Cancer centers need to ensure they have sufficient staff, the appropriate technology, and the right patient population to conduct clinical trials — both to contribute to oncology clinical research and to provide the full range of treatment options to their patients. Marie E. Lamont, General Manager of Inteliquet, discusses how the company’s tools assist cancer centers in assessing the appropriateness of specific clinical studies for their sites and in finding the right patients to participate in the trials they do conduct, in conversation with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D.

David Alvaro (DA): Can you introduce us to Inteliquet — from the company’s genesis and through any major inflexion points to the work you are doing today?

Marie Lamont (ML): Inteliquet was originally founded as TransMed Systems, which sought to help translation medicine labs with a business information system for oncology research. However, the market wasn’t yet ready for that offering, so in the company pivoted in 2015-2016 to explore how to help cancer centers make better, data-driven decisions. At the time and continuing to today, this was an acute challenge, given that electronic medical records (EMRs) didn’t collect data in a consistent way, so there was a need to translate and harmonize all these data to make them usable. The company began structuring the database to help match patients to clinical studies. When I came onboard in late 2019, we began re-evaluating our purpose and determining how we could best leverage our technology and data structure to truly help cancer centers.

In 2021, the company merged into IQVIA, a global organization also interested in using technology to enable healthcare and to help health systems reduce their administrative burden and make data-driven decisions. The purpose of Inteliquet has not changed at all since we became part of IQVIA: we work with cancer centers to help them make data-driven decisions, and IQVIA has encouraged Inteliquet to continue to do just that.

DA: Can you expand on the current challenges that cancer centers face and how Inteliquet helps reduce some of their burdens?

ML: Inteliquet thinks about the workload associated with finding patients for clinical trials. Thirty years ago, it was easy. There were fewer clinical trials, and they were not nearly as complex. Today, there are numerous biomarkers and disease stages — we calculated potentially 20,000–50,000 permutations. When you think about the types of studies with all those biomarkers and all the other measurements, it makes the work incredibly complex.

I am not sure that the industry — EMRs, hospitals, pharma companies, CROs, and so on — has done enough to help frame that out. A lot of the information exists as PDF files and in physician notes as free text. Globally, data cannot easily and quickly be accessed and leveraged by someone in a healthcare setting. For a cancer trial, for instance, if a person must have four relevant biomarkers to qualify for a given study, it is necessary to dig through the patient records to determine whether that is the case.

Inteliquet helps to speed the process along, reducing the burden on administrators by making data retrieval more efficient and effective, thereby also making it possible to reach decisions faster. If a sponsor or CRO proposes a new study to a cancer center, the center must conduct a feasibility assessment. Some sites call each primary investigator to determine how many potential patients they have. The individual estimates then have to be aggregated. We offer tools to perform that feasibility study electronically using patient data. It is not perfect, as staffing and patient data are constantly changing over time. But it provides the most accurate analysis at that time, and the center can make an informed decision about investing in the new study more quickly and with less consumption of human resources.

If the cancer center decides to participate in a new study, once it is set up, patients must be found. Finding patients is generally conducted in two ways: through a patient list obtained from a broad search using top-level criteria or via patient-matching using in-depth digitalized patient data. Inteliquet performs the digitalization under a confidentiality agreement, saving cancer centers time and effort up front. In essence, we are compressing the workload, which allows these centers to be more efficient and effective.

This approach benefits CROs and sponsors as well because they know that, if a cancer center says yes to a study, it means that center has the necessary patient population. Studies can be onboarded more quickly as well, which means giving hope to patients as fast as possible, which is also important.

DA: Can you expand on the considerations or limitations that are holding cancer centers back from fully participating in clinical trials?

ML: The complexity and administrative burden of clinical studies are issues, as are costs. There is a huge diversity of cancer center types, and not all of them have the staff or the resources to engage in clinical research. It doesn’t mean they’re not important — they are doing patient-facing care, and they are maniacally focused on helping those patients.

But research and clinical studies require investment, and it’s a lot of investment. Cancer centers need to be compliant with the FDA’s requirements to conduct clinical studies. Not all centers are ready to achieve that, and some might not have enough patients to justify the investment. Most are already dealing with many different systems and tools, and adding another may not appear worth it, at least for the near term. Some do beautiful work with patients but might just not be ready. Our work can lower the barrier to entry, but each center needs to determine if and when to make the change.

Some cancer centers may also be worried about compliance with data privacy regulations (e.g., HIPAA). They may be concerned about how the data are going to be used and may not fully understand how these digital tools can help patients. Certainly, some are more conservative, particularly if there has been a prior HIPAA issue.

With respect to interest in Inteliquet’s offering, there are smaller centers that do not have the time to implement a digital tool and big centers (particularly academic medical centers) that want to build their own tools. There are also competitive tools on the market, and there is an interesting decision stream. Nobody wants to be the first, a first adopter — the first to explore an unknown. The larger cancer centers will generally only be comfortable using a new product if they participated in its development and have staff that are already trained on how to use it.

There are many cancer centers that use and like Inteliquet’s offering. They have used the digital tools for a number of years and can effectively leverage our solutions. They have made that investment in the product, IT staff, and general staff training to maximize the possible benefits.

DA: Where are we presently in the course of the paradigm shift from viewing clinical trials as a primary care option rather than a last resort?

ML: We are watching an evolution toward targeted medicine, with the percentage of patients receiving standard of care (chemotherapy and radiation) changing over time because chemo and radiation have toxicity associated with them. There are growing numbers of studies involving novel therapeutics in combination with chemotherapy or radiation, but the overall shift is toward targeted therapeutics that only attack tumor cells.

Of course, we’re also looking at cell and gene therapy as potential options. Those are quite costly, however, and the cost needs to come down over time for them to truly become viable and valuable options in cancer. In the meantime, the onus is on the industry to demonstrate the value behind those costs.

Overall, for patients, it means they will increasingly have the choice of receiving a targeted therapeutic via a clinical trial versus chemo and radiation. Clinical studies are more and more often being seen as a primary care option because participation in studies of targeted therapies has the real potential to extend the patient’s life.

What is particularly exciting is that there is still so much being learned about biomarkers, which is leading to even better targeting. Understanding is also growing about which biomarkers work synergistically and which do not play together well, which is driving the development of combination therapies that are also evolving.

DA: How does the overall industry move toward decentralized clinical trials models — particularly in the wake of COVID-19 – align or synergize with the work Inteliquet does?

ML: Broadly, because of sizing — they’re smaller and don’t have the staff — many community centers haven’t been able to conduct clinical studies or, if they are interested, have not been selected by sponsors, who tend to look toward the big academic medical centers because they are cheaper. However, there has been recognition that this fails to provide sufficient access to patients. I saw one report that found that, while 85% of cancer patients receive treatment in the community setting, less than 5% of cancer patients in studies occur in a community setting. Clearly, patients with access to large academic medical centers have more access to studies.

That definitely leads to disparity. We need more diversity from economic, geographical, cultural, religious, and racial perspectives. The tools that can help community cancer centers will help drive sponsors into those sites and ultimately ensure a more diverse patient population. The tools don’t care about age, religion, or race; they provide a list of patients that match a particular study’s deep protocol criteria, the inclusion/exclusion criteria. Because they help smaller community centers perform patient matching faster and more efficiently, they allow a wider population of patients to participate.

I should mention that we have seen many larger health systems make deals with small cancer centers that enable those small cancer centers to participate in clinical studies. However, those patients still have to drive to an academic medical center. Consequently, a certain amount of unintended bias inevitably enters the picture, because only those people who can afford to take the time to drive into those academic medical centers are able to participate. That still leaves a need for trials to take place within community centers.

This is a priority, and not just because of the altruistic mission to increase diversity and equity in clinical trials. There is also a crucial need for diversity to ensure that data is representative of the patient population and that trials truly represent the population that will be using the new therapeutic once it has been approved.

Furthermore, choice is important. I have family members who have had cancer who had to receive radiation and chemo because they were the only option for them. I want to make sure that everybody has real therapeutic options. That’s the best of all worlds. It could be a clinical trial; it could be a cell and gene therapy. But you want folks that you know to have a choice. Decentralized trials at community cancer centers are one way to make that happen.

DA: Can you speak a little bit about the underlying technology that is enabling Inteliquet to accomplish its goals?

ML: The data engineering team at Inteliquet maintains a focus on how to bring data in a thoughtfully organized and structured manner and then uses artificial intelligence (AI) in a learning environment to constantly improve the solution. New biomarkers are being discovered all the time. New disease pathways and mechanisms are being uncovered within particular types of cancer. Those new biomarkers won’t be used commercially until the FDA approves therapeutics based on them, but they are often used in increasing numbers of clinical trials as positive data is generated.

AI helps with that journey. It may even help move it more quickly by examining all the measurements being collected and assessing whether they are indicators and whether candidates based on those biomarkers are having an impact. Inteliquet is part of a consortium that shares these types of data so that they can be aggregated, and trends can be identified.

DA: Does the AI technology itself need to continue to evolve, or is it sufficiently advanced and more a question of further training with data going forward?

ML: Both. It is essential to continue to train AI tools and ask the right questions. It is also important to push the technology forward. Indeed, in the future AI has the potential to provide clinical decision support. Regulations are being developed around medical technology as a device because it could influence care decisions. That creates an obligation for me. If a technology is going to influence care, there is an obligation to make sure that the AI continues to evolve — not just learning on set topics, but fundamentally evolving with respect to capabilities.

DA: Can you discuss the OncWeb software and the pragmatic user experience for the cancer centers that use the technology?

ML: When a cancer center deploys the OncWeb tool, it goes through a process of ingesting the patient data. Inteliquet works with the center to make sure the appropriate data points are coming in on a consistent basis, because of course time is of the essence. Once that step is completed, the system is up and running. Our clinical engagement specialist team works with the cancer centers on an ongoing basis. When asked, we digitalize clinical trials and structure the workflow in a manner that works best for the center and the trial. Patient data collection can be tied to specific events, such as cancer clinics, or testing result reporting, such as pathology reports and genetic testing. The data is used to identify patients on a watch list that are then evaluated as potential matches.

Ongoing communication with cancer centers helps drive innovation at Inteliquet. We constantly add new workflows into the tool in order to provide the capabilities the cancer centers need. For each cancer center, the tool takes into consideration what is done at the site and what information the site prefers to use to develop the watch list. For instance, with one cancer center, Inteliquet is currently testing an intriguing surgery workflow that enables a very unique decision-making process.

In essence, Inteliquet wants to partner with cancer centers to ensure that OncWeb continues to evolve in ways that help improve their workflows. The key is to look at the assistance the cancer center is seeking and to meet those specific needs, whether that is just for conducting the feasibility study or if it also involves patient finding. In fact, during the COVID-19 pandemic, our clinical engagement specialists who are former research coordinators or nurse navigators helped cancer centers to prescreen patients. We still offer that service occasionally today, because Inteliquet’s goal is to help our partners.

DA: What do you see as the long-term evolving benefits of digitalization for cancer centers?

ML: In the industry, there is more focus on the use and reuse of data to make decisions. That is piece one. The FDA and the EMEA are also having more conversations about comparator data (real-world data); for example, about a trial arm being completely comparator data. We’re going to see that happen more and more. There is more confidence in the data as well. The evolution toward decentralized studies, both interventional and real-world studies, will continue. The COVID-19 pandemic heightened this trend, but there is more work to be done.

The use of technologies and new opportunities for the use of technologies will continue to increase as well, particularly for enabling decentralized studies, whether they are actual trials or analyses of real-world data. The focus on patient-generated data — wearables, diagnostic tools, patient-reported outcomes, etc. — will expand, too.

As these changes occur, Inteliquet’s tools can still be used because the goal is to match patients to a study, regardless of whether it is traditional or decentralized, based on a wide diversity of data and agnostic of the source (physician, diagnostic result, or patient-reported).

In addition, the need for data collection from many different sources will be ever greater. Those data will be inconsistent but when aggregated have the potential to unlock new disease applications for existing drugs, as well as new approaches to treating well-known diseases. Fortunately, we are also seeing more emphasis placed on the cleanliness, credibility, and consistency of the data used by digitalized tools.

DA: How does having support from the larger IQVIA organization aid in Inteliquet’s mission?

ML: With Inteliquet as an interface between cancer centers and IQVIA, it is possible for us to learn about clinical trials that can be brought to our cancer center customers, which provides an intriguing opportunity for the centers to access options they might not have had before. That IQVIA is focused on connected intelligence and data means Inteliquet has access to tools and technology that can be woven into our system and become incredibly valuable options for our customers. For instance, IQVIA’s work on remote source data verification could potentially reduce the burden on cancer centers. Finally, IQVIA’s relationships with sponsors and cancer centers also add value to the Inteliquet team.

DA: When you look to the future, what sort of innovations, inflexion points, or paradigm shifts do you see on the horizon for cancer care or that might impact what Inteliquet is able to do? How will that fit into an evolving model of cancer care and clinical trials?

ML: Setting aside the need for more trials across a wider geographical spread to ensure more diversity, I see more decentralized studies taking place — it is just a question of time and speed and how they will be structured. Tracking patients over time will become more important because it is increasingly recognized that long-term follow information is critical, not just in cancer but for all disease indications.

Discoveries in one part of the world need to migrate more quickly to other parts of the world — because we are talking about people’s lives; the industry needs to facilitate that communication. The increased crackdown on counterfeit drugs that was achieved during the COVID-19 pandemic needs to continue.

Over the next five years, I expect the use of AI in drug discovery and development to expand. The more we dive into biomarkers and the layers beneath them, the more that AI is needed to quickly identify therapeutic options. Comparator studies, which are already being encourage by regulatory agencies, will continue to increase, while the use of placebos will decrease — a good development for patients of all kinds.

Patients will also continue to be more vocal about what they want and expect, including with respect to clinical trials. New tools and technologies are being developed specific to help patients match themselves to trials. This trend will continue, and not just in the oncology field but in all therapeutic areas.

We really are just seeing the tip of the iceberg when it comes to the introduction of tools and digital solutions to help trials sites find the best (and most diverse) patient populations and to help patients identify appropriate clinical studies. The end result will be better drugs that provide better outcomes.