S2: Ep3 Transcript
Welcome to Cut the Chat – Life Science Insider.
Season 2: Episode 4 : AI in Biotech: Hype, Reality, and the Future of Drug Development
In this episode of Cut the Chat, Life Science Insider, I’m interviewing Cristina Busmales. She’s a leading expert at the intersection of AI and biotech. And together we’ll explore the transformative potential of AI and drug development, discussing its promising applications and the challenges that still need to be overcome. Cristina is going to share her insights from her experience, offering practical advice for biotech companies looking to explore
possible uses of AI in their processes and looking at future trends. This episode explores the reality versus the hype of AI and biotech, providing a wide ranging perspective of how this technology can shape the future of drug development. So let’s get to it and cut the chat.
Christina Busmales is an accomplished technical and sales executive with more than 30 years experience in a career focused mainly on the healthcare and life science industry, specifically big pharma and biotech. And most recently, Christina was the chief revenue officer at Benevolent AI, an AI augmented drug discovery company. And she’s also the non -executive board member on that AO foundation.
It’s a nonprofit organization dedicated to improving the care of patients with multiskeletal injuries and pathologies. Christine has also held executive leadership positions at Google Cloud, IBM Watson Health, and IBM. Christine is a computer scientist by education and a consultant by trade. She is an industry thought leader on disruptive technologies for the life science industry.
Christina is a computer scientist by education and a consultant by trade. She’s an industry thought leader on disruptive technologies for the life science industry. And she’s a frequent speaker at pharma and digital health -focused conferences across Europe, where I first met her. And during my chats with Christina, I’ve experienced that she explodes with innovative ideas.
And she loves to creative solutions for her stakeholders, focusing on digital, data, analytical, AI, and cloud transformations. And Christina happily lives in Zurich, Switzerland. So welcome, Christina. It’s great to have you on Cut the Chat.
Well, hello, Christina. It is so good to see you again. And I’m just thrilled that you’re with me today. We met earlier this year, I think June, it was right at the HBA conference. And I was amazed at your ability to take a topic like AI and turn it into
super engaging and I remember listening to people at my table and they were like, wow, she’s doing a really good job of making this very palatable and understandable for everyone. So kudos to you. I don’t know if I ever told you that after meeting you for the first time, but everyone was very excited about your talk. And so just really happy you’re here and have taken some time to be on Cut the Chat.
Christina Busmalis
Great, I’m happy to be here, really excited to have this conversation.
Sabine
Yeah. And off we go into this wild, wild world of AI and biotechnology. So this is like, this is your world right now, isn’t it? You’ve been, you’ve, I think you have a long history, so I’m really excited in this topic of at least data and technology. So we’re really looking forward to hearing a bit about you and where did it all start?
Christina Busmalis
Yeah, it’s interesting. I started in tech when I was quite young, so eight years old. I started to program, which, given my age away, that was back in the early 80s. My father was a math teacher and a computer science teacher at high school. And every break, he’d bring home a computer. We didn’t have any games for the computer, so I just had to play with it in different ways. And so I did some basic programming as time went on and built lines across the screen, which back then took a lot of lines of code. Now it’s done for you.
And just love technology. So I studied it through college as well and moved into technology. Not typically the typical tech path, I guess, which I’ve been a programmer because I actually didn’t want to stare at a screen all day long, which is kind of funny because nowadays we screen all day long. But I would say over my career, how did I get into the AI field? I mean, actually I did some AI programming back in the 90s when I was still in university.
Sabine
Yeah.
Sabine
Right. Yeah, just see different things, don’t we? Right.
Christina Busmalis
But that was in early stages, very early stages. I would say there’s five key career pivots. One was coming to Europe in the late 90s. I came to Zurich, which should have been for a two-year stay. But I’m still here, and that’s a long time later. And I guess the next one was really moving away from pure consulting, IT management consulting, into more of a sales role with IBM, a sales management role.
And really into the life science industry. Was what I would say my biggest pivot was really getting into life science industry and seeing that there was so much that needed to be done with technology. I spent the rest of my career in the data and analytics space. Moved away from being more on the tech side later in around 2017 to run the life science business for Watson Health in IBM, which was the first AI-based practice of IBM.
It was great, right? I was responsible for Europe and Asia, was doing a role that, you know, I was never a sales rep. So I was also in leading a whole team of sales leaders and sales reps to bring AI solutions to market. I then, you know, left IBM and joined Google. So I went from old tech to new tech. Realize there’s not much of a difference on the other side, and then I really did the jump into moving out of a pure tech company and joining a biotech company, which was Benevolent AI.
Sabine
Hehehe… Hehehe…
Christina Busmalis
Last year, last fall. And it was a great, for me it was a really interesting move, because I moved away from a tech supplier into the biotech pharma industry to be a biotech company. But it was still a company that had a tech side. They find themselves, are they biotech, are they a tech bio? I mean, just as a two-sentence intro, they do an AI drug discovery-based solutions. They go up to bringing
Sabine
Right.
Christina Busmalis
Bringing the medicines into clinical trials and then ideally out-license them to something else and move them forward. But it was, you know, I took responsibility as their chief revenue officer to, you know, dealing with out-licensing of potential pipeline assets, selling, you know, collaboration research initiatives with pharmaceutical companies. And they also had some AI-based software that they developed themselves and we bring that to market. So I really liked the move from going from a big tech company to
Sabine
A small biotech company of a couple hundred people as well.
Sabine
Yeah, big shift. And also this combination, right? Bringing the AI into clinical research. You know, AI is just so prevalent today. Everyone has become ChatGPT, I think kind of has kicked off a lot of things, I guess, for the everyday kind of person that doesn’t focus on this. But we forget that this has been going on for a long time. This is not something that just started a couple of years ago.
Christina Busmalis
That’s correct. Yeah, absolutely. I mean, 1950s, if you think about it, it’s way back when they started doing artificial intelligence and just the capability has increased and gotten better and better because of data and processing. Those are the two things that have really accelerated, especially with generative AI, is really around having so much volume of data and having the technology and the computer to really accelerate that.
Sabine
Yeah. Yeah.
Sabine
Yeah, definitely. That is so true. And then what is fascinating is this whole connection between AI and biotechnology, which is something that you’ve obviously been focusing on at Benevolent. And I’m curious, how is that and how many organizations are you seeing that are focusing on looking at drug development and incorporating and actually running their companies based on AI?
Christina Busmalis
It’s a good question because I think there’s, I would, I haven’t done this research. I’m going to make a hypothesis that someone’s going to have to prove me wrong. Every company, every, you know, throughout the entire drug development process, right? AI sits someplace everywhere. So whether it’s machine learning, whether it’s really, really strong, advanced analytics, predictive analytics, somewhere AI is being used. And so if you think about, you know, in early drug discovery,
Sabine
We’re have to fact check it.
Sabine
True. Yep.
—–
—
**Christina Busmalis**
Whether it’s identifying novel targets, again, was what we did at Benevolent, assessing those targets, validating them, there’s AI pieces everywhere. And then if you go into clinical trials, could have protocol design or patient recruitment or site selection. AI is being used. Would I say that it’s brand new? No, but it’s in pieces of existing processes today that you see AI there.
**Sabine**
Mm-hmm.
**Christina Busmalis**
Already being used in very small areas.
**Sabine**
That’s true. It’s quite siloed, isn’t it? If you look at it that way. Yeah. Yeah, yeah.
**Christina Busmalis**
It’s very siloed. I would say it’s siloed or it’s fragmented, as you could say, right? There’s definitely been a trans—I’m not gonna use a transformation because I don’t think it’s a transformation yet. There’s been an implementation of bringing AI or machine learning into various areas within the drug discovery process and drug development.
**Sabine**
Yeah, and think if you look at it, looking at going into the clinic and what you’re saying too around the clinical trials, you’re right about protocol writing support, also feasibilities, looking at where and, you know, what countries, what investigators are starting to use it to determine a lot of these points when you’re running clinical trials. And what about a step before? So if you look in the development side, where are you seeing the biggest impact being made there?
**Christina Busmalis**
I think it’s still, what you’re hearing mostly is still in that AI for drug discovery, right? And there’s a lot of companies that are out there, right? So again, Benevolent AI was infusing AI across the discovery process. So from novel target identification through the hits, the bring the molecule and be an IND ready. They’ve accelerated the process, which typically takes five years down to three years. If you would say, where is AI being used the most? I think it’s
**Sabine**
Mm-hmm.
**Christina Busmalis**
Acceleration mode at the moment. Separate from, yes, it’s being used to identify novel targets, right? That’s a second step. It’s more of accelerating at this stage, which I think is coming out. But going from five years down to around three years already cuts something into, reduces two years of that, 10 to 15-year development cycle as we see today.
**Sabine**
Mm.
**Sabine**
Great.
**Christina Busmalis**
But this is not new. So these companies like Benevolent, there’s other companies out there. There’s a lot of companies out there. You’ve seen consolidation in the market where Recursion recently acquired Extentia. So there’s a lot of acceleration happening, there’s a lot of companies doing similar but different, right? Everyone’s doing something slightly different, but coming out with the same type of area. So I think that’s where you’ve seen the most focus. Now, where I find the challenging today is we haven’t yet proven it works because
**Sabine**
Mm.
**Sabine**
Right. Yep.
**Christina Busmalis**
Is that the patient? Right? We’re in a lot of phase two trials. A lot of companies are, you know, in silico medicine, other ones have, you know, there’s dozens of trials that are in phase two, dozens of molecules in phase two, but no one’s yet in the market. No one’s yet at the side. So until we really get them there, it’s still a, we don’t know. Now, are we confident this is the way to go forward? Probably, but it’s not yet proven that it definitely is.
**Sabine**
We don’t know. Yeah.
**Sabine**
Yeah, that’s true. And do you see also because we’re in an industry that is fairly slow in a lot of areas and we’re talking about acceleration, do you or have you experienced also regulations and regulatory bodies being able to handle that acceleration and work along with the speed that you’re seeing in some of the development?
**Christina Busmalis**
I think what’s happening still today, in my opinion, is things have not changed, right? So you have to still go through all those steps with the FDA regulatory authority, whether you want to be IND ready, whether what you need for phase one completion, phase two, et cetera, et cetera. They’re not blocking it by any means. They’re not saying, you can’t use technology to help you, but you still need to do all the paperwork and all the information that is required to get there. It doesn’t just solve the…
**Sabine**
Mm-hmm. Right.
**Christina Busmalis**
It doesn’t just change the process, right? The process has changed. We’ve just infused different ways of doing things. I’ve also seen, you know, we talked about, you know, patient recruitment, you know, years ago, back at IBM in Watson Health, we had a clinical trial matching solution. It technically worked, right? It would take exclusion, inclusion, exclusion criteria, match them against the patients and identify the best patients for whatever trial you want to run. The challenge we had was not the technology. It was getting the parties together. So having three-way contracts, right, with
**Sabine**
Right.
**Sabine**
Right.
**Christina Busmalis**
Rider side with the pharma side, obviously with IBM as the technology provider, that took a long time. So if you, you know, just to do that effort, you could actually do your recruitment, right? So you got to balance the two. It wasn’t, it wasn’t that it was technology hindering. It was more that companies weren’t ready, weren’t used to it yet. Right. And that initial, this is the first time we’re doing this sort of thing, you know, 10 years ago.
**Sabine**
Right.
**Sabine**
Yep, that’s so true.
**Sabine**
Yeah.
**Sabine**
Yeah, we see that as well. Sometimes just getting a three-way CDA signed can take months.
**Christina Busmalis**
And maybe, you know, this is probably the benefit of biotech versus big pharma. Big pharma are more challenging, but can the biotechs take that and move westward?
**Sabine**
All
**Sabine**
Yeah, but it’s also, and if you look at from the vendor side too, you know, there’s some pretty significant vendors that are out there as well that also, you know, you can struggle changing processes, adapting processes, and making things happen quickly. So that’s a really good point too, because not only looking at from the research and development side from the sponsors, but also the vendors, they also are going to have to adapt.
To AI in the industry and what are your experiences with that?
**Christina Busmalis**
So I think the CROs, if we want to say they are the pivotal piece for the biotechs to adapt the process. So again, AI can be used very early in discovering different things. So that maybe not the CROs. But when you go into, want to now take this medicine, whatever it is, and bring it through the clinical trial, the development process, most biotechs outsource that to CROs.
**Sabine**
Mm-hmm. Yeah.
**Sabine**
Right, true.
___
Christina Busmalis:
You know, digital endpoints, all those things—they’re doing them themselves, the big ones. Small companies, it’s hard unless you partner with the big ones to get the funding, right? So this is the challenge that they come out. Not always a big technology.
Sabine:
Yeah, true. And it’s interesting you talk about the digital endpoints because we’re actually working on a project with DIME specifically about this as well. And we’re trying to really involve more biotechs in this space as well to start utilizing. Actually, a lot of them already utilize it, but also to get their voices out more around the digital endpoints and incorporating that into their clinical trials and protocols as well.
Christina Busmalis:
Yeah, that’s a good example of, again, the technology, the sensors, we’re moving a little bit away from AI, but it doesn’t matter because it’s all the same in some way. They’re all connected. The sensors, there’s more and more capabilities getting out there. So the technology is probably not going to be the blocker at some stage. It’s going to be more, how do you get the interdisciplinary communication agreement that this is benefiting and how do you make sure that the data is valid and accurate and clean, those sort of things.
Sabine:
Yeah, it’s connected. Yeah.
Sabine:
Right.
Christina Busmalis:
But it could change a lot of different diseases where you don’t have good clinical endpoints, right? I mean, heart failure, what’s your endpoint? I think there’s gonna be a—and again, where does AI play that point? It could be in the sensors themselves. It can be in getting all this data and getting insights from the data. All those areas are gonna be linked to artificial intelligence as well as the post-war.
Sabine:
Mm-hmm, yeah, yeah, that’s true, yeah.
Sabine:
Yeah, so true. And it’s one of our previous conversations as we’ve had a couple of chats, obviously, since we met and we talked a bit about this. The scientists not trusting—or cannot maybe, maybe not that they would maybe love to trust it, but cannot completely trust AI in the drug development process. And I find that that’s really interesting as well. And I’m curious to hear about how you’ve seen it impact the process and what do you think needs to happen to change that?
Christina Busmalis:
Yeah. So trusted AI—I think that’s a really interesting topic, right? Because there has been, you know, how do you trust, if you haven’t made the decision yourself, how do you trust it? So how do you, how do you as an individual believe any information you get, right? You research it, right? And you make sure you’re confident what you hear from somebody is accurate. And that’s the same thing with scientists. So you can see, I mean, if I, again, I’ll go back to these AI drug discovery companies again, whether it’s Benevolent or other ones.
Sabine:
How do you trust it? Yeah.
Christina Busmalis:
It’s the same. If Benevolent, they use their platform to identify potential novel targets, right? It’s not for a specific disease, based on multimodal data. So taking all this data together, whether it’s publications or patient data, different types of different data, and you can get inferences and information that can potentially link to new targets. So the system doesn’t give you one target. It gives you a list of prioritized targets, disease area based on this. And then, you know, it’s a scientist’s job to go and validate this and to assess these, right? So you can’t just—here’s the list. Here you go. This tells you that’s not the case. And that’s why the process to get that list doesn’t take very long. The process to validate and assess that, to assess the list and then get down to validate and then go into wet labs is where the time is still, And it’s, you know,
Sabine:
Here I go. Great.
Sabine:
Mm-hmm.
Christina Busmalis:
In that list, there’s not going to be everyone thinks great, you’re going to throw away a lot of stuff because the potential evidence that the system would provide is not providing, you know, is good, but not good enough. Or it’s already out there, someone’s doing something, you know, whatever, whatever the biological reason. Then, you know, I think it comes back to biology is complex, super complex. And again, I’m not a biologist, I’ll never say I am, I don’t have the background.
Sabine:
Right.
Sabine:
Right. It’s so complex, right.
Christina Busmalis
And, you know, technology is helping to accelerate that scientists to get someplace faster. Right. And I think, you know, I’ve always talked about AI, you know, actually back in IBM, used, we, 10 years ago, we would say AI is not artificial intelligence, augmented intelligence. Right. Now that word is obvious, right? Everyone says, yeah, we’re not replacing humans. We’re, we’re AI is using, you know, is to help them do their job better and faster. And that’s what that, you know, and so the trust aspect is important because.
Sabine
Hmm.
Christina Busmalis
I think, especially a scientist, you see too much garbage, you’re going to not trust it. And I think that’s what you, it’s not perfect. And I don’t think it will be perfect. mean, things like alpha fold have helped. You have the protein modeling done, folding done in advance. But you still have to validate stuff. You still, as scientist, have to ensure that the evidence that it’s saying is good is good enough.
Sabine
Yeah.
Sabine
Right.
Christina Busmalis
I wish there was an easy way to say, hey, we’re going to change it and make it from this. AI is going to change and everyone’s going to trust it, but we’re not there yet, right? Especially in today’s world, the generative AI where there are hallucinations. You can manage it. It’s manageable, but it takes humans to help manage it. It just doesn’t happen overnight, that change.
Sabine
Yeah.
Sabine
Interesting talking about the scientists. What’s your experience in actually hiring? So to bring people in that have that mindset as well that are like, well, you know, we can use it. We still need to test. We still need to validate. But have you seen a difference also in the type of individuals or maybe the people that are coming out of universities now? Are the mindsets different?
Christina Busmalis
You know, I don’t think it’s an age thing. think there’s always an age thing, right? But I don’t think this, and if I look again at my experience at Benevolence, we had scientists of varying ages. They weren’t super young. There were some young people, there were some old people, there some in the middle, right? And so there wasn’t an age thing here. I think it’s the willingness to approach things differently. I can’t say we’re that, you know,
Yeah, we found very good people coming from other biotechs, from pharma, from university, right? And I think there are a lot of people out there that are willing to look at things slightly different than maybe was done 10 years ago. And that’s probably, again, I have no evidence of base of hypotheses, but I think that’s probably changing more and more as well with people coming out of university. Exactly.
Sabine
Mm
Sabine
Which actually fits to science, doesn’t it? Because it’s all about curiosity. I that’s why I studied chemistry too. I was curious. I wanted to understand and how do things work?
Christina Busmalis
And I think that especially the majors that you get, the university degrees you get now, bioinformatics is becoming just a standard, It was quite rare back then, you needed to do biology or you do technology, now you do both that are coming out. I think it’s just gonna continue to accelerate that way. there is still the, mean, and in most of these AI drug discovery companies, you have a tech side and you have a science side. How do you meld them together is really important. We were able to do that quite well at Benevolent,
Sabine
Yeah.
Christina Busmalis
know, is the tech people have to learn that not everything is perfect on the tech side. And the side people have to learn to start to trust. And when it doesn’t work, to communicate why it didn’t work so that then they can retrofit it,
Sabine
Mm -hmm.
Sabine
So how did you make that happen? Because that is, I think just in general it’s difficult to bring two different groups or mindsets of individuals together and to get them to really collaborate. What was your secret sauce there?
Christina Busmalis
Yeah, I’d love to take credit for that. It’s not me, I would say, because I came later in the organization. I think it was leadership that really brought that together over the years, right, of having that passion and having the right, you know, chief scientific officer, a great CSO, a great, you know, the CTO, and those kind of things to bring those capabilities together. What I saw when I joined as a chief revenue officer was how do you glue it, how do you glue it to make it sellable to drive revenue, right, because that was my job was to bring the revenue side.
Sabine
Hmm.
Sabine
Mm
Sabine
Mm -hmm.
Christina Busmalis
And that was, it was workable, right? Because we’d always get, know, when we did different, you know, we did different sales pitches and things like that, is getting both on the table because, you know, your potential clients want to hear from the scientists as well as from outside of things. And make the story work.
Sabine
Yeah, diverse group at the table. Again, that diverse topic comes up. So, true. So what do you see? What’s in the future? What if you pull up your magic ball? Where are we going with this? And maybe even not in the super long term, but if you look at the next five years.
Christina Busmalis
Yeah, very.
Christina Busmalis
I think so. Again, we talked about, we’ve had AI hit the processes at a very low level, right? So fragmentation of, we haven’t yet transformed drug development with AI. We start to transform pieces of drug development, different processes of it. I would hope over time that we start to really, I would love to see somebody reinvent and that’s got to be aligned with.
Sabine
Mm -hmm. Right.
Christina Busmalis
the regulatories and everything. mean, it’s not one company, but I would love to see some way to reinvent the whole drug discovery process and drug development process, excuse me. One thing I see, I don’t think it’s five years away. I think it’s little bit longer is digital twins, right? So I think digital twins, they’ve been around a while, right? So in manufacturing and supply chain, how do you simulate what you’re gonna do, what you’re gonna produce, right? And you’re now seeing it come into healthcare much stronger.
Sabine
Mm -hmm.
Sabine
Mm -hmm.
Sabine
Yep. Yep.
Sabine
Quite a bit, I think.
Christina Busmalis
Yeah. imagine today we give a therapy hoping it works, right? We get more and more, obviously genetics and the more and more we know the patient and we get more and more confidence it’s going to work. we still go through that, especially in clinical trials, we go through that process of how will it work and who will it work for, right? And it’s a long, painful process for the individual.
When can we get to the point that we can simulate clinical trials with digital twins? Up to the point that we go into patients, and I think you always have to go into patients. I don’t think that’s going to But can you go into patients when you’re at that high level of confidence it’s going to work for that type of patient? Not for all patients, of course not, but for type of patient. And have a high probability of success. I think that’s when we’ve really been able to change. Because maybe there’s not all those phases of clinical trials anymore. Maybe there’s no longer placebos.
Sabine
Mm -hmm.
Sabine
Mm -hmm.
Christina Busmalis
ideally, and being able to use more more digital endpoints, And all those sort of things come together really around that. And you’re right, it is being used already. mean, think John Hopkins University is running a clinical trial, creating a whole computational model of a heart, right? To deal with fatal arrhythmia. So they have 3D view of that individual’s heart. So everyone has something different, right? So it’s on the computer, they simulate the operation before.
Sabine
Right.
Christina Busmalis (25:11.426)
procedure before they go through it. And then they actually go and treat each individual patient differently. Again, we’re not there yet, but I think that’s where, you know, can we get to a point that we do development of medicines completely different than today? I don’t know if it’s five years.
Sabine
Yeah.
Yeah, maybe a little bit longer, but it is coming. mean, look at the warp speed that we’re at right now. It feels like warp speed with the technologies and what’s happening in the industry. So yeah.
Christina Busmalis
And you think about it, mean, even, I mean, COVID helped accelerate that. There’s negative, of course, of COVID, right? But there’s the positive, how we get everyone together. But we kind of took a step back again, I think, since COVID of not keeping that momentum. But let’s hope we can get.
Sabine
Mm -hmm, true.
Sabine
Mm -hmm. Yeah, that’s true. Yeah. And to trust in ourselves and to keep moving forward. That’s so true. So what do you, so where do you, what about you the next five or 10 years? Are there areas that you want to focus more in? there something like drawing your attention?
Christina Busmalis
it’s a great question. I wish I had a, you know, you know, as a salesperson, I should have a direct answers. But, I, you know, it’s, I, I’d loved, I mean, I’m still very passionate on, on, on, talk about passion already. I’m still very enthusiastic around the industry of life sciences. but, you know, I, I, I, yeah, I’d love to see us move faster and I’d to be part of that movement, right? As you know, I’ve, I’ve been in the industry now for a little over 20 years in life science industry, about 20 years.
Sabine
you
Christina Busmalis
working with AI somewhat or something for 12 years. And we’re making progress, but not fast, right? I’d love to, it’s funny, back at the HBA conference, there was a woman that spoke about, she was amazing. And I remember at the end of her talk, she says, this is not for me because I’m gonna retire soon, but for the next leaders, you have to do. And I was thinking like, I don’t want that to be me. I wanna actually tell that that.
Sabine
Mm -hmm.
Sabine
Right, be a part of that change, yeah.
Christina Busmalis
I do have quite a long time to work in the research. But I think staying in, know, really focusing on how technology, not just AI, because I think, again, think technology, I think AI is just one piece of technology, right? But how we can bring, continue to bring technology into healthcare and into life sciences to really, you know, all of us be healthier as we go forward.
Sabine
Yeah, let’s say you have a few more years.
Sabine )
Mm -hmm. Yep.
Sabine
Mm -hmm. Yeah. Yeah, fantastic. I know you will be. Yeah, it’s interesting if you think about the biotechs that are out there and that want to start and that want to take more steps. What are the top three Christina tips that you would give them to take a bigger step into this AI space in their R &D?
Christina Busmalis
want to be part of that journey, absolutely.
Christina Busmalis
I, well, if, if, if, again, if we look at the R and D process, so again, not, the innovation that they’re bringing potentially, but the process itself, you know, choose, choose the CRO, if you’re going to go down the CRO path, which is my most likely thing, choose CRO that’s going to be a trailblazer and maybe get on, get on that bandwagon or, or partner, you know, ultimately most of these companies partner with the big pharma, find a pharma company that can, that can help you drive what you want to do to go forward as well.
Sabine
Mm Right.
Sabine
Mm -hmm.
Sabine
Mm
Christina Busmalis
think that’s the interesting thing out there. It’s an exciting time, right? But there is that thing of you can’t do it alone. And if you want to go sideways, you’re going to have to find the right companies to bring into your ecosystem and take the time to do that versus just, that’s just outsourcing to XYZ, right?
Sabine
true.
Sabine
Yeah, yeah, so true. And knowledge sharing, I think that that’s such a key thing. And I think sometimes we tend to hold information and great things close and not necessarily share them as much. And I think that that has such a huge impact as well.
Christina Busmalis
Yeah, that’s a great point. I think, unfortunately, the industry has driven that sort of closed door kind of way. I don’t know. It may come down to individuals that are more interested to share knowledge than the companies driving that. Because you’re right. And again, that’s what COVID did, right? Companies came together to get something right and looked through it. And yeah.
Sabine
Mm -hmm. Yeah.
Sabine
Yep. Exactly.
Sabine
Hmm, Christina, maybe we’re gonna have to start like some roundtable discussions or next project to get people talking and sharing information.
Christina Busmalis
I would love to hear from you, those trailblazer biotech companies, what do they want to do? How do they want to not just be innovative of the medicine that they’ve identified, but as a company of, you know, as
Sabine
Yeah.
Sabine
Yeah, and to the greater good. So you’re right, to their organization, of course, that’s what it’s about. They have to be successful and then ultimately it’s to the patients and bring something there. But how do we come together to blaze more trails? Yeah, like that. All right, well, we’ll have to see if that sparks, if there are any trailblazers out there that want to be part of
Christina Busmalis
Yeah, exactly. It’s a good, it’s a good trailblazer. Yeah.
Sabine
part of something, let us know. Because it is true. mean, we hear a lot of CEOs, a lot of individuals get together for round tables and maybe it’s setting up like this kind of the safe environment where you can go and that’s your place to talk about these kinds of things and to look how you can leverage each other. think that that’s so often, we don’t have to all be in this alone. There’s so many great things out there.
Christina Busmalis
Yeah, because especially the drug development process is the same. mean, okay, it’s different if you’re in cell gene therapy. There’s different, I mean, the process itself is different, but it’s the same for the medicines, right? It’s not that, you it’s, everyone needs to get faster. Everyone needs to get cheaper, right? Versus one company is going to do it and no one else is going to follow, right? They’ll eventually follow the same approach.
Sabine
Sure. Mm
Sabine
Yeah.
That’s true. Well, some more food for thought for me and some things to ponder. As always, I think every conversation I have with you, I leave and you definitely get my wheels spinning and thinking Christina. I’m like, hmm, what can I do now? So thank you so much. It has been a treat.
Christina Busmalis
on the screen.
Sabine
to have even to have this conversation. I’m sure we could go off in lots of different directions. So we’ll see if we have you back again and maybe focus on a bit of a different area. And to everyone listening, know, Christina, if you have any suggestions on any books or articles or things that you find super helpful, we’ll have you share those with us and then we’ll share those in the notes of the podcast. That’d be great. It’s always good to
to learn from others about what’s valuable read. Yeah.
Christina Busmalis
We’ll do that. Great. No, thank you very much. I really enjoyed the conversation and happy to talk again. And yeah, wish you a good Monday.
Sabine
Thank you, you too, wishing a good week ahead.
Christina Busmalis
Thanks.