Hello and welcome to our first deep dive. This is a new series to provide a new level of insights into our pillars. We will describe how they contribute to our 2028 ambitions. Today, we will focus on our QIAGEN Digital Insights business. We want you to understand why the knowledge from QDI is so valuable and how it sets us apart in the market. We will feature business remarks from our senior management, along with contributions from experts and customers. You will also have the chance to participate in the live Q&A by the Q&A box in the webcast portal. You know that every investor event requires a safe harbor statement since we're going to discuss forward-looking statements, but no worries, I'm not going to read it to you. In short, actual results may differ materially from those projected in any statements that we make.
The factors that could cause our actual results to differ materially are discussed in our most recent Form 20-F on file with the U.S. Securities and Exchange Commission, and a copy is also available on our website. The first speaker will be our CEO, Thierry Bernard. Thierry will tell us why knowledge is crucial for the QDI business today and for our ambitions in 2028.
Thank you, Domenica, and from me as well, from Boston. Welcome to those new QIAGEN Investor Relations Deep Dive Session. Domenica explained a bit why do we want to do those deep dive sessions. If you remember, some years ago, we decided first to simplify the way we report our results. Our objective, because we understand that understanding the complexity, the challenges, the opportunities, and also the differentiation of QIAGEN portfolio of solutions in molecular biology can be a very complex task. And then we want to organize those quick sessions where you have the opportunity to do the deep dive in some specific activities of our portfolio, not only with QIAGENers, but with also some of our key customers or some key opinion leaders. It's not going to be a one-shot session. Get used to it. Next year, in 2025, we will have other deep dive sessions.
For example, understanding the positioning of QIAGEN in sample technology and the way we are developing new solutions and new instrumentation. Syndromic testing with our solution, QIAstat, and why are we taking market shares. QuantiFERON, who continues to deliver $100 million revenues quarter- after- quarter and proving its efficiency against skin tests, for example. Or the power of digital PCR, where QIAGEN literally democratizes access to this new technology for hundreds and thousands of customers all over the world. We do that because we want to give you confidence that we are laser-focusing on achieving the target that we gave you in our last QIAGEN Capital Market Day in June of 2024. Please allow me to remind you of those targets. It's very simple. It comes down to three numbers: 7, 31, 2. 7% target of the top line that will outpace the market growth.
31% of EBIT margin to be delivered by 2028 or before that. $2 billion of revenues achieved with our pillars of growth out of a total objective of $2.6 billion, and in addition to that, we added one target, the one to return to you, our investors, $1 billion of dollars, at least $1 billion, absent of significant M&A activities. In 2024, we are celebrating 40 years of QIAGEN. Now, I'm sure that you all know that QIAGEN is a market leader in sample tech, that QIAGEN is very active in life science or clinical diagnostic. But do you know, do you know that for the last 10 years, QIAGEN has been building an incredible leadership position in bioinformatics? Do you know that QIAGEN is selling software? Do you know that QIAGEN is a leader in bioinformatics with more than $100 million revenues?
Why did we decide, either organically or with merger and acquisition of smaller companies, to build that position in bioinformatics? It's very simple. Genomic analysis, informatics, the complexity of oncology, tumor mutation have been generating over the last 10 years incredible amounts of data, trillions of data every year. But accumulating data is not the most important. What counts is making sense of those data, allowing a clinician, for example, to understand what those data mean, and especially for their patients. In short, this is what we are doing with QIAGEN Digital Insights. It's the power of knowledge. It is making sense of complex genomic data with the power of software. But we do this quite specifically. First of all, because Digital Insights at QIAGEN doesn't mean only artificial intelligence.
For more than 10 years now, we have thousands of PhDs all over the world that are every day curating data from all over the world to enrich what we call a knowledge base. And then now we are also investing into artificial intelligence to automate, to boost the impact of that knowledge base, and to always provide our customers, either in research or in clinical, with faster and more accurate knowledge. And this is the power of bioinformatics. It's a growing market, at least $1 billion as of today, where QIAGEN is already a leader. And guess what? Not only a leader, but also a profitable leader. When most, if not all of our competitors are literally bleeding money in this activity, bioinformatics at QIAGEN is accretive to our gross margin, to our EBIT margin, and to our EPS.
With the investment that we are going to continue with the value that we are bringing to the market, and you will see that with our experts and some customer testimonies, we are confident of doubling our performance in QIAGEN Digital Insights for 2028 and achieve $200 million revenues. You will hear from Nitin Sood, our Vice President for Life Sciences. You will hear from Dominic John, our responsible for QDI, but perhaps even more importantly, key customers, key opinion leaders. Welcome again, and join me. Let's take a deep dive into QIAGEN Digital Insights.
Thank you, Thierry. We're all excited about this new format. Deep understanding comes from experience. Reflecting on my own time in the lab, after a while, just by looking at the cells I was working on, I knew exactly what they needed. Like an experienced pediatrician who can differentiate just by looking at the child, a normal fever from a severe fever. There are two types of knowledge: proven facts and knowledge that comes with experience. QDI is both. The knowledge base that QIAGEN has developed over more than 20 years has been curated by experts, by people that dive deeper and deeper, and that's our Sample to Insight.
The sheer volume of biological data available today is overwhelming, but we have the tools to harness this information.
When you get a large set of data, it becomes very overwhelming. You can try to look at it, but we don't have that computing capacity really as humans, and this is where we're making use of some of QIAGEN's products.
What I wanted to do was understand how the cells worked inside the complicated molecular pathways, which may identify vulnerabilities in an individual person's cancer. There's QCI Insight looking at mutations in tumors. There's Ingenuity Pathway Analysis, or IPA, which we also use looking at gene expression mainly. We can understand where the regulation of those molecules has gone wrong to allow a cancer to grow out of control.
We can identify a number of therapeutic targets, which then can be used to rationally design drugs to prevent disease from progressing.
By giving meaning to complex data patterns, we turn vast amounts of information into powerful knowledge, driving progress and shaping the future of science and healthcare.
Without this type of software, we couldn't gain as full an understanding as we do.
Powerful analytics to understand genomics. Bioinformatics by QIAGEN.
Knowledge is power for clinicians and researchers around the world. Let's have a quick look at what our customers say about two of our QDI products, IPA and OmicSoft. Two keywords that we hear all the time are speed and reliability. What they also highlight is the great foundation of information that fuels their research. QDI helps us with the discovery of new drugs and precision medicine. It helps our customers to shape the future of science and healthcare. But how did we get there? I had the chance to talk to one of our QDI experts, Ben Turner. He is passionate about his work and wants you to understand why his team's work is so valuable. So we thought it would be a great idea to take you on a journey that would allow everyone to understand what QDI really is about. Here we go. Hi, Ben.
Thanks for joining. We appreciate your time today to understand our QIAGEN Digital Insights portfolio. Why is bioinformatics so essential for science?
That's a great question, Domenica. So let me make a simple analogy. So we're in a library now with thousands, if not millions, of books. Imagine DNA has an alphabet.
A's, T's, C's, and G's.
Exactly. These four letters are like instructions for something we call genes. These genes create a map within us that makes us sort of unique. This map is what we call the genome. Genes are like pages, and genomes are like books. Bioinformatics is like the librarian. They help locate the book.
But I guess not every librarian does a good job.
You're right about that. You can imagine the best librarians will help you find the book you're looking for, but maybe they've also read the book. So they can provide deeper insights. They can help interpret the book even. Maybe they could even suggest similar books if you like it. If you look at it and think of the librarian now, the great librarians can really help you understand what the book is all about. And within the book, this is something we call content. And content is actually the most important thing.
How about the content of DNA?
Bioinformatics helps us understand the content of DNA and these genomes, as we spoke about before.
Now let's dig into how bioinformatics is helping our customers.
QIAGEN helps its customers unlock insights from DNA and RNA, the building blocks of life. This is what we call Sample to Insight. Our bioinformatics is essential to help customers understand what's coming out from their next-generation sequencing data. This is a technology used to analyze tens, hundreds, or even thousands of genes in those genomes we spoke about before. And it generates absolutely massive files. And this is nothing that you can just analyze manually. So the steps from starting with the biological sample to getting a data file is the wet lab. Making sense of the data file with bioinformatics, that's the dry lab. In the wet lab, we first prepare the samples to get the DNA. This is called extraction. Then you need to prepare the sample to go into the sequencer.
The goal here is to make lots of copies of the target material and cut the DNA or RNA into millions and millions of tiny pieces that we call fragments. So the next step is we transfer the sample to the sequencer, and then we generate the result.
So now we're ready for the dry lab?
Yes, we are. And this is where the QDI software helps us with the analysis and interpretation of the data.
So the really interesting part for you at QDI starts now.
Absolutely. Because without analyzed and interpreted data, it's completely meaningless, right?
What's next?
What's next is we have to make sense of these thousands and millions of small fragments that were generated in the wet lab. This is sort of a chaos, you could say. This is where bioinformatics comes in.
We need bioinformatics to help us understand the chaos.
It's these tools that help our customers generate really valuable insights from the data, so it could be breakthrough discoveries. It could be new therapies. It could be helping improve patient care going forward, so one of the most important things you can do is to be able to identify the differences between genomes, because this might lead to understanding disease susceptibility or if a person is tall or short, has brown eyes or blue eyes. You kind of get the point.
Yeah. What's next?
The first step is what we call secondary analysis. In technical terms, what we're looking for is variations between a reference genome, in this case, and your genome, or let's say a sick genome and a healthy genome. To do that with the QIAGEN Digital Insights portfolio, we have one product called the QIAGEN CLC Platform. This software allows you to do exactly that. It allows you to match the fragments that we got from the next-generation sequencing to your reference and then identify all of the variations.
Also with that, then we could see if a variant is helpful or harmful, right?
We're almost there. Yep, so now we're at the point where we understand that there are a lot of these variations, but we don't quite understand exactly what they mean yet, and that's what we call interpretation, and that's really the next step.
That's what we call tertiary analysis.
In plain English, we call it interpretation, because from the secondary analysis, now we have basically a big list of variants. Now we want to understand what do they actually mean.
Do you have an example for that?
Yeah, I think a good example could be leukemia, which is a blood cancer. A physician would want to be interested in the genetic profile of the cancer cells themselves. The reason that they would be interested in that is to understand, is this patient going to respond or be resistant to a specific treatment, for example? What would the prognosis be? Perhaps are there clinical trials that are recruiting for patients with this type of profile?
Which QDI products do we have for that?
So remember the librarians that we had before? Our librarians are people actually across the world that are reading the scientific literature or the books. And what they're doing is they're verifying, they're structuring this information into a huge database called the QIAGEN Knowledge Base. And this knowledge base can then be used to drive analytics going forward. The products, as an example, within our clinical testing solutions would be the QCI Interpret and QCI Precision Insights.
I'm wondering, and also many other people are wondering, how does that compete with AI-only solutions?
Actually, AI-only solutions can't compete with the quality and the scale that we're providing that's powering our clinical interpretation platforms and helping our customers generate their complex biological insights. And we do use AI in our processes and in some of our products, absolutely. But it's not where the value actually is. So what sets us apart is our use of AI and expert staff to be able to generate these sorts of insights and sort of information at high quality and at a scale that is unrivaled out in the market today, actually.
So QDI is so valuable because of the combination of AI and human curation.
Exactly.
To sum it up, what do our customers get as a result from using our QDI bioinformatics?
In some cases, the customers could get a report that could help a clinician guide patient treatment, for example, and most importantly, they get insights.
Take me back to the leukemia patient example that we had in the beginning.
Okay. In that particular example, I think the end result would be a report, and in that report, what we're going to see are the clinically relevant variations, for example, also referred to as mutations. We'll see recommendations on treatments, maybe prognosis, and perhaps even trials that might be appropriate for this particular individual. Not all leukemias are the same, and this pushes us in the direction of what we call precision medicine, where we actually take into consideration the uniqueness of the individual's cancer type, and we are able to identify a therapy just for that particular individual.
What we're providing basically is highly relevant insights at the touch of a keyboard.
Remember, this knowledge is coming from the activities of hundreds of experts around the globe that are contributing to the high-quality knowledge base.
Knowledge is what truly unlocks the power of sequencing and creates the value.
Absolutely. And then we've come from sample to insight. And the insights from our software that are generated from our software is what makes improvements in life possible. And this is part of the vision of QIAGEN.
It's all about unlocking the power of knowledge.
Now we're back at the library, so imagine you're a librarian and you happen to give out the wrong book. It's not that big of a deal, is it? But imagine now we give out the wrong answer.
This would be devastating.
Yeah, it would be a great impact. Now imagine if you're the trusted librarian and you always get the right book. That's QDI.
Would you agree QDI is both proven facts and knowledge that comes from experience?
Absolutely. That's definitely QDI. And that's why we're the leader in the field.
Thank you so much, Ben. QDI is both proven facts and knowledge that comes from experience, and it addresses both researchers developing new drugs and the clinical field with precision medicine. In other words, a huge market, and here too, knowledge is power. Nitin Sood, Senior Vice President and Head of the Life Science Business, is taking us deeper into the QDI business, and he will also tell us why it is so attractive. QDI harnesses the power of AI, and Nitin will tell us how it enhances our portfolio to achieve our 2028 growth ambitions.
Thank you, Domenica. QIAGEN Digital Insights is our portfolio of software and bioinformatics products. These products are driving the future of drug discovery and precision diagnostics, a future driven by data and insights. Our products help clinicians and researchers transform data into actionable insights. Now let's start by talking about the challenges scientists face when it comes to data. In the past decade, biological data has exploded. It's massive, complex, and multidimensional. This data is brimming with potential insights that can drive innovation in drug discovery and precision diagnostics. But here's the reality. Data alone doesn't drive breakthroughs. It's the insights hidden within this data that drive innovation. And that's where we come in. We provide tools for visualization of the data, for curation of the data, for organizing this data into structured knowledge bases, for analyzing this data, and finally, for transforming this data into actionable insights.
Now let's talk about the market opportunity. Bioinformatics has grown from its niche roots to be a powerful catalyst for innovation. Growing at 10% annually, it will hit $1 billion by 2028. Bioinformatics cuts drug discovery timelines by 30%, saving R&D costs. And it accelerates precision diagnostics tests by 90%, ensuring timely and accurate care. It's a great time to be a leader in this market. Converting samples to insights is QIAGEN's core mission. As you saw earlier from Ben and Domenica, we cover the entire workflow. We start with the wet lab with the best sample technology, followed by platform-agnostic NGS library prep, finally converting data into insights. This final step is where QDI comes in, converting data into actionable insights. Now let's zoom in further into the QDI business. We are the number one bioinformatics providers.
You'll find our solutions all over the world, from the top academic research centers to the top 25 pharma companies to the top diagnostic labs in the world. Whether you're a scientist trying to understand the fundamental nature of disease to discover better drugs, or you're a clinician using innovative diagnostic tests like liquid biopsy for precision medicine, we cover both the markets, research and the clinical market. 60% of our revenue comes from the research market, serving customers both in academia, in biotech, and pharma. 40% of our revenue comes from the clinical market, covering the world's best hospitals and the top diagnostic labs in the world. By 2028, we're targeting $200 million in revenue, growing at 15% annually. We're leaders in this market. Let me show you why. It's our data.
Our data is our superpower, built over three decades of rigorous curation and validation by a team of 150 scientists. And it's massive, i t contains 643 million biomedical relationships and 26 million findings. More importantly, it's accurate, significantly more accurate than AI-only data sets. By combining AI tools with expert human curation, it sets us apart, allowing us to create a knowledge base like no other. Over the last year alone, our team of 150 scientists has reviewed 950,000 variants, ensuring our data and knowledge bases are up to date and accurate. Now let's dive into how customers are using our products in real life. In drug discovery and research, our tools help scientists understand the underlying basis of disease and help them identify novel drug targets. In the clinic, our tools help physicians identify precise therapeutics that match to an individual patient's genetic profile.
Now let's dive into two customer examples. First, let's start with an example of how QIAGEN is helping drug discovery scientists. Imagine you're a scientist at a pharma company studying cardiac damage. Cardiac damage is complex, involving multiple biological pathways, processes, and tissues. In order to solve cardiac damage, you need to know where to intervene and how to prevent further damage. You need a comprehensive view of the disease, and this is where we come in. Our tools provide a full view of the underlying disease, like cardiac damage. They help identify all the biological pathways and processes and all the tissues impacted by these pathways. This comprehensive mapping of all the biological processes is based on 30 years of curated data, and this guides researchers towards more effective experiments and helps them develop better drugs faster.
Second, let me now walk you through a powerful example of how QIAGEN's tools are helping oncologists. Let's say you're a patient that's been diagnosed with lung cancer. Oncologists really need to understand the molecular basis of the lung cancer for that individual patient so that they can match them with the right therapy. A class of therapies called targeted therapies that are more effective and less toxic. This is where QIAGEN's tools excel. Our clinical solutions provide a comprehensive patient-specific report. For example, in this report, the patient has tumor mutation burden and microsatellite instability. Both are very high for this patient. These are commonly occurring mutations in lung cancer, and based on these mutations, our software recommends a particular type of targeted therapy called immunotherapy. To ensure confidence, the report also includes science-based backup evidence for that recommendation.
It also includes information that matches the patient's tumor profile to eligibility in a clinical trial. From this, you can see how QIAGEN helps turn raw data into actionable insights and turns complexity into clarity. Now let's hear from one of our customers in the clinical space, the CEO of Natera.
Yeah, Natera is at the forefront of carrier screening. We use advanced next-generation sequencing technology to deliver highly accurate and comprehensive insights for reproductive health. We process hundreds of thousands of carrier screening tests per year, making us one of the largest companies in the industry, and we chose QIAGEN because they allow for seamless integration into our existing workflow and because of their large bibliography of curated evidence, enabling us to maintain both speed and precision. QIAGEN's curated content helps us to ensure we can deliver reports in a timely manner with the highest confidence for our customers. QIAGEN's software helps us to handle scale by reporting and automating large parts of the classification reporting process. This ensures we meet demand without sacrificing quality or increasing turnaround time.
QIAGEN's standardized interface is one of the tools that allows us to pull classifications and variant content directly into our workflow, creating seamless and efficient processes. It reduces manual steps and ensures consistency across our reports. Yeah, by automating a significant portion of the curation process, QIAGEN has helped us avoid the need to hire a significant number of additional full-time employees to handle the workload. QIAGEN's software allows us to scale efficiently without compromising quality.
Another more recent and growing use of our tools is AI and data science. AI is powering drug discovery today. But AI without high-quality data is like a rocket with no fuel. Our comprehensive data set and knowledge bases that we've accumulated over 30 years is perfect for powering large language models and machine learning models that are in use in drug discovery today. Looking forward, we're accelerating our product development and global presence. By 2028, we're increasing our sales force, especially those focused on SaaS or our subscription model. We're investing heavily in AI and are planning to launch 14 AI-enabled applications. We're continuously increasing the content in our database. For example, by 2028, we will have information about 12 million genomic variants in our database. These initiatives will maintain our leadership position in the market and we'll be the partner of choice for our customers.
In 2025, specifically, our clinical business is going to do really well and is expected to grow 20%. For our discovery business, we're going to launch three AI-enabled applications. And our focus is on transitioning our customers from the old licensing business model to the new software as a service or a subscription-based business model. For over 30 years, QDI has delivered consistent value. We have 4,000 customers. And in those customers, we have 90,000 active users. And our customers are found all over the world. Our solutions are in place in 60 different countries. And our expertise is trusted. Our products are cited in 80,000 publications. By leveraging economies of scale and by selling solutions with other QIAGEN products, we are ensuring profitable growth and delivering exceptional shareholder value. As we look to 2028, our ambitions are clear: to drive innovation and to expand our reach.
With unmatched data and cutting-edge tools and our commitment to science and medicine, we at QIAGEN, in partnership with our customers, are unlocking the power of biological insights. Thank you for being on this journey with us.
Thank you, Nitin. So let's understand what we're really talking about here. Humans, treating illness, finding new cures, enabling scientists to do research, understanding the basis of life better and better because we're still only at the very beginning. The more we know, the better curated our knowledge is, the more we can help find answers. What type of cancer do we face, for instance? How does it need to be treated? And what is the right therapy? And those are only applications that involve patients. There are thousands more applications in research and science that depend on accurate knowledge. We know from our customers how important our bioinformatics is for their work. For example, defining the exact diagnosis is crucial for any next step in patient care. Let's hear from one of our clinical customers.
I'm Dr. Lauren Lawrence. I'm a laboratory and medical director at Guardant Health and the service director for our tissue NGS product. Guardant Health's mission is to provide physicians and their patients, specifically cancer patients, with information that can help them select treatments that their cancer is likely to respond to, and QIAGEN, N-of-One, is a really important partner in translating that reduced amount of information into very specific predictions about what the patient is most likely to respond to, and they are really an incredibly important partner in staying on top of the mountains of preclinical and clinical data that are coming out every day in the literature, refining that information in an iterative fashion so that our patients and physicians have the most up-to-date data that they can use and rely on.
If we didn't have access to QCI Precision Insights, and N-of-One, a lot of that labor would shift to myself and my colleagues. We would probably have to build an internal team to reproduce what services are provided through N-of-One. So the ability to have really precise information provided to our patients about the features of their particular cancer that we're able to identify molecularly and a really high-quality description of the likelihood of that patient with that cancer with that profile to respond to a particular therapy is incredibly impactful.
The development of new drugs is equally dependent on accurate knowledge. Here, too, only with knowledge is it possible to find solutions. Let's listen to more testimonials.
I am Iulian Pruteanu. I am a Senior Director of Bioinformatics within Flagship Pioneering. Having access to published knowledge, having access to those interactions between genes, having access to the data that you guys curated and put together is critical for us here. We want to make sure that we use everything that is publicly available already. And so this is exactly what your platform is doing, right? It's offering us quick access to public knowledge and also quick access to data, RNA sequencing, whether it's bulk or single cell, that would be critical for us. So we could immediately go and validate, test those questions that we have with the data that you guys brought in-house. Your tools and your data sets are just highly valuable for us. Flagship may have an idea. Okay, we see we can do, we would like to do this, this, and this.
For us, the role is, can we validate that question? Is there any data that brings some sort of ground truth to the question that I have in mind? And this is where OmicSoft comes in right away. We used to do this work manually, find the papers, build pipelines, internalize the bulk RNA-seq data, get some findings from it. That was one way. Now, with your help, we can just look across studies, across publications, across targets. And just, it's a time saver. Manual curation is actually highly valuable to us because the alternative to that would be, let's say, a large language model reading all the papers at once and just building some sort of a knowledge graph that has no meaning. That knowledge graph is just frequency of words that came out in publications, right? That's one way of doing it.
What you guys have done and what we find valuable within IPA is the manual curation by an expert. All of a sudden, when we care about what is upstream or downstream of a specific target, actually those things make sense. Make sense from the biological perspective, not just because they show up in 10 papers 5x . Therefore, there is a frequency of 50% or what have you. IPA and OmicSoft are a no-brainer because there is no competitor, first to start with, because they really bring that validation factor. What you have been building here, I think it's to us, that's the key, right? That's how we really bring that validation factor on board. So from where I stand, from my perspective, there is no other solution that does this.
Yes, so my name is Alexander. I'm based in Munich, working for Neo4j. My background is bioinformatics and a PhD in biochemistry. Neo4j is trying to make sense out of the data using the connections in between the data points. Customers across the industries are using that to first combine the data and then put analytics on top of that. One prominent example here is the Panama Papers, where they connected the data. And by connecting the data, they could see some unusual patterns. QIAGEN provides an extremely unique data set of manually curated data, not only from the past, I don't know, one or two years, but for more than 20 years. So there's a variety of data in there that is not only curated, but normalized and standardized, meaning that you're out of the box.
You can straight away start your analysis and work with the data instead of wasting your time of integrating data, of connecting data and making sense out of that. The manual curation at the one side, second, the standardization and harmonization of the data. I see many customers fiddling around with data sets, and they waste a lot of money and a lot of time to bring them together, to normalize it, to standardize it, right? And that's why BKB is the perfect data solution for ready to start in the next five minutes. GenAI is promising, right? But in the end, we have to see an AI can only be as good as the training data.
So this is where I see from the training perspective and from the backend perspective, we have to provide high-quality data to the machine learning models in order to provide a solution that can predict new things. So this is where I see BKB as one of the crucial points: high-quality data for training very good prediction models.
Very reassuring statements from our customers, and with that, we come to the last point in the agenda, the Q&A. If you haven't done so, please type in the questions into the Q&A box in the webcast portal, and we're happy to take your questions. Today for the Q&A, I have John Gilardi with me. Hi, John.
Hi, Domenica. Thanks for putting together such a great event today with the team, so I think my question to you, Domenica, is what do you take away from this program after watching it? What are the key learnings for you?
For me, two things stick out. Number one is, and you heard it a couple of times already, knowledge is power, and I can confirm that. And the second thing is I did lots of sequencing and bioinformatics during my studies. However, I didn't have access to software and databases the way we heard it today. And I wish I would have had access, not only because this speeds up lots of things, but also to make sense of the data in a more informed way.
No, I think what I take away after looking at how we put this business together over the last decade is how QIAGEN really offers the breadth and depth of what customers really need for bioinformatics. And we're at the forefront. And that gives me confidence in getting to the $200 million goal in terms of sales for 2028.
Perfect. Yeah, thanks. So now let's dive deep into the Q&A. Today we have live from Boston, Thierry Bernard. Hello, Thierry.
Hi there. Happy to be with you.
We have live from Redwood City, Nitin Sood and Dominic John.
Hi everyone.
Nice to meet you.
So let's start with the first one, John.
The first question came in on the Q&A, and it's what barriers exist out there for competitors to try and replicate what we offer in research and also in clinical healthcare? Thierry, do you want to parse out the question?
Thank you, John. And I'm going to ask Nitin also to leverage my first answer. But the first thing I would say, John, is we are not doing bioinformatics for the last year or two years. It's a complete buildup of a very comprehensive knowledge base. I think Nitin alluded to that in his presentation today. For more than 20 years, where we have PhDs all over the world curating data and enriching that knowledge base, multiple publications, citations, clinical trial results. And more recently, we obviously boost that knowledge base with the power of AI. And so you do not replicate that knowledge like this in one day. So I would see this as one of the main barriers to entry. But Nitin, you might want to complete that answer.
Yeah, I mean, I think I just want to build on that. And you sort of heard that in our presentation earlier. It's our data. It's highly curated. It's accurate. It's comprehensive. And it's up to date because of that team of 150 scientists that we have that are using AI-powered tools to keep this data up to date. And on the clinical side, our differentiation also comes from the fact that we offer the complete solution. We go from sort of QC and variant sort of alignment to variant calling to variant interpretation. Some folks only offer one piece of the solution. The others offer the other piece of the solution. We offer everything on the clinical side. And you can see this in the output. We have 90,000 active users, and we're really trusted. We have 80,000 publications out there.
Thanks. So the next question is from Hugo Solvet. How would you expect the 60/40 research-clinical split to evolve over time? And could you discuss the benefits and limitations of being an independent diagnostics player versus pharma companies having integrated bioinformatics business?
I can take the second part. Hugo, thanks a lot. I mean, I think for the pharma, for our partners, the pharma partners, the point is not whether we are independent or not. The point is in our expertise. It's in our expertise, not only in that incredible level of data that we have accumulated, as Nitin again repeated. It's a fact also that we have a significant expertise in putting a complete solution through regulatory approval. But that's how I would address that second part, Hugo. And for the first part, the split clinical and research, please, Nitin, give an insight on our evolution of our portfolio here.
Yeah, I think our clinical business is obviously growing very robustly as our underlying customers, a couple of them you heard from today, Guardant and Natera themselves are growing. So that's driving the growth there. And our investment in the clinical business in terms of R&D is going to be focused on expanding beyond genomics into multi-omics and bringing more data in, as well as expanding beyond sort of oncology and germline mutation into other disease areas. And then on the discovery side, our growth is going to be really fueled by data science and AI. And that's exactly where our R&D efforts are. You heard again there in the presentation from one of our customers how our biomedical knowledge base is powering large language models and other foundation models that are used in a wide variety of our customers, including many, many of the top pharma companies out there.
Good. We have about 100 people watching online, so we have a really good participation rate today. So the next question comes from Dan Arias at Stifel. He's saying that QDI sales were down low single digits in Q3, and it reflects the market transitioning to a SaaS or a subscription-based model. What does this mean for growth acceleration next year in 2025 en route to your 2028 revenue target?
Yeah, it's a fair question, and we will obviously see when we would publish our full 2024 results, the actual performance, but what is important to highlight? One is that we believe in this market. We believe that we bring an added value to our customers, especially customers specializing into NGS and especially more NGS for oncology patients. We believe that it's a growing market. Nitin also alluded to that. We are close to reach 1 billion market, so there is a lot of space to grow. We have disclosed in full transparency that our business model was evolving progressively towards a more SaaS- business. After all, we are selling software in bioinformatics. And therefore, in the revenue recognition, nothing changes as regards to the number of customers that we have. This is still growing, or the amount that we are achieving with those customers, it's the revenue recognition.
It's the way we recognize those revenues. And as we have said many times, we believe that this is going to normalize in the coming months, and we would be in a more normal situation in the second half of 2025 to go back to a double-digit growth. And that's our objective. And this is why we are repeating today that we are very confident in achieving our target that we set in our Capital Market Day for QDI, which is $200 million by 2028.
Thank you, Thierry. The next question comes from Levine from Redburn Atlantic. As you said, input data are very important to make proper conclusions. What kind of data validation methods do you have to make sure of the veracity of the data handled? How about data auditing and review periodically?
Nitin, why don't you take this one?
Yes. You know, we have, as I said in our presentation, we have a very dedicated team of individuals. These are highly trained individuals. It's a mixture of scientists and clinicians that continuously review the data. And within this team, we have a very systematic process by which we go through multiple layers of independent teams validating the data before this data is made available in our knowledge bases. And Dominic, I don't know anything else you want to add to that?
Yeah, I've had a couple of points to that. I mean, first, from the IVD perspective, to be certified in Europe, we've gone through some very rigorous auditing and processes to prove that our quality and the processes are adherent to delivering high-quality content. Secondly, we have to bear in mind, I mean, it was alluded to earlier, the content's been in market 20 years. We've been building relationships in our content. We've had over 90,000 customers looking at that, and they provide us continuous feedback. So that's consistent between the process and this in-market feedback. We get the quality.
Good. Thank you very much, Dominic. I think the next question is back to the question of barriers we got from somebody online. What do you see as the barriers to faster growth? What's keeping this business from growing double the rate that we're seeing right now?
I believe that we can be the three of us taking this question. It's a constant obsession for QIAGEN. How are we fueling the growth? As we said, we believe in this market. You might remember that two years ago, we disclosed to the market that we were believing in that market so much that we were looking for a private equity investment. We had exceptional discussions with different partners. We have a very high level of interest, but we also said that we didn't want to forgo the majority of that business, owning the majority of QDI. And so we couldn't reach an agreement with some of those interests that were very high. Again, it allowed us to deep dive into our activity and even better understand the added value we were bringing to our customers. And so we decided to invest organically.
It's a program that we disclosed to you all last year. It's code name inside QIAGEN, Golden Gate, and it covers different dimensions, adding more feet on the ground. It's fair to say at the moment that this business is mainly a North American and European-based business. I think we have potential to grow faster in some areas like the Middle East, like Asia-Pacific, Latin America, but we need to put people in the field for this. Second, you know that by regulation, a lot of our, I would say, country customers are demanding that we localize servers. It's an investment. We do that in Saudi, for example. We had to do this in Japan, in China, Turkey, but it's an investment. We need to go through that, and we accept to do this investment. Last but not least, obviously, we need to constantly upgrade our different software.
I mean, technology moves fast. Data, as Dominic and Nitin confirmed, are also growing fast. So we need to constantly upgrade this offer. And last but not least, it's no mystery that M&A is one of our also preferred capital allocations for QDI specifically, companies that are complementary to what we offer. But Nitin, you might want to add something to that.
Yeah, I mean, I think the only thing I'll add to that is we're committing to spend 20% on R&D and again, to continuously upgrade our portfolio, both on the discovery side and on the clinical side to drive growth, and I'll give you an example on the discovery side, which sort of expands the user base, so we are going to use AI to expand the usability and the collaboration ability of our discovery software to expand it to biologists who are less trained on bioinformatics. As you know, we provide a lot of complex information. Our data is very comprehensive, and when we present that, bioinformatics expert users are able to use it, but we want to expand it to sort of the biologists who have less training in bioinformatics, and that's where we are using AI to really make the tool much simpler to use.
That's an example of us trying to accelerate the growth.
Thank you. So the next question comes from Aisyah Noor, Morgan Stanley. What are the growth and profitability implications of a shift from licensing to SaaS in the genomics business?
We have an Aisyah. It's a good question. It's not impacting our growth. It's impacting momentarily in that transition phase because it's a different way of recognizing the revenues. But as I said before, we expect this to normalize by the second half of 2025, and then we will show you again get back to a double-digit growth, which is our target that we disclosed in our Capital Markets Day. Profitability is not impacted, as we have repeated today. We are probably the only company who has been able to make this activity a profitable one, and QDI, bioinformatics, is accretive to our gross margin, accretive to our EBIT margin, and accretive to our EPS.
Good. Thank you, Thierry. We have a good question from Casey Woodring at JP Morgan. He's asking, what is the cross-selling opportunity between your sample prep business and other areas of your wet lab portfolio and QDI in the dry lab?
Oh, that's an excellent question and something that we should continue to improve. Nitin, why don't you give some examples here?
Yeah, I mean, I think on the clinical side, it's very clear. We are the leading sample tech company in the world, and one of the big areas for sample tech is liquid biopsy, for example. So we obviously provide sample tech for liquid biopsy, but then we also have QDI solutions for oncology diagnostic labs that are sort of pushing the path forward with liquid biopsy, so that's a great example. And likewise, a lot of our pharma customers are using RNA extraction technology from QIAGEN, as well as our QDI solutions, so there's a lot of cross-selling, upselling opportunities for us, both on the clinical side and on the discovery side, and I'd also like to point out that we're platform agnostic, completely platform agnostic, so we work with all the sequencing platforms out there. We work with all the sample extraction technologies out there.
And so where it makes sense, we sell jointly. And where customers just want bioinformatics from us, we provide that as well, again, because of that platform agnostic nature of our business.
Thanks, Nitin. So we have one more from Mike Ryskin, Bank of America. How captive are QDI revenues? Is there significant turnover in client customer base each year? For example, what are your customer retention metrics? And how penetrated is QDI into the potential market today? Is your market share there above, below your sample prep, library prep market positions?
Good question. Dominic, why don't you give us some key data here?
Yeah, certainly. So from our perspective, I mean, if you look at the two portfolios, the discovery portfolio is more mature. We have a lot more users there. That said, we are reaching out to new markets in that discovery portfolio. So data science is a key area. We sell our content to a lot of bioinformaticians. We're also expanding the market reach into biologists. So typically, it's being used by bioinformaticians. So while we've been highly penetrated in those markets, there are adjacent segmented markets that we can go after, are going after with the latest investments. On the clinical side, that is a very interesting market. There is certainly, I think we're on the cusp here. We're seeing investment in starting to decentralize to hospitals. We've got a lot of the core labs in testing labs in the U.S. There's a lot of space left in the clinical.
And it's not just the actual penetration of those accounts. There's also the actual increase in the patients getting testing. So we're sort of benefiting from two aspects. One is the white space out there for new accounts. And two is the number of patients coming into those accounts, offering a lot of white space for future growth there.
Good. Thanks, Dominic.
Thank you. I think John and Dominic, I believe we are coming to the end of the session, if I'm not wrong.
Right. We're out of questions as well, Thierry. So we hand over to you.
Yeah. So guys, yeah, I'd like to wrap it up. First of all, I'd like to thank the team who has been working in this deep dive. I hope that it's a good format. We took the commitment to allow you to understand better our opportunities, our challenges, our differentiation in an hour of your time. We know that your agenda is very busy. But I hope, really hope that you did like the format. And as I said in the introduction, we will have other deep dives during 2025. If we would like to leave you now, I would like on QDI to leave you with four messages, four messages take away with you. First of all, we continue to address a growing market. Nitin said that again, we are getting close to $1 billion market, unlocking insights for next-generation sequencing customers mainly.
The second is that we have a wide portfolio. We address needs from research customers, academia and research, pharma customers for drug development, or clinical customers for patient outcomes. Really wide arrays of needs answered with our different softwares. Third, it's profitable. We said it, profitable to our gross margin, profitable to our EBIT margin, profitable to our EPS. So it's really worth continuing investing into that activity. It's a value-added activity. And last but not least, with the actions that we are putting in place with the dedicated team that we have, we are on our way to achieve our target for QDI 2028, which is 200 million sales of revenues. Thank you very much. Hope it was useful. Thanks, Dominic. Thanks, John, also for coming up with these ideas. And if we don't talk to you before, for those celebrating it, Merry Christmas to all, or Happy Holidays.
Thank you so much. Bye-bye.
Thank you, Thierry.
Hi, everyone.
Thank you, John. Thank you, Dominic. And thank you, Nitin. Thank you, John.
Yeah.
With that, we are at the end of this deep dive event, and see you then in 2025 in the next deep dive event.