Category: Preclinical Research (CRO)

Leading preclinical contract research organization Charles River Laboratories and human data–centric artificial intelligence (AI) technology provider Valo Health recently announced the launch of Logica^TM, a collaborative AI-powered drug discovery solution that leverages the expertise and experience of both partners to rapidly deliver optimized preclinical assets — at both the advanceable lead and candidate stages — to pharma clients. Charles River’s Executive Director of Business Development, Early Discovery Ronald Dorenbos, Ph.D. and Valo Health’s Vice President of Integrated Research Guido Lanza discussed Logica, the inefficiencies in drug development it seeks to overcome, the underlying business model, and what the future holds for the partnership, with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D.

David Alvaro (DA): To start things off, can you tell me about the inception of the partnership between Charles River Laboratories and Valo Health and why both organizations felt that there was a potentially productive synergy between them?

Guido Lanza (GL): I don’t believe that I’ve ever been a part of a partnership where the vision and the framework for achieving it came together as quickly as what occurred between Valo and Charles River. I think that was possible because the idea had already been incubating for a very long time, essentially independently at each company. Charles River had a vision of undergoing a very deep digital transformation that would enable the company to combine, unite, and extract more value from the data that they generate across their operations, which would unlock some significant new opportunities.

There was a complimentary vision on the Valo side. While Valo is a relatively young company, the relevant digital platform for computational drug design was built in part through acquisition of another company called Numerate, of which I had been CEO. We felt that if we could figure out a way to partner with a data-generation powerhouse like Charles River, we could overcome some of those bottlenecks.

Ultimately, setting up our first meeting to discuss what our combined capabilities could offer was the trickiest part. After that, it was a smooth journey to establish the actual model, what the offering would look like, and the benefits to the customer.

Ronald Dorenbos (RD): Over the last 10–15 years, we’ve seen hundreds of millions of dollars poured into the industry, with lots of AI companies trying to perform drug discovery and development using AI alone, which has not been particularly successful. While AI unlocks all kinds of new possibilities, it’s clear that it’s not sufficient on its own for productive drug discovery. This collaboration was designed to advance to the next logical step: Valo brings extensive AI expertise from the chemistry perspective (because a lot of people there are also chemists themselves), which combines with the powerful data generation and experimental engine that Charles River has. Charles River provides an enormous arsenal of capabilities that are unmatched by any other company in the world. By combining the considerable traditional drug discovery capabilities of Charles River with Valo’s AI expertise and technology, we could create a very effective platform that could really advance drug discovery, which we have named Logica.

DA: Can you expand a bit about the conventional drug discovery and development process and where you see the most critical bottlenecks or inefficiencies that inspired the platform and why this combination of traditional discovery and AI is the most sensible way to overcome them?

GL: I’ve been working in the AI space for over 20 years. If you look at the history of the deployment of AI or machine learning (ML) and where it has had an impact, most has occurred within the traditional siloes of the pharma industry: image analytics as a screening platform, virtual molecule design, and so on. The data and the algorithms unlocked a lot of new possibilities, but they operated within the traditional chevrons. As a result, we saw a great opportunity to rethink the whole paradigm by removing the traditional chevrons and focusing on the real moments of kind of value generation.

I would argue that there are three key moments of value. The first is performing some magic biology — omics or the like — and finding a target. The next is a chemistry that allows you to test a hypothesis that is advanceable and patentable. And the third is the moment in which you have a candidate that is ready to enter IND-enabling studies and beyond on the road to the clinic. At every point in between, you can’t really be certain how close you are to that value. So, we wanted to take a step back to focus on defining those value-generation points and assessing where we are underutilizing data that could increase our chances of reaching those points.

AI essentially provides a means of cheating and looking into the future, or at least a good simulation of it. For example, if we have a good model for tox studies, we can simulate the results of those studies much earlier, which reduces or improves the odds of success downstream. If you break down siloes, you can use the data about future success and failure to inform decisions today. AI lets you melt away those chevrons and think about data as something more fluid that supports the reduction of uncertainty, which can allow you to apply totally unrelated data from a different project to guide your decisions.

I can’t imagine a greater data generation platform than Charles River, who supports more than 1,300 IND programs every year. We just needed to figure out how to unlock the value in those data for future programs to increase the chance of success, or at least to help programs to fail fast and early rather than later and at greater cost.

RD: We see Logica as version 3.0 of applying AI and drug discovery. Version 1.0 had a very narrow problem scope, a siloed approach, an inability to extend the analysis beyond the initial problems and no intentional, large-scale data generation. Version 2.0 added a limited amount of data generation, as well as expansion into broader problem categories and some wet lab access. With Logica, we are breaking down those siloes across early drug development, integrating wet lab work with the AI capability, and focusing on cycle numbers and data intentionality to, as Guido was saying, predict a likely future as early as possible.

DA: With Logica, were you looking to tackle all relevant pain points in drug discovery or begin with some low-hanging fruit and then build up to more complicated challenges?

RD: Before we start a project with a pharma or academic partner, we take a good look at the target, because targets come in all kinds of different varieties, from easier ones to approach, like kinases, down to RNA, epigenetic, and more exotic targets. Across more than 25 projects, we have had more than 90% success. Logica has processes and methods that enable it to work on various types of targets, of varying difficulty levels.

While the technology is essentially target agnostic, we always perform a feasibility study at the start of a project to determine which targets we feel comfortable with pursuing, because we don’t want to get involved with a project that our platform and our experience indicates has a very low chance of success. To that end, it helps that Charles River is such a large organization, with around 18,000 people, including many with 15–20 years of experience working at the major pharmaceutical companies, like Pfizer, Novartis, Merck, AstraZeneca, and GSK. That experience helps with the feasibility studies but also in navigating certain challenges and bottlenecks.

GL: Over time, the offering will get better and better: the output quality will improve, and the time will be reduced. At a high level, this all helps better align the CRO model with what the customer wants: they want the best product as fast as possible, we do better when we can make that happen, and doing so improves our platform so that the results are even better in the future.

In traditional drug discovery, you typically start off by running a screen, and then build your set within the universe of compounds that comes from the screen, analogs of those compounds, and so on. What we do is a little bit different; we see three parts to the process. The first is the generation of data to train the model. It’s great if you can understand the chemotypes and get to some starting point, but what you really want is information as the very first path. That whole universe is flattened in our mind because it’s a data-generation universe.

The second step is to unleash that on very large spaces of chemistry that are bespoke for your problem — if the first space is tens of billions of compounds, you want to go even larger on your second space, evaluating hundreds of billions or trillions of compounds specifically designed for your problem. Then, third, you want to pick the series that are most advanceable, because you’ve made millions of virtual analogs of those and simulated your future against models of all the things that can go wrong. Some series are of course intrinsically going to be better than others, so the ability to measure that a priori sets you up for success later. This provides a significant quality advantage because you’ve looked at so much more information about that series than people typically would.

RD: It’s critical to start with the highest quality and value of compounds. Obviously, clinical trials are coming further down the road, and a lot of molecules will eventually fail in trials, but if you can increase the chances of success even by only a tiny amount, that will have tremendous benefits. So, it’s not just a case of better molecules but of an increased success rate further down the road to help get these molecules to the market and the patient.

GL: We work with clients ranging from early seed companies all the way to big pharma, and they have very different drivers: the pipeline, the timing, or the cost. We offer a model that is very transparent and very straightforward: six to nine months for the first phase to get to the advanceable lead — which we call Logica-AL — and then another 12 to 18 months to get to the IND-enabling candidate that is ready to go into GLP tox and safety — which is Logica-C.

The whole trajectory of going from scratch to an IND-enabling molecule takes at least 36 months. Logica can get there within 18 months; if we run into some challenges or need to set up special assays, that may extend to 27 months, which is still significantly faster than the traditional method. And being able to reach one critical conclusion in six to nine months and the second in another 12 to 18 months is very attractive.

DA: As you mentioned before, the more data that you put into an algorithm like this, the more refined and accurate it becomes. To that end, are you able to leverage data from customer projects to feed back into the platform, or do you run your own internal experiments to generate data?

GL: There’s a continuum. Some customer data is pre-competitive, and some is not, so there are some kinds of data that customers are quite willing to share and others that they generally are not. In some cases, we have to generate our own data or import published data.

The questions “Can I use the data to learn from?” and “Can I see how my model did?” are very different. Both Charles River and Valo have a lot of experience handling confidential customer data and building the appropriate firewalls, which helps customers be confident that we will only use their data in approved ways. Of course, many customers see the value of more people sharing data and how that benefits their projects and are very happy to share what isn’t hypersensitive.

DA: Can you explain the business model underlying the Logica platform?

RD: We use a risk-sharing model where the cost is tied to success and the creation of value, which aligns incentives with the customer. Rather than charging on the basis of the number of experiments run or the hours needed, most of the payment is tied to those moments when the customer receives real value. We typically divide things into the two phases we discussed, but everything that is needed to reach the advanceable lead series is included in the milestone payment for that phase. Sometimes people ask us how many FTE hours they get for the price, but that’s not really a relevant question, because it’s as important for us as for the client to reach the milestone — that’s how we get paid and how we advance to the IND-enabling phase. If the client then wants us to pursue optimization, there is a continuation payment, which is typically higher than the payment for the first phase, because this second phase requires more lab work, chemistry, and animal experiments. Then, after we spend another 12–18 months to get to an IND-enabling candidate that is consistent with the target product profile and the specifications that were agreed on at the beginning of the project, there is another milestone payment. Finally, clinical milestone payments and royalties will come into play when the candidate moves through the clinical phases and goes to market. The client’s success is our success, and we keep everything very straightforward and transparent.

DA: What response have you seen from the market? Has it been relatively easy to convince potential customers of the value of this approach?

RD: There is a great hunger for a value-based offering in the small molecule discovery space. At the BIO International in June, I spoke with many people who were really excited about this new approach to drug discovery, and we are in further conversations with many of them. We are in discussions with big pharma companies, venture capital firms, small biotech companies, and seed companies from universities, and, across the board, people are enthusiastic and see Logica as a great model that could fit into their strategy.

I have not heard really anything negative, but we are relatively new and still need to build our track record and our history together to match the very strong individual track records of the two companies.

DA: Assuming that Logica leads to the optimal outcomes you envision and ends up in widespread use, how transformative do you think it could be for the industry as a whole and the ways that drug discovery is conducted?

GL: At the moment, there’s an interesting economic argument to be made for Logica as a small molecule generation engine for various types of things. If we can consistently fix the uncertainty in small molecule discovery, you’re re-empowering the people that are doing the earliest work — if we can level the playing field on chemistry, then biology will become the dominant piece.

Those who can best define human disease and translate those definitions into preclinical models will be the winners of the future, not those who can have the biggest libraries. For example, Parkinson’s disease is currently defined by the FDA and the ICD9 code as a single disease, but in reality it’s probably 50–100 different diseases. Those that can better define targeted subpopulations and develop specific compounds against them will benefit. But before you begin, you can focus on your translational path and your translational journey and establish a patient ID for a compound even before running a screen. If we can define that journey in a frictionless way, we totally change the economics by dramatically increasing the plane of symmetry (POS) and quality of the compound, which empowers the people that are defining the disease as the value-generation hub.

That’s where I think AI is going to make the biggest impact after Logica, because that’s where you have complex, high-volume data reflecting all the omics on a per-patient basis. To me, that’s the really exciting development a little way down the line.

RD: Everything boils down to getting better medications to patients faster. What Logica can unlock is the ability to make the whole process more efficient and more economically attractive and to operate as a well-oiled machine, where you also can consider targets that you normally would not consider because of cost concerns. As Guido often puts it, Logica is “democratizing” the process of drug discovery and the AI capabilities for a much wider audience, and the whole world will benefit from that.

DA: Before we wrap up, is there anything you can share about what might come next for Logica or for the partnership more broadly? Is it possible to build on this success and tackle large molecules?

GL: We are looking at other modalities, although we can’t disclose anything right now. Beyond that, we want to advance the concept to predict ever-more complex phenomena. That intersects with the need to avoid or minimize failures and how they become costlier the later in discovery that they occur. We continue working on determining the best sources to inform our design decisions. Where we really want to push the envelope is in making sure that we’re not modeling intermediate steps that are poor proxies for the ultimate goal but instead finding the best proxies and focusing there.

RD: The expansion of our platform will be tied into what’s happening in the rest of the field. Lots of groups are applying AI and machine learning to particularly complex biological systems: neuroscience, gastrointestinal disease, oncology. These technologies can read all relevant published manuscripts and become much more effective at natural language processing, which will also lead to new insights and new targets, which can be combined with our efforts to use these targets to develop new drugs. I think we will probably see that happening more in the future.

Another potential impact suggested by the insights we’ve gained from the AI models that interact closely with the wet lab work at Charles River is that Logica can also help reduce the wet lab work that needs to happen. We can scale down the numbers of the animals that need to be used for these kinds of studies and some of the assays, even getting rid of some assays altogether, because the AI can predict the result without needing to perform even one experiment. I think that any of these adjacent areas where a lot of development is occurring will become very important to how Logica develops into the future.

Collecting, sharing, and visualizing the huge volumes of data created during the early stages of drug discovery and development have long represented bottlenecks to accessibility and efficiency in need of smart digital solutions. Charles River Laboratories is an established leader in providing essential products and services to help accelerate preclinical research and drug development, and is well positioned to understand the pain points in data sharing during safety assessment and toxicology studies and their impacts on workflows and efficiency and to develop frictionless, real-time, and customer-centric tools to improve the efficiency of customer interactions and the development process overall. In this Q&A, Charles River’s Corporate Senior Vice President, Sales and Client Services, Kristen Eisenhauer and Corporate Senior Vice President and Chief Information Officer Mark Mintz discuss the company’s development and recent launch of Apollo^TM, a secure, cloud-based platform for real-time toxicology study data, in conversation with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D.

David Alvaro (DA): To begin, can you share a bit of background on Charles River and introduce the idea behind Apollo and its origins at Charles River?

Kristen Eisenhauer (KE):  Charles River helps support drug research and product research. The company originally focused on supporting animal studies, but we expanded over time to provide early-stage drug discovery services, middle-stage services used to gain approval from the FDA and other appropriate regulatory agencies, and post-market services, where we test both small and large molecule products for impurities. We are a comprehensive preclinical or nonclinical CRO; we perform sample analysis on clinical trials but otherwise focus on all of our customers’ research needs before they reach clinical phases.

Mark Mintz (MM): Our mission at Charles River is to help accelerate drug development processes to, wherever possible, reduce the time required for those processes. The idea for Apollo came from a larger push at Charles River to update how we work in a more automated fashion.  Obviously, the work that we do to support our customers generates massive volumes of information and data, but before Apollo, all of that information was accessed and shared manually: a customer would send an email asking for information or data or a document, and then somebody at Charles River had to manually send that back –– sometimes requiring multiple steps and multiple people. We had a simple portal in place, but it wasn’t really interactive, and data were manually uploaded and not in real time. We saw a critical opportunity here to digitize, automate, and streamline the way that these data travel from our labs to our customers and back. The underlying goal is to make data sharing as quick, easy, and automated as possible to allow us to focus less on these manual processes and more on the science itself.

DA: Can you expand on the types of data your customers need during these studies and the limitations of not having easier, real-time access to the data? 

KE: Before we developed Apollo, accessing, monitoring, and utilizing data was completely manual. Typically, the client would have to email the study director, who would then need to get access to that data if they didn’t have it at their fingertips. Then, they would have to respond either with an email containing spreadsheets of data or manually put them into the portal, where files could be accessed by the customer. No matter how responsive our people were, this inevitably took time. Additionally, it was virtually impossible for a customer to access these data during off-hours because there needed to be a person working on the other end of the line to support those requests. In many cases, there really was an important unmet need to share data –– things like body weights and observation and pathology findings –– more rapidly than was possible.

Additionally, many customers expressed ongoing and increasing concerns about the limitations of how those data could be shared or transferred. Manual processes unavoidably create opportunities for data to be sent to the wrong person, and in this day and age there are more secure options than using email to share data.

DA: Will giving your customers increased and real-time access to these data create new opportunities and transform how these studies are conducted and what is possible? 

KE:  We believe so. At a very simple level, this allows employees at our customer and client organizations to have more immediate meetings with their management to evaluate data that has already been visualized for them, rather than them needing to take the time to input the data into their database or system to do so. They are able to make decisions very rapidly about the next dose or the next study that otherwise might have taken weeks of back and forth between the study director and the client around the major milestones of the study. We can definitely envision taking that and growing it beyond just an individual study in a programmatic way and seeing timelines continue to shrink as these decisions can be made faster and faster. 

With the self-quoting or ballpark quoting tool we have added to Apollo, people can not only assess what study to run next on the basis of those real-time data but even what budget is likely to be required for that next study. Rather than taking a month to make those determinations, customers can now prepare their next step internally and act much more quickly. As we know, everything these days requires approval, and budgets are incredibly tight –– the faster people can access that type of budget information rather than waiting on our end for a quote, the quicker they can act.

MM: By building this platform to make these data accessible and secure –– and through ongoing dialogues with our customers around how they work with us and how they would like to work with us –– we’re constantly exploring new and different ways to bring additional data together to make it easier to develop new and more meaningful insights and to enable them to make decisions more quickly and optimize how they work.

KE: We are always thinking about the future and what else we can enable. Today, we’re identifying trends very early on with regards to a specific study with a specific drug for a specific customer. Going forward, as we look to free the rest of our data and put it all together as the Western world’s largest nonclinical CRO, the amount of information and data we have access to on a very high-level perspective will absolutely transform the decisions made in the future. Our next step will be to go from an individual client and an individual biologic or small molecule to examining, for example, all of the monoclonal antibody studies we’ve run, and assessing the collective impacts to different tissues. We think that we could ultimately leverage that huge data set on a very holistic basis to help clients make better decisions and/or to better predict outcomes, which help transform the speed and accuracy of drug development.

Apollo™ is an innovative platform that empowers users with real-time access to study data, milestones, documents and program planning tools.

DA: Was developing something like Apollo inevitable given the bottlenecks you mentioned, or was there a particular inflection point that got things moving?

KE: The real impetus came through feedback from our customers. While a large portion of the Charles River preclinical business comes from large, global pharma companies that have large staffs and lots of resources to conduct research, the majority of our customer base is smaller biotechs that lack these resources. Our initial focus was on providing real-time data in a way that could support these companies that most need help to make these quick decisions. The more we looked into developing solutions, the more we found that data visualization tools were practically nonexistent in the preclinical CRO space, and as a result companies were having to spend money either building teams to do it or outsourcing those activities separately. We had a bit of a eureka moment where we realized that, since we are producing the data, it would add significant value to present it in a way that is both visually pleasing and more easy to grasp, along with a self-help tool that allows people to access it any time that they want.

MM: Like a lot of the digital transformations across the industry, the push for Apollo really came in 2020, during the lockdown phase at the peak of the COVID-19 pandemic, which really emphasized the need for digital experiences, technology enablement, and moving more efficiently with less manual work. That drove the company into a real commitment to a digital journey and digital transformation, and Apollo represents the first big piece. Ultimately, we want to expand this offering, not only help to our safety assessment customers but to create end-to-end access across the portfolio of products and services that Charles River provides so that all of our customers can benefit from these types of experiences and reduce timelines to get results out efficiently and as effectively as possible. Additionally, we want to enable our own employees to spend maximal amounts of time on the science and helping our customers, versus having to do manual things that can be addressed through analytics and automation. 

We developed our minimum viable product (MVP) in 2021, which we then released internally with just a few customers and just a few data sets and features. We then spent the last year-and-a-half growing the MVP exponentially to the point where every preclinical Safety Assessment customer working with Charles River is on Apollo and has access to their data, their study milestones, etc. We launched Apollo publicly this March, which was a big milestone for us. We first wanted to make sure that we felt confident that we could stand behind everything and fully put out there in the marketplace, and we did.

DA: Has the response from both customers and your own teams aligned with your expectations?  

MM:  Absolutely. Apollo truly transforms these workflows in a positive way: people can spend far less time on routine tasks like pulling things together, creating spreadsheets, and sending emails back and more time actually examining the data, discussing them with the customer, or coming up with new insights. There is a change-management component built into Apollo to ensure that everybody understands how to use the new tools, which also creates opportunities to improve workflows and interactions with customers and with internal colleagues. 

The customer feedback we have received has been very positive. Apollo eliminates steps from their workflows by creating these visualizations that ensure they are looking at the data in the way we intend. They don’t have to worry about doing those things; they can just log in and get the data, develop insights, and make decisions without having to do that manual work, which not only saves them time but also makes their processes more effective.

DA: I’d like to circle back around on the point you made about Apollo being the first big step in the digital journey Charles River is embarking on. Can you share anything about that larger vision? 

MM: As I mentioned, in 2020, Charles River embraced the idea that a digital transformation was critical to furthering our mission of accelerating drug development timelines while also creating unique and delightful experiences and industry-shaping opportunities. We set out to bring people, processes, and technology together to reimagine how we work both internally and with our customers toward that goal of meaningfully reducing drug development timelines. 

We will achieve this by implementing best-in-class technologies that are instrumental in optimizing internal processes into more digitally native ones, which enables us to help our clients get their drugs and therapies to patients in less time. The core pillars for our digital transformation and journey are customer-centric design thinking, agile test-and-learn processes, and technology-led innovation. With a strong emphasis on the customer and employee experiences, we’re working to reimagine important processes that could ultimately improve patient care, cost effectiveness, drug discovery, and development and create new and better ways of working. 

From a technology perspective, what we’re doing is tightly aligned with the most important and high-impact customer needs and business opportunities. We assess our customers’ biggest needs and where can we help them to drive their drug development processes as effectively as possible and determine how we can best use technologies like the cloud, artificial intelligence and machine learning, connected devices, digitization, and automation to create these experiences and help get their products and therapies to patients as quickly as possible.

Apollo™ provides access to study data that empowers users with insights and digital features to enhance decision-making abilities.

DA:  Sometimes, when an interaction shifts from being handled by people into a digital portal, there can be a perception that something gets lost without that human contact. Do you think that’s an issue at all with Apollo, or does it simply free people up and allow them to focus on more meaningful interactions?

KE:  Apollo allows us to spend our time communicating about important things –– like the real science underlying drug discovery and development–– rather than the logistics of running studies, milestones or budget quotes. Everyone’s time is limited, but now, rather than spending 30 minutes of an hour on the logistics of how to share data, we can spend the whole hour discussing the drug itself. The interaction isn’t lost, but the quality is significantly increased.

DA: You mentioned the quoting tool within Apollo and the budget confidence that it adds. Can you expand on how that works and whether it really adds something to the picture that just wasn’t possible previously?

KE: This was an important piece of our evolving ability to respond to the voice of the customer. When we were developing the MVP for Apollo and determining what features to work on first versus what would be deprioritized, we conducted customer interviews, and this kept coming to the top in terms of unmet needs. I manage the group that creates these quotes, and even I was surprised by how important this was to customers. We already had this on our radar for later versions of Apollo, but customer interactions convinced us that it should be a top priority from the beginning.

The feedback on this tool has been tremendous. Customers understand that it provides a ballpark budget rather than a precise quote, but that is what they are looking for at those early stages. However, it is absolutely our goal to continue to refine the tool and eventually get to a place where people can develop their own study online and create a quote document that they can digitally sign right then and there. It was definitely a learning experience for us in terms of what’s important to the customers, and we ended up with a really important and useful tool that we didn’t anticipate.

MM: I think that this is a reflection of things people have become accustomed to doing in their personal lives: you can go online and find the price of something, whether you are making a purchase or just considering budgets; you can buy cars and even houses online now. People expect similar things from their business environments –– to be able to see what things would cost and how might they configure things. Allowing them to do that digitally and enabling them to either complete it on their own or talk to somebody to help do that extends our ability to meet our customers where they are and provide them the tools and capabilities to work in the ways they want to work to enable the most efficient process. I like to use the word “frictionless” a lot: make it as simple as possible for them to engage with us in any way they need to so that we can serve their needs as best as possible. 

DA:  Did any other unanticipated features emerge along the way that have either already been integrated into Apollo or are in the wings for future updates?

KE: One surprising benefit has been how Apollo has increased our understanding of the complexity of our customers’ organizations and how they are structured and organized. While that may not have yet led to a new feature in Apollo, it’s helped us to better identify what will be helpful for them in the future to avoid other speed bumps, like determining the different stakeholders at the customer company that ought to have access to the features for a given project. It been a very good learning moment for us to better understand how they work internally, which has in turn helped our salespeople interact with them in a more productive way to ask the right questions and reach the right people faster. 

Additionally, it’s helped us better understand our customers who are smaller, one-drug biotechs for whom everything really hangs on the development of that drug, which gives them a competitive advantage that they need to maintain. Apollo has helped further solidify to us how important these individual drugs and the corresponding data are to these companies’ lifelines. 

MM: At a very high level, what always fascinates me when we work on projects like this that are strongly driven by conversations with customers is that we often end up somewhere quite different from what we imagined at the onset. Those conversations don’t only happen early on; we also share different mockups and prototypes and have them walk through them and inevitably iterate that many times. 

That direct connection is invaluable. By the time we properly launch the product, we can feel confident that it meets those customer needs, because we have had the conversation multiple times during development. The iterative process is critical, because you keep being able to refine your original concept and understand which elements were spot on and which need to change. It requires being open to ending up in a different place than where you thought you would, but that usually means discovering a lot of great new ideas.

Currently designed to support safety assessment and toxicology studies, Apollo™ offers complete program oversight to help create efficiencies in the drug development process.

DA: I realize you only just launched Apollo, but I’m curious about what may come next. Are you more focused on further refining the existing features, adding new ones, or extending the portal across the other work that Charles River does? 

MM: It’s really all of the above. We are continuously expanding the scope of Apollo’s features for our safety assessment customers –– including expanding to different study types, as well as different data sets and insights on those data sets –– and continuing to increase the breadth and depth of what’s there. We also plan to do this beyond our safety assessment business and across our entire portfolio of products and services. 

Ultimately, we aspire to create industry-leading digital experiences that more seamlessly enable the drug discovery and development process. We are working to do this across all of our businesses and products; in different ways depending on what different customers need. We aim to delight our users and colleagues with intuitive, easy-to-use experiences that take meaningful time off the drug development cycle and ultimately help our customers create healthier lives. 

DA:  Looking beyond the immediate term, is there a vision for more significant shifts that Apollo and the rest of this digital transformation may enable in the future?

KE: We have a longer-term vision for Apollo to not only support the work that customers do with Charles River but to integrate all of their drug development activities, whether those are services outside of the scope of what we do or simply things for which the client has chosen another provider. We see real value for customers to have a single hub for all of their drug development data; on the other hand, we see value for Charles River in better understanding work outside of our purview, which can help our M&A and digital strategy. 

MM:  Charles River is known for vast scientific expertise, and the work we do for our customers and the trust we have built is second to none. From my perspective, science and technology go hand in hand. Further evolving our technical capabilities to align with our science capabilities and growing as a leader in both the science and the technology enabling that science is not only a great aspiration for us internally –– it’s exciting and motivating and allows everyone to focus on super high-value work. It’s also good for our customers and the industry overall to create these frictionless, high-speed processes that help get these therapies out there more quickly, which is great for patients and the health of society. Becoming a true technology-backed science leader is a real aspiration for us.