Infrastructure Wins: Research & Diagnostics • Danaher / JC Gutierrez-Ramos
Infrastructure Wins
Breakthroughs don’t fail because of bad science – they fail because the systems needed to deliver them don’t exist yet.
In this episode of UnNatural Selection, Nic speaks with Jose-Carlos Gutierrez-Ramos, Senior Vice President and Chief Science Officer at Danaher, about the hidden bottlenecks slowing progress in life sciences – and what they reveal about innovation in every industry.
JC shares how his journey from patient to immunologist shaped his mission to improve human health, and why the biggest barriers today aren’t discovering new therapies – they’re selecting the right patients, generating real-world evidence, and building the manufacturing and diagnostic infrastructure needed to scale breakthroughs.
The conversation explores why innovation increasingly happens between industries, why incentives across ecosystems often don’t align, and why the next decade will reshape who leads discovery, development, and delivery.
This episode is about the uncomfortable truth behind progress: discovery is only the beginning.
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Nic (01:42)
JC, thank you for being here. It's such a great pleasure to have you and it's great to see you again.
Jose Carlos Gutierrez Ramos (01:48)
Same here, it's great to be with you and your audience.
Nic (01:52)
⁓ We go back 10 years. It's been a while. I think the first time we made contact, you were a leader at Pfizer. And I think I just cold called you on LinkedIn just to make contact and just kind of start a conversation. And then over the years, we've been involved in startups together and we've just remained long-term friends. So it's been, great to finally have you on the show.
Jose Carlos Gutierrez Ramos (01:58)
War. War. War.
Same thing here, same thing here.
Nic (02:24)
⁓ To get started, I always try to set the context and start with a foundational question that gives the listeners an understanding of, as we go through the course of an hour conversation, what has motivated you all this time and what really drives the work that you do? So can you please let us know, JC, in your own words, what need or impact drives your work?
Jose Carlos Gutierrez Ramos (02:48)
You know, I've profound, you know, I've been a scientist like you for 30 years or 35 years, and I fundamentally love science. The process of, you know, discovering one area and going deeper into it and learning about it and the fascination, I use this word sometimes, I get transfixed in some of these
feels that which is going to. And so that's what really, really is at the core of my personality and behavior, really is curiosity, is exploration. And I was lucky. I was lucky because I could have, and also maybe exploration, curiosity, and maybe a little bit of ADHD as well. I just, I come from Spain. was born in the sixties, so there was no ADHD.
but it was like just a guy that was not focusing. And I was lucky that by chance through my life, I could put that passionate play towards trying to help human health. And so if I can say in one word, and I hope that this doesn't sound megalomaniac, I do it at my level, of course. ⁓ What drives me is...
Nic (03:50)
Hahaha.
Mm-hmm.
Jose Carlos Gutierrez Ramos (04:17)
that I can, I've been lucky enough that I can use my passion and my instincts and my training to try to help, to have an impact in improving health outcomes. And also it's very personal for me, you know, when I was, I was being a, come from a family of Navy sailors in Spain. My grandfather was an Admiral, my father was this, my uncles, my aunts are married to sailors. So I was training to be
Nic (04:39)
Mm-hmm.
Jose Carlos Gutierrez Ramos (04:46)
Navy sailor. And then I got a non-Hodgkin's lymphoma when I was 21. My spleen broke, my liver was a mess. And so, I was a year in the hospital, literally, with chemo and this was pretty reticent. so, that changed my life completely in terms of trajectory. And I always say, I'm a patient. For many years, I was a patient because I was like sort of this submissive behavior.
Nic (05:00)
Wow.
Jose Carlos Gutierrez Ramos (05:17)
that you want to be good, you want to get through. And I saw many people in hospital later on that were not as lucky as me. Lucky, really, because medicine to some extent is luck still. Less and less, with more molecular, molecularly driven, but imagine. So this is to make a long story short that I think is a combination of a personal motivation.
Nic (05:20)
Mm-hmm.
Jose Carlos Gutierrez Ramos (05:46)
And we all have it. In my case, it's that one. But you know, it's your parent, it's your father, it's your mother, it's your wife, it's your daughter. It doesn't matter. We all are touched by disease and by the frustration of not knowing, not able to detect, not able to characterize. And only at the beginning of treatment, really, that's we are. And I think that to be in that interface is something that has been pure luck for me. And I'm going to...
exquisite like a lemon until the end of my days. I'm not moving from there for that intersection of science, medicine, and awe and exploration.
Nic (06:17)
Thank you.
Well, you know, I think for the benefit of those of us that are recipients of all the medications and all the therapeutics that you've worked on, I think it's a good thing for us because you've been so influential. Again, I started by mentioning you were Pfizer. So from the pharma space to the ⁓ now you're in research and diagnostics, you've been in startups. And so you've had your fingerprints are through all kind of the spectrum of.
⁓ from research to discovery all the way through treatment and so on. And so I think we're lucky to have you be so inspired by this. And ⁓ then, you know, just for the sake of the audience that may not be familiar with the space, can you let us know for people outside the industry at a high level, what does Dennerher do and what role does it play in the broader research diagnostics ecosystem?
Jose Carlos Gutierrez Ramos (07:21)
Yeah, no, it's of course an important question. I'm more than happy to tell you this. You know, maybe I make an intro to that. In my career, as you said, through pharma and biotech, trying to develop drugs, I had two consistent barriers, even when I was accountable for them. I couldn't blame anybody. It was me, it was my department. And I'm talking to all the barriers and all the obstacles then.
the complexity of biology and pathology and disease. That of course goes without saying, but you you get at it. But there were two things. was patient selection. You know, you might have a drug that is active. Most of the drugs are not active, active meaning that has efficacy. And you might not know, and it's active in 30 % of the patients that you try. Today,
Unless you can predict what is the 30 % of patients that you try, you might not develop it. You might leave it. Phase three, you might not get funded in the last stage of, because it's difficult to compete, it's a risk. Now you're going to put another half a billion dollars to a billion, depending on the indication in that last push. And you don't know exactly who's responding. You might end up at the same 30 % as a competitor drug. that, or you get through.
Nic (08:30)
Mm-hmm.
Jose Carlos Gutierrez Ramos (08:48)
But then in the marketplace, doesn't perform well. So patient selection is super important. And I'm going to go back, of course, to Dana Her. And then the second topic has been always a limitation has been that to do true experimental medicine, meaning to experiment, of course, ethically and with all the controls. If a drug works in humans in early phases of development.
is truly not doable at scale because it costs you $3 to $5 million to generate enough drug to do a small experimental study. I'm talking about a naked antibody, I mean a simple antibody treatment. And that, course, know, how many of those can you do? So just to go back, then these two were barriers to really do experimental medicine to choose the best drug.
Nic (09:31)
Mm-hmm. ⁓
Jose Carlos Gutierrez Ramos (09:49)
Or if I have a drug that maybe is not the best, but it's good enough to select the patients. And I reached the conclusion that pharmaceutical companies probably are not the best place to do either. And I tell you why. ⁓ Precision medicine or personalized medicine requires detection, requires diagnostics and
In a pharmaceutical company, of course, you have a large division of what we call precision medicine colleagues that are finding the assays. Of course, we understand the pathways. They configure an assay, they deploy it to a clinical trial. But if you are lucky that the assay performs, then you have to partner to bring it to scale. Not just 10 clinical sites or 200 clinical sites, but to thousands of clinical sites. You have to partner with a diagnostics company and
The diagnostics company incentives and the pharma company incentives are not necessarily aligned and it's invariably a jerky advance. So I reached the conclusion that unless diagnostics companies really pick up the ball of evidence generation and on precision diagnostics, it's going to be very difficult to...
Nic (10:58)
you
Jose Carlos Gutierrez Ramos (11:16)
take advantage in real time of all the great drugs that are being developed. It will happen, but it wouldn't take, instead of taking 10 or 15 years, it's to take 20 to 40. Because, you know, diagnostics companies have traditionally work in a more technical comparativity, comparison realm rather than generating clinical evidence.
And Danaher, so Danaher has a diagnostics division that is around $12 billion in sales. We're up there with Roche and with Abbott. We perform 1.6 million diagnostic tests an hour in up to 40,000 sites, maybe 18,000 hospitals.
Nic (12:08)
Well...
Jose Carlos Gutierrez Ramos (12:17)
So it has the scale to address that issue. Can we truly...
Enable precision diagnostics and I will talk more about that So then I heard that's diagnostics and then that I heard in one division in the second division called biotechnology also is called site Eva there's a lot of bio manufacturing and To address the second topic that I was talking about, know the ability to really generate lower the cost of drugs the drug manufacturing
Nic (12:26)
Mm-hmm.
Jose Carlos Gutierrez Ramos (12:52)
that allows much more experimental medicine or much more access even later on. And this is obvious, for example, in new modalities like cell therapy or gene therapy, as you know, it's very expensive. But even in antibodies, I mean, again, you know, most of CMC and manufacturing of antibodies is recombinant DNA technology of the nineties rather than synthetic biology of the 21st century. And I think because the...
pressure is so high on pharma to, in the moment that you have a signal in human just to move the drug forward, even if the process is not perfect, it doesn't matter. The end is 12 to 14 % of cost of goods is only 12 to 14 % of the cost of the drug. So it doesn't matter really. I mean, it matters, but it's not that dominant. So I think that companies like Danaher or other companies are in a better position to bring the latest science to bio manufacturing.
Nic (13:34)
Mm-hmm.
Jose Carlos Gutierrez Ramos (13:50)
And that's the second division that we have, and that's where I came here. The third division that we have is a life science analytical methods and reagents company that performs, that is also similar, like around $10 billion in sales from instruments like mass spec, microscopes, reagents, like antibodies, conjugates. Overall, if you look at the three divisions,
diagnostics, life science, analytical methods, analytical instrumentation and ⁓ bio manufacturing or biotechnology. We really are an enabler of biopharma globally. And that is what, and I think the unique opportunity to bring, that's what of course drives us to work every day, the latest science and technology to accelerate each one of those.
Nic (14:46)
Yeah, it's great. Thank you for that. And it's great that you touched upon the challenges that pharma companies have. Like yourself, I've been in biotech and pharma. I've been in life sciences and research. I've been all the way downstream to clinical delivery and point of care. And a lot of times you hear people that don't know better talk about, and by no means are pharmaceutical companies saints.
There have been tremendous errors made in the past by some companies and so on, like all other industries and companies. But you hear people talking about how drugs should be free, or you hear people talking about how pharma companies aren't incentivized to cure diseases because they would rather sell the treatments for it. And when I hear this stuff, I think to myself, maybe they should read a little bit more about the process. It's incredibly hard.
discovering drugs that work at these minute details and modifying whether it be a molecule, an antibody or a gene or any combination of things. And even if you can get to that point, it's even hard before I to understand what the mechanisms of reaction are in the first place and be able to then manipulate them in such a way that you can save a person and not just be toxic and hurt them more. And there are so many complexities in between that everywhere that I've been, whether it's been helping to start biotech companies or consulting for bigger ones,
everyone's always trying to do the right thing. They have their strengths, they have their weaknesses, but I agree with you that pharma companies aren't necessarily in the best position to be able to apply modern technologies, especially because what they're doing at scale requires a lot of their thinking, a lot of their concentration, following regulations and all the other things that they do. So a lot of the experimentation can be left for the research and diagnostic companies. They're relatively a little bit more nimble.
Jose Carlos Gutierrez Ramos (16:36)
Yeah, yeah, no, that's right. So as you know, for example, I don't know the numbers in 2025, so I might be dated, but 2024, three out ⁓ of four, well, two out of three, don't get me, ⁓ biologic drugs that were developed in pharma came from biotech. So the discovery, and again, same thing, very likely for small molecules, the discovery piece of it, the path of biology.
And even the platform, the therapeutic platform, mean, self-therapy was started not in a big pharma, or in all CAR-Ts, or then it became big, but you know, was really either academic medical centers or small biotech. So the process of bringing new targets with new candidate drugs and new platforms, other than small molecules or antibodies, even the antibodies started outside of what was pharma at the time in the 80s and the 90s.
comes from a smaller name, but they rarely invest because it doesn't have the same value perception upfront on manufacturing, optimization, or on patient segmentation. And I think that that is where, and as you said, there is a gap because when it comes to pharma, again, you know, I'm focusing now on, you know, again, on clinical, on clinical development, there's going to be 90 % of the cost.
Nic (17:45)
Mm-hmm.
Jose Carlos Gutierrez Ramos (18:04)
and the risk that I'm going to take. So there are gaps in different areas that I think that have to be taken by some group. And I think that is a perfect group to do it. And there are others, of course. I think it's a little bit of musical chairs at the moment. ⁓ how, particularly now with computing and data science that you know very well, of course. ⁓ I think in the next 10 years, I feel like musical chairs, like everybody's up, there are five chairs and...
who is going to take the seeds is not clear because discovery is going to look different, obviously, much more computational. Even modeling that before, I mean, I'm talking about pharmacological and physiological modeling was, you know, was like hampered. It was like one hand in the back was based on ones and zeros rather than now having a much more complete picture. Then, of course, clinical trialing that was really inefficient and
Nic (18:37)
Mm-hmm.
you.
Jose Carlos Gutierrez Ramos (19:04)
will be improved. so I think in this process of moving chairs, I think there is a great opportunity for us as patients, for us as scientists and clinicians to really have an impact.
Nic (19:20)
Yeah, I agree. I definitely, a lot of what you're saying resonates with me. The historically treating, diagnosing and treating disease, if we look back just even human history has been a random walk of luck. If we look way back, when we were treating disease with either herbs way back or even with leeches and bleeding.
It was just random luck and they had all kinds of names for diseases. then sometime during history, we ended up coming up with names like cancer, which also meant a whole bunch of stuff because it was just like a general thing that makes you sick. Then we realized, no, it's actually there are different types of cancers. And then genetics came in and we realized, okay, even breast cancer means multiple things. And now we're understanding more that even those things can be multiple different diseases within one area. And so I'm really excited by it. Like you said, in the beginning, where the science is going.
Now we're understanding far better and there's still so much more to learn, but we're understanding so much at the multiomics level that genomics, proteomics, lipidomics, metabolomics, all these things independently, we're learning a lot. We're not crossing that border yet where we're starting to look at them in combination. And as you know, what bubbles up is your health is a combination of all those things, including your behavior and your environment and your socioeconomics.
But now you start looking at very, very complex data. You don't really know what the relationships or the patterns are, but in Wokson, the modern technology of AI, AI has been around for a long time. We've been doing machine learning and neural networks for a very long year, but the LLM form that connects all these patterns. And so I feel like things are accelerating and things are to get very exciting. But to your point about the musical chairs, I hadn't thought about it like that. But one of the things that I have seen just in this podcast alone, I've interviewed
pioneers in the life sciences and the pharmaceuticals and the medical devices. And even in the big digital companies like the, you know, the Barreles and Microsoft's. And one of the things that I'm noticing is there's this convergence. The borders between, know, just five years ago, there was a very clear separation. You're either life sciences and diagnostics or you're pharma or you're digital. And now they're all kind of like, there's Venn diagram where they're all morphing more so where now I'm thinking like, wow.
Siemens is a competitor to Thermo as much as it is to Sanofi. And it's like, it's not clear who's who. And so when you said the musical chairs, I started thinking to myself like, yeah, it's actually happening very, very quickly. And it's not even just within one industry. It's a bunch of industries all coming together vying for the same four seats.
Jose Carlos Gutierrez Ramos (22:05)
That's right. That's exactly right. Exactly right. It's even more than before. And that's good for us. I mean, that's good for society. It's good for... And I think this disruption that we see is, know, companies will get dislocated or dislocated or dislodged. So two things, as you were talking, two things came to my mind. One, I think it's important also for all to realize, listen, of course, disease and drug development and drug discovery has been a little bit like...
Connect the dots. You remember this game? I think it's called connected or cynical it would you go and say, you know one thought and because it was really Binary one to zero. Okay. Do you have this assay? I mean you get to the hospital or even in the lab Okay, do you have this protein one zero was up, know, do you have this gene mutated one zero? so we made we had a Sometimes few dots and we have been adding those but at the end dots
Nic (22:54)
Mm-hmm.
Jose Carlos Gutierrez Ramos (23:02)
And then we were, think until recently and still today possibly, in a situation where you look at the dog and say, ⁓ is this a horse? The legs, ⁓ it's an elephant. You don't see the trunk, but you know, that is to some extent, certainly in my time, you're younger than me, but in the early 2000s, that's what we were doing drug discovery. This in five years, in 10 years today, I mean, today we see it in language, as you said, and
Nic (23:09)
Hmph.
Jose Carlos Gutierrez Ramos (23:31)
and encoding. But for biology, think that before we know, we'll go from connect the dot to see an image to a raw video data file of your disease. Like this. I again, I say this to shock my colleagues because that's the comparison.
Nic (23:48)
Mm-hmm.
Jose Carlos Gutierrez Ramos (23:57)
Of course it will not happen overnight, but at full characterization. And of course the problem is that we have been talking about metabolomics and genomics and I've been talking about it. I worked for a company called Millennium Pharmaceuticals in the late 90s. And of course we were dreaming with all this, but the technology was not there. The physical technology and then the technology, the physical technology was developed, of course with sequencing and this, but then the computing technology, as you said, was not there really. Now, and you know more than me,
amazed by the ability of autoencoders to transport data of different types. So I think that would you describe before maybe one thing that you have to validate on us because you are the expert. But I think that our ability to merge data, multi-layer data, multi that before it was not possible, it was anecdotal one paper that compared this, but at a massive scale to generate that raw video file of your disease specifically.
Nic (24:50)
Yeah.
Jose Carlos Gutierrez Ramos (24:57)
I think it's closer than ever, maybe not completely there, but close, don't you think?
Nic (25:02)
I totally agree. To your point, the examples we have historically of connecting all these different dots multimodally has been for a paper where some people very painstakingly went through and they already kind of knew the answer beforehand. They kind of wanted to show that, this works and there's a science to be had in looking across layers. I think the exciting part now with the modern technologies is we can start to get to a point where it can be done at scale in clinical implementation.
So it's no longer theoretical, some interesting paper in nature. Now it's something that your clinicians might be able to use in real time, not today, but in the next two, three, five years. And science is just going to completely change based on all this knowledge that we've been building up since the Human Genome Project 20 years ago, all the Olmecs, LLMs. I think all these things are converging. And I think part of the reason why all these different companies and different industries are converging too is because they see the opportunity.
They're like, I don't want to miss that boat. don't want to be the one standing at the end of these musical chairs.
Jose Carlos Gutierrez Ramos (26:02)
Exactly. How science, you know, you know this, I mean, we think a lot about this, of course, and act on it ourselves. ⁓ With this 1.5, 1.6 million tests a day that we generate. So of course, there are chemistry, immunochemistry, genomics, mean, PCR, pathology. So, you know, when we look at disease, I'm going to start in diagnostics and then I go to life sciences. When they look at disease, unfortunately, we biased.
depending on your training, oh, I'm going to look at genomics, right? I'm in your genes and I look at cDNA. But of course, maybe the best, no, maybe not, almost 100 % sure that the best definition of your disease is a combination of one chemistry, 10 genes, two proteins, because at the end, you don't know where you're going to, in that moment in time, other than having longitudinal follow-up of yourself,
At that moment in time, maybe something that was a protein is now a metabolite downstream or something that was a gene, ⁓ you know, was not quite mutated, but is a post-translational modification that you wouldn't get. So I think very likely the multi-modal or the multi-parametric combination of diagnostics would be much more powerful in defining your journey. And similar
In biology, I think this multimodal definition of a process, know what, the suppression of the tumor microenvironment of a tumor, right? You know, instead of looking at 10 things for the paper, as you said, not for the paper, that sounds bad, no, for a genuine interest that you have, but that you cannot scale, you cannot generalize. I think that you guys, the computing guys have brought this important term.
Nic (27:46)
Mm-hmm.
Jose Carlos Gutierrez Ramos (27:55)
That is general. mean, I think it's coming from you guys. don't know. Generalizable. These generalizable platforms that are physical or computational are super important. And I think that's where, where science or this biomedical science got a little bit stuck. And a lot of the things that we're doing were not generalizable. And again, we couldn't, I guess. I mean, I'm not blaming ourselves. You had an observation was reproducible and therefore you put it forward.
But it was not generalizable. think that the scale that we operate today allows to be, to really be more generalizable. Something that's just come to my mind. My daughter is making a, is a PhD in neuroscience. She's doing a PhD in neuroscience and she took, I hope that she doesn't listen to this, know, like that I quote her, but she took a biostatistics course, no? The first two years of their PhD.
Nic (28:53)
Mm-hmm.
Jose Carlos Gutierrez Ramos (28:54)
And I go, oh my God, this is super important. And then she goes, Dad, this is completely outdated. And I no, mean, you know, because in parallel, she's work, she's working databases, 10,000 subjects with imaging and this and that. says, well, you know, and the examples, of course, in the book in traditional biostatistics are statistical analysis of, you know, don't know, 50 miles with this and that.
So I think that we see a complete dichotomy in which new generation of scientists are all data scientists. In fact, I gave a talk recently where I was just saying, which science? You mean data science? Because it's difficult to separate both anymore. Remember how we even in your time, think it was like bioinformatics and then the other ones. I think when you hear, when I go to a company and they have this separation and of course some separation still.
Nic (29:31)
Mm-hmm.
yeah.
Jose Carlos Gutierrez Ramos (29:53)
Makes sense, you know, it's a new world where data is our gate to this multi-layered, multi-layered, multi-modal reality that will get us closer to the truth.
Nic (30:07)
I totally agree. it's, and I think whether you consider yourself low data, big data, medium throughput data, ultimately the sciences are really all data sciences, right? You're just trying to collect as much data as you can. Sometimes it's little. If you're working with cells, like I was a stem cells 25 years ago, you collected very little data. I was told data sciences. was just like individual data points.
Or if you're working with genomics or if you're working with astrophysics, it's tons of data. But I think those barriers are starting to thin out now because there is a lot more data and there is ways of dealing with it that ⁓ I think, especially with the way that AI is disrupting a lot of fields, that people that are going to be the most well off in the future, regardless of what field you're in, are the ones that are comfortable dealing with a lot of data and making higher order decisions.
that are driven towards a science or society, things that impact humanity.
Jose Carlos Gutierrez Ramos (31:10)
That's right. Even the, I again, I don't want to be, I don't want to go too deep on this one. If you want to go to another, just redirect me. But even as you know, lab science is, and we deal a lot with this in our company, in our analytical instruments and reagents division called Life Science Instrumentation Group. How is going to be the lab of the future? The future, I don't mean 100 years from now, five years from now.
Nic (31:37)
Mm-hmm.
Jose Carlos Gutierrez Ramos (31:39)
What is the role of a scientist where 90 % of the work that we do in the lab is very likely, you know, automatable and agentic workflows can be taking care of most of it. And also how much of the lab work will be data collection versus hypothesis testing, ⁓ physical versus hypothesis testing, mainly computational for all the reasons that you said.
Nic (31:50)
Mm-hmm.
Jose Carlos Gutierrez Ramos (32:09)
Probably it's because it's difficult in a physical world to deal with all the parameters that you want to deal with when you ask thoroughly a question. And I think that world is something that we are very, at Danaher, we're very, very interested in. And we just signed up, for example, a big partnership with a company in London called Automata that is mainly basically had this concept of a lab bench, like the ones that we always had, but it's not human. You know, it's all, it's...
It's not just automation, it's autonomous, right? And it learns. of course, there are different lab ventures for different functions altogether, or different, ⁓ not just one task, but one discovery or development or process developments that is learning and is going back and is connected. So what is going to be a scientist? And is the main creative science the one that...
is going to make questions computationally.
The other is going to be agentic workflows that just load ⁓ all the cell types that you can get with as many conditions as possible and measuring as many things as possible and by multicellular combinations in organoids and tumuroids just to get to another level of complexity and then another level of complexity is going to be fascinating.
and we are trying to position ourselves to help in all these parameters.
Nic (33:41)
Mm-hmm.
That makes sense.
So if we pull this back to Danaher in your role as CSOs, largely to be able to steer innovation in the direction of the company, what does innovation mean to you in the context of the work that Danaher does? Obviously, innovation in pharma is a different, in software is a different ballpark, but in the Danaher sphere, what does innovation mean to you? And then the second part of that is
How did your experience being a patient affect your thinking about what innovation means?
Jose Carlos Gutierrez Ramos (34:24)
Yeah, very good questions, Nick. So maybe I start with the first one.
You know, one of the things that I do with my colleagues, step back and say, okay, in a world like our world today, with all the vectors and variables and technology changes, what are the pillars of where biomedicine and biomedical research goes? It's going to go. ⁓ Regardless of the how, the pillars are really four, I think.
One, and we try to address them with our technologies and build them in one way or another, alone and with partners. But the four are, you would you want to detect disease early, as early as possible? And you know the numbers, right? I four weeks in the delay of a detection of cancer, increased mortality by 10%. No, detection, no, treatment, delayed treatment in cancer.
Nic (35:33)
Mm-hmm.
Jose Carlos Gutierrez Ramos (35:35)
50 % of lung cancers are detected at stage four, small cell. ⁓ In neuro, it takes 18 months to 40 years from the beginning of neurodegeneration, the beginning of cognition decline to get to a neurologist. And 70 % of those patients, by the time they get to a neurologist, already have passed the window of treatment.
And again, it's the beginning of it. Now there will be more treatments, of course, the beauty is that, still. So detecting early, I'm not talking to screening. Screening is of course important, but we'll get to that just early detection. So that's one. And I will come back to that. Second, of course, detection early is important and molecular definition of your disease. Number two, super important in cancer. What is your disease? What are they? As you were saying before, cold.
Nic (36:12)
Mm-hmm.
Jose Carlos Gutierrez Ramos (36:34)
breast cancer, many things we call, and now we know the triple negative. So what is your disease? And we're getting more and more in cancer, certainly better, but in other areas, or the immune disease, not good. number two, number three, you know, the treatments would be more and more tailored to your molecular characteristics. Now, 80 % of the drugs that we develop in oncology are molecularly targeted. And it's not personalized.
Nic (36:43)
.
Jose Carlos Gutierrez Ramos (37:03)
to your, I mean, like we do it now in genomic medicines for rare diseases. It's not gene editing for one, right? And we are very big in that. We'll talk about that later. We have decided to build and we treated patients with a physician scientist for gene editing in vivo. So it's not gonna be that way, but it's gonna be more and more personalized. And if cancer vaccines makes it with personal new antigens, it'll make a big break.
Nic (37:11)
Mm-hmm.
Jose Carlos Gutierrez Ramos (37:33)
That would be a big direction. one. So, so, and, and this goes back to our patient segmentation before. It's going to become more and more personalized. And fourth, once you are treated and you are in remission and your disease is more and more chronic. And we have seen it, right? Multiple myeloma 10 years ago, 15 years ago was a death sentence. Now it's a chronic disease.
Breast cancer was a disaster. Now, you know, still you can be unlucky, but for the most part you are managed well. HIV, I mean, you name it. So more and more of these, you know, deadly sentences are now chronic diseases and therefore MRD detection of relapse, call MRD in cancer, but in general monitoring yourself and understanding what's your trajectory is going to be super important. So these four things.
define us as patients, because it's important to us, define how medicine will be, probably you be, I mean broadly, but also define the type of research that we're going to be doing in academia, because at the end you're asking one of those questions directly or indirectly. And so at Danaher we're trying to address these four. And in early detection, for example, we have...
Nic (38:36)
Mm-hmm.
Jose Carlos Gutierrez Ramos (39:01)
I would say one of the most potent platforms for immunoassay, we detect atomols, ⁓ atomols of a molecule we detect in the periphery and again we published on this, neurodegeneration proteins that other people can detect only in the CSF, we detected in the periphery. Post-multi-sational modifications. We launched a machine called the XI9000 that is really remarkable. We bought Upcam.
Upcom is an antibody company that with antibodies allow us to detect this type of analytes just to increase our menu and give those. So this is at the protein level. At the genomic level, of course, circulating tumor DNA has been fundamental. And we have several companies that have partnered with Exact Sciences, with Natera, that build the libraries, company called IDT, that build libraries to detect your tumor.
super, how do you call it, sensitive detection is important. And then it will be complemented by this computing that we talking before, multilayer. In understanding molecularly your disease, one of our companies, and there are others, but I think we're very good, called Leica Biosystems, a pathology company that is really trying to determine
in the tumor simultaneously in a quantitative fashion. I'm talking about generalizable. Of course, we can detect, this molecule from the tumor microenvironment, this molecule from the tumor, this molecule from the immune system. These are the few players that determine if you are going to progress or not. Well, now we have technologies that allow us to quantify. And of course, pathologists or a doctor wants a clear-cut report. They don't want, you have a little bit more of this.
And that is something that is not easy, but we'll get there. We and the field will get there. Because, you know, it's important to say, you know, I have, we both have bladder cancer, but the best target for me is the ADC connecting for, and for you is combined with immunotherapy, and for you is FRNA2 with something else. I think we will get there. And the field will get there too, as there are more and more precision targeted medicine.
Nic (41:01)
Mm-hmm.
Jose Carlos Gutierrez Ramos (41:26)
you need precision targeted diagnostics. I think in therapeutic tailoring, I think for rare diseases, N of one, N of hundred, N of thousand, a lot of it is gonna be mRNA-based gene editing for true genetics. Then I think mRNA vaccines, I'm not talking about prophylactic vaccines, ⁓ I believe in those too. I mean, obviously they don't give you long-term memory, but it's a question of...
doing research, we'll learn how to do that with other modalities. So we'll learn to do that. But just, ⁓ you know, CAR-Ts are now being done in vivo with mRNA transfer. So there's a lot that can be done. And then going back to computation, even for small molecules or for antibodies, the fact that you don't take two years and 10 cycles of
Nic (41:56)
Mm-hmm.
Jose Carlos Gutierrez Ramos (42:23)
medicinal chemistry and synthesis and optimization, but rather a month to get your candidate because computationally you can generate trillions, right? Also makes the possibility of having a much more tailored to your polymorphic. You know, remember your genes are polymorphic and therefore your target for disease X might be slightly different than mine. And you know, if in a month, with relatively in a week,
Nic (42:40)
Mm-hmm.
Mm-hmm.
Jose Carlos Gutierrez Ramos (42:50)
relatively less effort that can get a drug that is for your polymorphic variant and you and the guys that have your genetics, genetic configuration, that's what we're to get. And then finally, the last thing that I say is this following disease trajectory is super important. And, you know, we just announced that we acquire a company called, that we would like to acquire a company called Massimo. We haven't closed the deal yet. That is a disease that
There's a company that specializes in sensor technology, particularly around pulse oximetry, but many sensors. And we believe that also the whole area of sensing.
Nic (43:21)
and
Jose Carlos Gutierrez Ramos (43:32)
In addition to biochemical and genomic, I think will bring another level of of longitudinality to to your disease trajectory or patient trajectory to determine, you know, is the disease coming back? Are you likely to to relapse? Are you not likely to relax? How do you move the expression of your face, the color of your skin? There's so much information, as you know.
Nic (44:02)
Mm-hmm.
Jose Carlos Gutierrez Ramos (44:02)
that
probably by itself would be enough, but certainly for a while combined with biochemical parameters. So I think these are the four areas, the four areas and these four that guide our direction to some extent are the areas that as a patient, you know, I would like to see. For years I was terrified of the disease coming back. And even today, if I, I don't know, I get a bad flu or something, you know, I go, I call the doctor right away.
So I think that to understand the U.S. disease can back understand, am I going to be 30 years? If somebody would have told me, hey, you know what? You had this mutation and this is your blood profile is unlikely. I mean, have 0.1 chance that over the next 30 years, you have a relapse or that you have your, I will have lived the first five years after I left the hospital much, much better than later. ⁓
So
anyway, I think these are the key principles. I think in moments of change, have ⁓ to take advantage of the change, 100%, embrace it and lead it, but at the same time anchor to what is important for us as humans, right? Particularly in health and disease.
Nic (45:17)
Yeah, I totally
agree. And like we said in the beginning, both of our careers have spanned kind of like the full spectrum from early discovery down to point of care. And so you said a couple of things there that really resonated. It's sometimes you're so focused on the silo that you work in, which is like some scientific discipline or some technology, or it could just be a device that you don't think about how it will be adopted into the broad population.
And you mentioned there, a clinician, when they get a report, they want something that's actionable. I've worked with so many clinicians, and they tell me, it's like, OK, your technology is great, but don't come to me with more problems. I want solutions. So if you're going to identify a risk, if you're going to identify some biomarker, whatever it is, your report better tell me what to do. And then I'll take it from there.
But if you come to me with some statistical model that tells me there's a risk here and there's no solution or nothing I can do about it, I don't have time to go and do the research and figure out what to do with that. And then all the way down to the level of patient care, like you said, as a patient, one thing is to treat your condition. Another thing is to answer some fundamental human questions that you may have about what does this mean for me going forward? What does this mean for me, my diet or for my children? Any number of things that...
more times than not, patients in the believing in doctor's office with kind of more questions than answers and very unsatisfied and very scared. And so the more that we can kind of take the initiative of thinking how to solve a particular scientific problem, but then also how it will be implemented in the course of care and then how it affects all the stakeholders on stream, I think that science eventually is gonna move down that path, especially as we have more people like us that think.
at a broad scale thinking more than our little window of work, but really how is this going to affect health care in the long run?
Jose Carlos Gutierrez Ramos (47:14)
Not only people like us, I mean, think people like us for sure, but also, you know, my wife has developed this interaction with a language model around the health of our dog. I mean, I'm using this where that language model knows the history of the dog, knows my wife and suggests very sophisticated.
not therapy outcomes, but therapy solutions. But when my wife said, what if I do this about a test or a treatment, or could this be this? There is quite a learning that has occurred about this specific individual. In this case, my dog, so I use it on purpose versus a human. But you know that I'm surprised. So I use this as an example to say we as patients, all of us.
Nic (47:59)
you
Jose Carlos Gutierrez Ramos (48:11)
Certainly some of us have the privilege of education and experience and this, but all of us have more and more access to knowledge, not just a website knowledge, but rather a learning, evolution knowledge that will get to know more about us and about the questions that we ask and the level that we understand. And that is going to help us all as society to be much more fine-tuned about what we expect from care.
Nic (48:19)
Mm-hmm.
I agree.
Jose Carlos Gutierrez Ramos (48:41)
⁓
Nic (48:41)
Mm-hmm. I totally agree. So, JC, this has been fascinating. I could go on for another five hours because I love hearing from you. I love learning from you. But we started with kind of what motivates you. And throughout your career, you've accomplished so much and you're still doing so much. And so if we zoom out for a second, just to finish off with the zoom out question and think about a point in time in the future where you're ready to reflect and look back at all the work that you've done.
currently and even things that you anticipate, things that you're planning on doing in the near future. ⁓ What would be a best case scenario where you could look back and feel like you achieved the fullest impact that you were capable of? What changes in the industry or improvements in human health would make you the proudest and feel like you achieved that deepest sense of fulfillment?
Jose Carlos Gutierrez Ramos (49:30)
You know, the bar is changing for all of us, right? I used to think, okay, you know what? ⁓ If at the end of my career, when I moved from academia to industry, I if I participate or help develop one drug, that would be good. And that would be enough, no? And certainly I've been lucky that over the years we developed four drugs that are in the market and are helping, you know, hundreds of thousands of patients. And then you think, well, of course, and that's super important.
But now in this new world, which you can imagine all the things that we have been discussing, what would it be? I think that I go back to one topic that we discussed. I think that we will change fundamentally the attrition rate, mainly the failure of drug candidates. I would enable that more personal, less toxicity, more efficacy would be enabling this, we call experimental medicine. ⁓
or translational medicine, call it at times, experimental medicine sounds tricky, I don't mean it ethically, but really say, for your disease, for my disease, what is the best drug? And with all the safety ⁓ coverage that one can have to test it in me, and for that, that requires a much faster...
and precise ability to generate these clinical probes or candidate drugs. And that I am certain this would make the continuum of drug development make a quantum leap. And that could be mainly physical, but eventually it would be also computational. Because, you know, the more you measure in a human after giving a drug, the more you can model it, et cetera. So I think that to enable
to enable that truly personalized, to be part of the enablement, through enabling a personalized medicine, both from generating the drug and testing it quickly, ⁓ will be my dream, I hope. I mean, it's getting, it's being tight for me because I'm aging, but I have another five or six years to give it a shot, so I will try to help. Thank you, Nick. This has been a pleasure from my side. Thank you.
Nic (51:53)
⁓ it's been wonderful for me. the pace that science and technology is moving five years is an eternity. So I hope to have many more of these conversations, maybe working together someday to try to solve this problem, because it's near and dear to my heart as well. But JC, it's been a privilege knowing you all these years and an honor getting to know you and having you as a friend. Thank you for being on the show, and I look forward to many more conversations.
Jose Carlos Gutierrez Ramos (52:15)
Thank you so much, Nick. Thank you. luck. Cha chao. Bye bye.
