I'm very used to scale up being from a six-well plate to maybe a 24 well plate.
• 25:13
  SPEAKER_00: You know, these are just very small stepwise changes.
• 25:19
  SPEAKER_00: Whereas here we have tens of tech ans being able to push through thousands of samples every single, and I mean thousands, in in our at the peak of what of our processing, we were able to process 60,000 samples a day.
• 25:41
  SPEAKER_00: And that I know, and and and saying that, we've got automated minus 80 biostores that are holding currently 12 million, 12 million, and I can't describe to you what 12 million looks like.
• 26:01
  SPEAKER_00: It's on it's at a different scale, what we do, but it's that churn, it's it's being able to provide the data piece for large research projects, and we house the largest research projects in the UK to get them the data that they need to be able to progress, and that's what it takes.
• 26:29
  SPEAKER_00: It takes 12 million plus samples being held at minus 80.
• 26:33
  SPEAKER_00: It's it's being able to extract the useful, oh, the the useful fractions from a single blood tube so that we don't have to recall people back to the clinic.
• 26:50
  SPEAKER_00: Every drop counts.
• 26:53
  SPEAKER_00: But it's it's at a scale that I I still have to pinch myself to this day.
• 26:58
  SPEAKER_01: I am a little curious because in in all the biopharma companies I have been in so far, sample management and sample tracking has been a big headache.
• 27:10
  SPEAKER_01: Like to the point where we sometimes have had to send people out to kind of manually count samples at different sites to figure out what do we have and where is it.
• 27:22
  SPEAKER_01: Yeah.
• 27:23
  SPEAKER_01: At your scale, like that would simply be impossible.
• 27:27
  SPEAKER_01: Like you you must have better ways of organizing and understanding where where is our stuff and what is in the samples.
• 27:36
  SPEAKER_01: Can you share a little bit about that?
• 27:39
  SPEAKER_00: We're very reliant on our LIMS system.
• 27:43
  SPEAKER_00: So that's a laboratory information management system.
• 27:46
  SPEAKER_00: Our workflows are designed so that data, whether it's a timestamp of the sample coming in and being scanned and uploaded to the point where our extraction machines are providing the data to be uploaded onto the limbs all the way through how a sample is picked from a minus 80.
• 28:13
  SPEAKER_00: Those are all recorded and those are all uploaded and ingested into our limbs so that any of our customers can get that data in a timely fashion.
• 28:26
  SPEAKER_00: So it's it's very much standardized and automated what we do.
• 28:30
  SPEAKER_00: And that's what we need when it's when it's over you know a thousand samples every morning and then some.
• 28:38
  SPEAKER_00: And that's just for one project that we'll have to have capacity for.
• 28:44
  SPEAKER_01: Well, I my curiosity here is it sounds like it's pretty automated and structured.
• 28:51
  SPEAKER_01: How automated can it get?
• 28:54
  SPEAKER_01: How how much of the human touch in the lab could we actually lead to leave to machines?
• 29:04
  SPEAKER_00: The good news is that the machines won't take over our scientists or operators on site because you will need that verification process.
• 29:14
  SPEAKER_00: You need aspects of quality control.
• 29:17
  SPEAKER_00: You'll need to have that deviation workflow in place when a sample is not the perfect sample.
• 29:24
  SPEAKER_00: You know, so we have samples coming from all over the country, coming from different clinics, coming from different patients, and every patient will have a, I guess, a different biological makeup.
• 29:40
  SPEAKER_00: So not every sample is going to run smoothly through our workflow.
• 29:46
  SPEAKER_00: So it'll take an experienced operator, uh a lab scientist, to be able to manually extrapolate the useful material out of them.
• 29:59
  SPEAKER_00: So for us, our lab scientists, our operators are very much needed.
• 30:07
  SPEAKER_00: In an ideal world, everything will be automated and it'll be seamless end-to-end.
• 30:14
  SPEAKER_00: But it's not quite there yet.
• 30:17
  SPEAKER_03: Well, I mean, completing the loop, this is where the uh maybe the uh the uh ancient Egyptology can help, right?
• 30:25
  SPEAKER_03: I mean, because you were saying that part of the point of that is that you have one sample and one sample to get that right.
• 30:32
  SPEAKER_03: And so maybe the learnings from how to do that then need to go into these automated systems in terms of in terms of getting the most from a sample.
• 30:41
  SPEAKER_03: I mean, it's it it's this conversation sparking my interest a little bit as well in terms of bringing it back to this core question of the podcast, which is how we get better from from out of the lab into into patients' lives in terms of treatments and things.
• 30:57
  SPEAKER_03: And it it strikes me actually that that one of the major challenges we're trying to get our arms around in the interest industry right now is this one of scale.
• 31:07
  SPEAKER_03: Scale is the new frontier in a way, but it's how to firstly generate scale in terms of the samples, which clearly seems to be something you're on top of, and then also how to manage that scale in terms of interpretation and in terms of the data analysis and things.
• 31:28
  SPEAKER_03: Right.
• 31:29
  SPEAKER_03: And so are there learnings that you're creating there that are of value even you know to the full pharma development cycle that you see?
• 31:40
  SPEAKER_00: I think so.
• 31:41
  SPEAKER_00: There's a foresight that I wish I guess every company, clinical trial, et cetera, et cetera, would I guess would take on, which is what really happens to their samples from the moment that it's manufactured, perhaps, or drawn from a volunteer all the way to storage and distribution?
• 32:16
  SPEAKER_00: Does it allow for continuous research?
• 32:21
  SPEAKER_00: What are you trying to get from that data?
• 32:24
  SPEAKER_00: Uh is what you're doing in the interim going to ruin your chances for, I don't know, let's say downstream genotyping.
• 32:33
  SPEAKER_00: You know, how how can you cover all avenues right at the beginning so you don't have to chop and change even down to that simple sample integrity part of that part of it how what is the impact if you are going to change something in your work stream?
• 32:59
  SPEAKER_00: And and I jump on so many calls and question is this really truly what you want in terms of risk?
• 33:14
  SPEAKER_00: Are you looking to do X, Y, and Z maybe five years down the line?
• 33:19
  SPEAKER_00: So I guess foresight is is something every person wishes that they could have, but I think there there is value of really planning for scalability and downstream application.
• 33:41
  SPEAKER_01: I'm seeing this image in front of me based on what you're describing.
• 33:46
  SPEAKER_01: In in behavioral economics, our subject of research is the population.
• 33:54
  SPEAKER_01: We're looking at people and how they behave and try to analyze their interactions and figure out well, what will this mean for well, for example, the financial development of an economy because people are essentially finances.
• 34:09
  SPEAKER_01: So for samples, that is essentially the population.
• 34:15
  SPEAKER_01: And we're trying to investigate that same population for different things to investigate, well, how what does this say about whatever we're trying to investigate uh in that uh breadth?
• 34:30
  SPEAKER_01: So it it is kind of similar in that way.
• 34:33
  SPEAKER_01: And you also said before, Jennifer, every drop uh counts.
• 34:37
  SPEAKER_01: Is there any limit to like how much research can be done on any given sample?
• 34:44
  SPEAKER_00: Yeah, it also depends on the equipment itself as well.
• 34:48
  SPEAKER_00: There's always going to be a minimum threshold, especially when you are trying to process at such large scale.
• 34:59
  SPEAKER_00: But one blood sample can lead to so many different things.
• 35:05
  SPEAKER_00: And my advice is if you're going to be working with a third-party service, is do a pilot study.
• 35:15
  SPEAKER_00: Let's see where one vial of blood can take you.
• 35:22
  SPEAKER_00: You know, how much can you get from their existing workflow?
• 35:27
  SPEAKER_00: And it's such a good opportunity to then look at sample quality and also volumes.
• 35:36
  SPEAKER_00: What can you do with these volumes?
• 35:38
  SPEAKER_00: So that's my advice to to anyone out there who's designing a large research project is to organize some pilots first and foremost.
• 35:52
  SPEAKER_03: Yeah, it reminds me a lot of one of my favourite analogies, and I can't lay claim to this one actually, so it's not new, but it's it's the the the meerkat versus the the rabbit uh thing, which is you know uh well and and it's also another term for sort of it's also another term for specialists versus generalists.
• 36:11
  SPEAKER_03: You know, the specialist is is more like the rabbit, it's very easy to go very deep down that hole and dig and dig and dig and dig and get somewhere sort of very specific.
• 36:20
  SPEAKER_03: Whereas the mere cat, I mean of course they also dig holes, but they they stand up everyone once in a while and look around and and see what else is out there and and and see what else needs to needs to happen.
• 36:32
  SPEAKER_03: And this seems to be a little bit uh what you're suggesting here, and strikes me as useful in a world where everything's getting more complicated, everything's getting more interconnected, all of these different pieces matter much more because you pull on one thread here and it affects something down here.
• 36:50
  SPEAKER_03: That that yeah, rabbit hauling is always useful, but it needs to be much more combined with this broad level look around and asking this question first of okay, what else do we need to know?
• 37:02
  SPEAKER_03: What else do we need to understand and how can we best understand that?
• 37:06
  SPEAKER_00: And strangely enough, I think I'm naturally a rabbit hole type person.
• 37:14
  SPEAKER_00: You know, I I re I I dig and dig because you know, I I guess coming from from the bench side, you just want to know why, why, you know, and and it's only I guess this kind of a past decade or so is being able to take a breath and pop out, pop up from you know, between the weeds and and really say, okay, what's the impact here?
• 37:42
  SPEAKER_00: You know, what really are the risks?
• 37:45
  SPEAKER_00: Uh are we overdoing it?
• 37:47
  SPEAKER_00: Are we overcomplicating things?
• 37:50
  SPEAKER_00: But it's yeah, it it's something I don't think happens or occurs naturally to me.
• 37:57
  SPEAKER_00: I have to make myself just go, deep breath.
• 38:02
  SPEAKER_00: Let's pop up for a second.
• 38:04
  SPEAKER_00: What what's the, you know, I guess what's the wider impact?
• 38:13
  SPEAKER_03: Well, maybe that's the benefit of a of a convoluted journey as well, because maybe you spend a year digging one hole and then you spend a year digging a different hole and you spend a year digging a different hole, but then you've dug holes over a very wide area.
• 38:27
  SPEAKER_03: So then it's easier to stand up and have a look around and pull these pieces together.
• 38:33
  SPEAKER_03: What about you, Ivano?
• 38:34
  SPEAKER_03: Are you a are you a mere cat or are you a rabbit naturally?
• 38:37
  SPEAKER_01: I generally I do sports the mere cat hat.
• 38:42
  SPEAKER_01: I I do try like looking around and trying to understand.
• 38:45
  SPEAKER_01: Essentially, I need to understand the big picture before I go deep.
• 38:49
  SPEAKER_01: But then there are some certain areas where I just can't help myself and I need to understand every single little detail, and I will not come out until yeah, I I I have understood.
• 39:00
  SPEAKER_01: One of those things is essentially like behavior in large groups.
• 39:06
  SPEAKER_01: I I can never get enough of understanding why do people like an individual can be reasonable and smart and then in in a large group suddenly people behave in a different way than they would individually.
• 39:17
  SPEAKER_01: That's that's just mind-boggling to me how that happens.
• 39:20
  SPEAKER_01: So whenever every piece of research I found, find about that, I just I just have to understand and have it and observe in large groups at work or in other social settings, like how how what are the dynamics between people here?
• 39:35
  SPEAKER_01: Super fascinating.
• 39:37
  SPEAKER_01: Yeah.
• 39:38
  SPEAKER_03: No, and going back to the beginning, this is what I like about seeing organizations as themselves being living living structures, right?
• 39:50
  SPEAKER_03: Because it's it's the culmination of all these individual behaviors.
• 39:53
  SPEAKER_03: And that gets into the structural analysis of well, if we want this organizational level organism to behave in a certain way, how do we how do we best optimise that?
• 40:04
  SPEAKER_01: Yeah, and which sample should we uh take from it to understand what is currently happening in it?
• 40:10
  SPEAKER_03: Yeah, I've been reading uh a lot of uh Stafford Beer recently, if you if you know him.
• 40:15
  SPEAKER_03: He's he's a very old I'm sure you want me saying that because he's dead now, but very old uh cyberneticist from from I don't know, I guess the 40s or 50s.
• 40:26
  SPEAKER_03: But he had this big theory about structuring organizations to to learn and adapt and and to work towards it towards a clear outcome.
• 40:36
  SPEAKER_03: Which is why this is very much on my mind right now in in in terms of this world where maybe the way that we've run pharma for a long time is is not optimal for the world that we find ourselves in, and then working out what this new way forward is and and how we build out these pieces at all the different levels to to get what's needed for patients.
• 41:00
  SPEAKER_03: It's a it's a good question.
• 41:03
  SPEAKER_03: Yeah.
• 41:04
  SPEAKER_03: But I think it's this depth and breadth, right?
• 41:06
  SPEAKER_03: I mean, you're already telling us a lot, Jennifer, how your breadth of experience is helping you do just that for your for your clients in terms of creating this, or your customers creating this scale of information and managing that scale of information and getting as much as possible out of that single drop of sample, which wasn't the process before, sorry.
• 41:28
  SPEAKER_00: No, I think also for my role, there's an air of just honesty and transparency.
• 41:36
  SPEAKER_00: I think internally and externally, people would see right through me if I didn't believe in what of the or experience the things that I I say and do, and that keeps keeps me honest as well.
• 41:52
  SPEAKER_00: But there's also a leadership element as well to this about living and breathing the values or and your expertise, and as I say, yeah, my team would see through that in a heartbeat because yeah, everyone's so smart here, and and I always say I didn't hit get here because I'm smart, I got here just because I grafted in the lab.
• 42:21
  SPEAKER_00: Yeah.
• 42:23
  SPEAKER_03: I mean, that is another interesting thing for me, which is about how we create that transition as well.
• 42:29
  SPEAKER_03: Uh, because you have that expertise and you have that transparency and and that ability to stand up in front of people and and say, This is what you need to do, we understand the space.
• 42:39
  SPEAKER_03: I think it's a common challenge when you're in this transition that you you come you you do that or you make that statement in front of the old way of doing things, right?
• 42:49
  SPEAKER_03: But the old way of doing things doesn't necessarily automatically pick that up.
• 42:55
  SPEAKER_03: So I mean a two-part question.
• 42:56
  SPEAKER_03: Is is that an experience that that you've had?
• 42:59
  SPEAKER_03: And if so, how are you dealing with that?
• 43:02
  SPEAKER_03: How are you trying to other techniques that you have to to pull this old world into the into the new a little?
• 43:09
  SPEAKER_00: That it's really interesting you comment because it was again in conversation where I reminisced at an old job of mine, and it was the first time I really heard, right, I know I know you've got X amount of years experience, but I need you to leave your ego at the door and we start afresh, and it stays with me today, and is something that I say to my teams because it's so important to leave all that baggage behind.
• 43:44
  SPEAKER_00: You may have learned a technique somewhere else, and it's in your head that you think it's the best thing to do for a particular process, but to be able to provide innovation and to provide change.
• 44:00
  SPEAKER_00: Or optimizations to a process, you need to consider everything.
• 44:05
  SPEAKER_00: You know, what is it that makes your idea or your process better than my old process?
• 44:12
  SPEAKER_00: Is there some hybrid that is actually the best solution?
• 44:18
  SPEAKER_00: And so for me, how I apply it in this role is I really fought for an optimization team.
• 44:26
  SPEAKER_00: So this is a process and development team.
• 44:30
  SPEAKER_00: Because previously we were trying to run BAU operations and that innovation process optimization all at once.
• 44:40
  SPEAKER_00: And there was never any value given to either kind of workflows.
• 44:47
  SPEAKER_00: You never had time to improve because actually BAU would always take priority.
• 44:56
  SPEAKER_00: So we could never optimize, and vice versa.
• 44:59
  SPEAKER_00: And though that communication was also broken between the two tasks.
• 45:06
  SPEAKER_00: So kind of separating that out and separating it out with a team that had previously worked on those BAU activities was even more important.
• 45:19
  SPEAKER_00: People that knew and understood what the workflow and and coming into this workplace, I you know, I had no ambition to change anything.
• 45:35
  SPEAKER_00: The first at least six months was understanding actually what the customers were saying about us.
• 45:48
  SPEAKER_00: Before I could get that idea to try and tweak some of our processes with a different team.
• 45:55
  SPEAKER_00: So yeah, but I always remember that that phrase, leave your ego at the door.
• 46:02
  SPEAKER_00: And yeah, it's uh it's very good advice to take.
• 46:06
  SPEAKER_03: Yeah, I think it's excellent.
• 46:07
  SPEAKER_03: I'm also curious, Ivana, what what your experience here is because I know you're at a similar coal face in a way of digitizing uh clinical trials, which have traditionally been very well haven't had such a big revolution in in how they're they're run for such a long time, right?
• 46:25
  SPEAKER_03: Is this also something you face?
• 46:28
  SPEAKER_01: Yes.
• 46:30
  SPEAKER_01: And I do recognize, Jennifer, like the major points in what you're saying, that it's to be able to rethink how we work, we need to meet as human beings again.
• 46:44
  SPEAKER_01: Like there is a certain pride in this is how we've always done things, and that comes with this is how I have always been successful in organizations when I have done things this way.
• 46:55
  SPEAKER_01: And to rethink things does require that we kind of put aside our expertise and dare to be vulnerable again and dare to explore and say, I actually have my pride in being someone who is curious and willing to learn rather than someone who already knows.
• 47:14
  SPEAKER_01: And that transition is easier for some than others.
• 47:18
  SPEAKER_01: And I find it helpful when I lead by example, but that is also sometimes uncomfortable because then I'm the the first one to say I don't know, and the first one to expose my own ignorance, and then hope essentially that vulnerability can be a force of leadership and not just like the expert power that comes with many years of experience.
• 47:45
  SPEAKER_00: And you've just said it so eloquently as well.
• 47:47
  SPEAKER_00: Yeah.
• 47:48
  SPEAKER_03: I mean, I really like both of these discussions because I I think this is another layer over the the uh mere cat rabbit model as well, in in terms of how to get new things done.
• 48:00
  SPEAKER_03: Because, you know, we're systemizing animals.
• 48:03
  SPEAKER_03: We like to think as little as possible, and so categorize things, and so yeah, we're digging the hole and it's okay, there's the root branch, here's the the left turn, here's the right turn, or even as the mere cat is, you know, there's the tree, there's the hill.
• 48:19
  SPEAKER_03: And and you sort of almost stop processing things, or or considering that, well, okay, yeah, the tree's the tree, but it could be, I don't know, shade, or it could be a windbreak, or it could be something else.
• 48:32
  SPEAKER_03: And so it's not just enough to either dig into the hole or look around, yeah, we need to keep this not presume that we know what everything is all of the time and and sort of be open to reconsidering them.
• 48:45
  SPEAKER_02: Yeah.
• 48:46
  SPEAKER_03: Which I really like.
• 48:47
  SPEAKER_01: And that ties me back to kind of my current observation about my my friend who was suddenly considering how to organize a bank according to AI, which my first reaction was preposterous.
• 48:58
  SPEAKER_01: Why would you do that?
• 49:00
  SPEAKER_01: But but my second thought is like, is there something that I'm what is the terrain?
• 49:06
  SPEAKER_01: Is the hill is the meaning of the hill suddenly different?
• 49:10
  SPEAKER_01: Is the is the meaning of the tree has that been redefined with new technology?
• 49:15
  SPEAKER_01: And I'm not sure whether the terrain is still the terrain.
• 49:18
  SPEAKER_01: Uh who knows?
• 49:19
  SPEAKER_01: But it's very interesting to kind of go through that consideration.
• 49:24
  SPEAKER_01: And even though maybe I will not find that AI necessarily means that we need to make a different kind of departments or teams.
• 49:34
  SPEAKER_01: But maybe that will be the spark of some considerations that will actually get me to reconsider how I view things.
• 49:42
  SPEAKER_01: Or maybe there's now you have to organize into an A and an I, and then like people go into two different teams, and that's that.
• 49:51
  SPEAKER_03: I mean, I think it's also interesting, which is uh it feels to me at least it's always gonna particularly in this area of organizational structuring, it's always gonna have to be team up with with people and AI or any other technology for the reasons that you said, Ivana, which is yeah, okay, it can open up some new avenues of thought, but it doesn't have an emotional landscape, at least yet.
• 50:17
  SPEAKER_03: And so much about how organizations work operate in this uh emotional space.
• 50:24
  SPEAKER_03: I mean, you can uh create the most rigid hierarchical, you know, everything's in every box structure that you like, but the extent to which that structure works is always going to depend on how people feel uh in that structure.
• 50:39
  SPEAKER_03: So uh I I like to think it's a little bit of both, but maybe I'll be proven wrong someday.
• 50:44
  SPEAKER_03: I don't know.
• 50:46
  SPEAKER_01: I would like to lead us to the next part of the conversation now.
• 50:52
  SPEAKER_01: We have uh had a very interesting conversation about uh the the labs, the sample of one, this many, many samples very exciting uh for me, especially.
• 51:06
  SPEAKER_01: I want us to look into some of the dysfunction we have in our industry from a like a partnership collaboration perspective.
• 51:14
  SPEAKER_01: One of the main ways that many, especially large pharmaceutical companies conduct clinical research is through clinical research organizations.
• 51:24
  SPEAKER_01: And most of the time that either goes more or less well or it goes well, and then sometimes it goes spectacularly wrong.
• 51:33
  SPEAKER_01: And speaking of rabbit holes, this is a rabbit hole that I went down to figure out well, when it goes wrong, how wrong does it actually go?
• 51:42
  SPEAKER_01: And Jennifer, what stands out for me from our conversation is kind of that you have to understand your space well enough to be brave and say, I understand where we've been, but I don't understand where we're going.
• 51:54
  SPEAKER_01: So let's let's find out.
• 51:56
  SPEAKER_01: That really stands out for me from our conversation.
• 51:59
  SPEAKER_03: Yeah, we gotta it's it's a variation of oh, I've lost a value.
• 52:04
  SPEAKER_03: Oh, sorry.
• 52:04
  SPEAKER_03: And no, it's a very it feels a variation of that uh that thing that startups say when you're you're building the car while you're while you're driving, but we're we're building the crystal ball while we're driving.
• 52:16
  SPEAKER_01: Yeah.
• 52:17
  SPEAKER_01: Well, thank you so much.
• 52:18
  SPEAKER_01: It was such a pleasure to talk to you.
• 52:24
  SPEAKER_01: This was Lab2 Lives.
• 52:28
  SPEAKER_01: Follow us on YouTube, Spotify, Apple Podcasts, or wherever you get your content.
• 52:35
  SPEAKER_01: What is your take on how we get from the lab to making a difference in patients' lives?
• 52:40
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• 52:44
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