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DE&I

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1rem

Phase III CheckMate -8HW trial shows the combination of Opdivo (nivolumab) and Yervoy (ipilimumab) significantly improved progression-free survival and overall response rates in patients with microsatellite-instability–high or mismatch-repair–deficient metastatic colorectal cancer.

Updated CheckMate -8HW Trial Results Show Significant Survival Benefit with Opdivo Plus Yervoy in MSI-H/dMMR Colorectal Cancer

Use of decentralized approach in a Phase 1 pharmacokinetic trial shows the ability to enable remote data collection and monitoring, which could improve patient access and enhance the efficiency of clinical research.

Decentralized Phase I Trial Highlights Promise of Remote Data Collection to Improve Clinical Research

An updated long-term analysis of the Phase III KATHERINE trial confirms that adjuvant treatment with Kadcyla (trastuzumab emtansine) significantly improves overall survival and long-term invasive disease–free survival compared to trastuzumab monotherapy in patients with HER2-positive early breast cancer with residual invasive disease.

Long-Term KATHERINE Trial Analysis Shows Kadcyla Significantly Improves Survival in HER2-Positive Early Breast Cancer

A focus on data and process excellence advances product development.

Five Predictions That Will Drive R&D Effectiveness in 2025

Pilot trial shows that a user-friendly electronic patient-reported outcome (ePRO) monitoring system significantly improves patient-centered cardiovascular care by enhancing patient-physician communication, increasing clarity in treatment explanations, and empowering patients with better knowledge of their conditions.

Study Shows ePRO Monitoring System Enhances Patient-Centered Care with Improved Communication, Treatment Clarity

In this video interview, Dipanwita Das, CEO & co-founder at Sorcero, highlights increased data management, DE&I, and regulatory preparedness as key trends for the industry in 2025.

2025 Trends: Investing in Clinical Data

, Dipanwita Das, CEO & co-founder at Sorcero, discussed advancements and future trends in clinical trials. Key challenges include high costs, long timelines, lack of diversity, and rigid protocols. Das highlighted technological advancements such as AI, real-time monitoring, and historical data analysis, which are all expected to optimize trial design and patient recruitment in 2025 and beyond.

ACT: What are some trends we’ll see in clinical trials in 2025?

Effective and well-designed clinical trials is essential for the commercialization stage, and particularly when it comes to making sure that the drugs and therapies, as designed and commercialized, are getting to the right patients, which means the links between the clinical stage and the commercial stage, that's becoming stronger, and the gap between those two things are becoming much less. The first area where we are going to see a lot of investment rightly, is in data strategy and in integration of data itself. First and foremost, the industry has to be prepared for increasing volumes of real-world data and real-world evidence getting integrated in a real-time process into measuring the effectiveness of trials, as well as adapting or modifying them. We're definitely going to see a lot more on data standardization. That's actually a problem. Before you can do AI (artificial intelligence), you need to get your data into order, check the quality of it, the governance of it, the updating of it, and so forth, so making sure that that organizations have enough data infrastructure to handle that sheer diversity of data sources, which, by the way, we see every day, the diversity of data sources, the unstructured nature of it, the non-standardization of it, can be a really, really huge problem. Then in 2025 I predict we're going to see a lot of investment, of course, also in patient diversity and inclusion, where you're strengthening strategies for diverse patient recruitment, looking at more decentralization so you can reach people where they're at, you want to reduce the barriers that a typical patient will face in getting to a trial and adhering to that regimen through the length of the trial. You're also going to see a lot on building relationships with underserved communities, and I think this is important in working with local health systems, with nonprofits to access the communities which might have a disease prevalence that isn't reflected. Last, but certainly not the least is regulatory preparedness. Regulations are getting more complex and more prescriptive and more demanding, but monitoring FDA guidance on novel trial designs and DCT approaches, staying current with international regulations, so that you have a very smooth commercialization process, as well as data privacy. As we use more AI to analyze data that has PHI and does not, making sure that you're able to actually align with those data privacy requirements, which is essential for trust is going to be very important. I could probably go on, but these are some of the key items that I expect to see a lot of progress being made on.

Videos

In this video interview, Kimberly Tableman, founder & CEO, ESPERO, discusses how EDC and EHR data can be streamlined to increase efficiency at sites. 

2025 Trends: Streamlining Data at the Site Level

, Kimberly Tableman, founder & CEO, ESPERO, discussed the challenges and trends in protocol development for clinical trials. She also highlighted technological advancements, such as data interoperability standards like ICH M11 and CDISC, and how they are expected to streamline protocol development and support generative AI in 2025.

ACT: What do you think should be top of mind for clinical operations executives going in 2025?

I also think we're seeing a lot with real-world evidence and real-world data. Overall, there's an opportunity for streamlining again, at the site. Eighty percent of the data that we collect in the EDC is also collected in the EHR, and we're seeing a lot of solutions from a tech perspective out there, where you actually can leverage the data that's being input into the EHR, leverage that into the EDC, and then there's only about 20% of the data that's very specific to clinical research that then has to be filled out. That's a huge benefit to the site, because we talk a lot about burden for the site, and removing that burden, making life easier for them, not asking them to dual data enter the same information is really a huge benefit.

In this video interview, Kimberly Tableman, founder & CEO, ESPERO, highlights how data standards are empowering the use of artificial intelligence.

2025 Trends: Leveraging Data Standards

I think there's a number of different things, but I think the data interoperability part that I just spoke of, and leveraging the standards, again, from the ICH M11 all the way through enabling that digital data flow, but what it really empowers again, is artificial intelligence. We are seeing the use of AI, and we talk a lot right now about artificial intelligence with a human in the loop, so I think for where we're at right now in pharma, I think that clinical operations executives need to be looking at it, but I think also realizing that we're not yet at the advent where artificial intelligence is taking away jobs and so on and so forth, you still need people that are looking at what the AI generates and saying, “Yeah, this works, or it doesn't work.” For us, for example, we use artificial intelligence to bring in a bolus of objectives, endpoints, inclusion/exclusion, criteria, that are options that you can put into the protocol, but a human being is still looking at that information and making a decision about, “Yeah, I want to leverage this objective or this endpoint, I want to put in this inclusion or exclusion into the study design overall.” I think getting comfortable with a trust but verify, taking some steps in that direction, to start getting more comfortable with artificial intelligence will be really helpful.

In this video interview, Kimberly Tableman, founder & CEO, ESPERO, talks protocol submission formats and how data interoperability can support artificial intelligence.

2025 Trends: Advancements in Data Interoperability

ACT: What are some technology trends we’ll see in clinical trials in 2025?

I think one of the things we're really seeing right now is related to data interoperability, so we have historically had a set of data standards, particularly getting data submission ready from a CDISC perspective, but we have also now seen with the FDA putting forth the guidance on the ICH M11 standardization around the data for protocol collection as well, so that data, and then there's an opportunity, really, from an interoperability perspective, to map all the way through, so you have the protocol now in the ICH M11 format, you can map that to CDISC. We've done that as well as USDM, and we have for the schedule of activities, mapped all the way through to this, to the actual SOA itself, so the ability to then, from a FHIR perspective, be able to share that data, which this has all been things that the industry has been talking about for a number of years, and it is now finally coming to fruition. The benefit of that data interoperability is supporting generative AI. It's really allowing you to use data in downstream systems to really automate that digital data flow. Again, stuff that we've been talking about but haven't really been able to fully recognize, I think we're going to see that happen in 2025.

In this video interview, Sujay Jadhav, CEO of Verana Health, talks external control arms and how they can increase study feasibility and inclusivity. 

Accelerating Drug Development With External Control Arms

, Sujay Jadhav, CEO of Verana Health, discussed potential advancements the clinical research industry could see in 2025. Jadhav highlighted how unstructured data like physician notes and imaging data can be leveraged to improve disease identification and progression tracking. He also talked synthetic control arms and how they can replace expensive non-treatment arms, enhancing trial efficiency.

ACT: How can external control arms (ECAs) accelerate drug development?

There's a number of areas in terms of how external control arms can accelerate drug development. As I mentioned, the biggest advantage is when you're looking at the non-treatment arms and then the costs associated with recruiting patients is pretty sizable, and that's really where you can plug in real-world data to help be a better proxy, or help bolster that part to allow you to compare and contrast. Some of the areas where control arms can accelerate drug development, one is addressing ethical dilemmas. When you look at ECAs, they eliminate the need to randomize patients into non-interventional groups, particularly in studies where a placebo or a standard of care may be ethically questionable.

Another area is utilizing existing data, so what we find is in rare diseases with small patient populations, external control arms allow researchers to use historical, real-world data to make studies more feasible and more inclusive. As you can imagine, without external control arms, going through and recruiting enough patients, which can be a good comparison there as well in rare disease areas, is very, very difficult to do, and that's where real-world data can shine quite well.

There's a quality element in terms of how external control arms can help, so providing high quality insights by creating reliable comparison groups that can really help reduce the reliance on traditional non-interventional groups, or maintaining robust scientific validity there as well. What we are seeing, again, is the theme of going after the unstructured data to help improve these high-quality insights. With technology we have today with helping ingest data in a more efficient fashion than historically has been as well, that allows you to bring in a lot of this unstructured data and provide insights in a very timely fashion, which historically has been very difficult to do. Fundamentally, I think when you look at external control arms, they accelerate the overall therapy development, and these methods help streamline the clinical trial process, enabling faster development and effective therapies without actually compromising scientific integrity.

In this video interview, Sujay Jadhav, CEO of Verana Health, highlights the role of real-world evidence alongside unstructured and structured data in better understanding the patient journey.

Reaching Improved Outcomes With Multiple Data Sources

ACT: Can you share some examples of therapeutic areas that will benefit the most from advancements in real-world evidence (RWE)? What will its impact be in these areas?

One of the other therapeutic areas that we focus on is in ophthalmology. We’re using real-world data to enhance the management of conditions such as glaucoma and age-related macular degeneration. There is a heavy focus, not only on just the structured data, but also unstructured data, and curating the physician notes to help bolster certain medical variables that typically real-world data and unstructured data cannot solve. With that helping, again, improve diagnosis of patients with those particular diseases, and what we find, particularly in ophthalmology, is a lot of these diseases take a good period of time to actually progress overall. This is something where longitudinality of data is extremely important, and understanding that dynamic by bringing in both structured, real-world data, and non-structured data on a very regular refresh cadence is extremely important to make sure that that you're helping understand sort of that overall patient journey there as well, so we're seeing that a lot in some of the key disease areas in ophthalmology.

Then another area in the urology side where we're seeing real-world data being leveraged quite a bit is in bladder cancer and looking at benign prostatic hyperplasia, and offering clinical insights into treatment outcomes and disease progression there as well. By tapping, again, into the unstructured data, and what we find in urology, actually, while there is a number of structured data variables there, a lot of the insights, even more than we see in ophthalmology, are actually in the unstructured data. We are leveraging natural language processing techniques on the physician notes to identify key variables to understand this particular disease progression. This leads better patient identification for clinical trials, improved treatment decisions, and optimizing trial site selection as well.

In this video interview, Sujay Jadhav, CEO of Verana Health, discusses how using genomic data and real-world evidence can provide added context to traditional factors such as Gleason scores and PSA levels.

Complimenting Traditional Research With Genomic Data in Urology 

I think there's a number of areas and examples that we're seeing in our therapeutic areas that we focus on with real-world evidence. One is in the urology space around prostate cancer care and leveraging not just taking data that is available from EHR data, but also genomic data as well. Historically, when you look at prostate cancer, a lot of the reliance has been on traditional factors such as Gleason scores and PSA levels. Sometimes this results in overall under treatment of that particular disease, but I think complementing that with real-world data and genomic data, I think it can lead to better diagnosis of this and better understanding of the patient journey as well, and then allow you to tailor the treatment decisions and help improve recruitment with the overall clinical trial process. We’re seeing that actually play out quite a bit in the prostate cancer arena.

Complimenting Traditional Research With Genomic Data in Urology

 In this video interview, Sujay Jadhav, CEO of Verana Health, talks AI and its role in optimizing trial design.

Leveraging Artificial Intelligence Alongside RWE/RWD

ACT: How big of a part will artificial intelligence (AI) play in potential advancements around real-world evidence (RWE)/real-world data (RWD)?

Looking at how we leverage AI and real-world evidence/real-world data, it's going to play a substantial role, and it is starting to play a substantial role currently, right now, in unlocking the potential. There are multiple areas in which it is starting to unlock the potential. One that we're seeing and we're focusing on as well is predicting disease progression and assisting with early detection to support patient recruitment and retention, and so identifying suitable participants and helping reduce dropouts. The earlier you can do that, the more cost effective it can be in the overall clinical trial process. The other area where we're seeing and we're leveraging is identifying trends to inform data driven decisions across the product development life cycle, so helping improve the upfront process around how you optimize clinical trial design, and enabling adaptive trial design. Thematically, the more you can actually simulate what can happen with the particular clinical trial, and do that more upfront. That allows you to set up the actual trial from a logistical perspective more effectively and reduce costs and time as well. Also, as you go through that particular clinical trial leveraging AI to help support the adaptive trial design, if you need to actually change inclusion/exclusion criteria as you start to get results, leveraging AI to do that can be very, very helpful, as I said, and can help improve the overall effectiveness of the of the trial. The third area where we're seeing and leveraging AI quite a bit is enabling precision medicine, and that's by identifying subpopulations that are most likely to benefit from the treatment being investigated in a clinical trial, and ultimately helping drive and improve patient outcomes.

In this video interview, Sujay Jadhav, CEO of Verana Health, talks AI and its role in optimizing trial design.

In this video interview, Sujay Jadhav, CEO of Verana Health, highlights how the industry can use RWD to compare outcomes from treatment and non-treatment arms. 

2025 Trends: Leveraging Real-World Data for Synthetic Control Arms

ACT: What trends do you think we’ll see in 2025 with real-world evidence (RWE) and real-world data (RWD) in clinical research?

The second bucket is around external control arms, synthetic control arms. It's been around for quite a while, overarching use of this for the particular components of the clinical trial where they're actually not getting the treatment, so the non-treatment arms are leveraging real-world data to understand and compare and contrast to the treatment arms that you put forward there. Historically, recruiting patients where you're not providing them the treatment, as you can imagine, is very, very expensive and very time consuming overall. I think that's the area where you look at synthetic control arms, and leveraging real-world data can help be a good proxy, or can help augment and allow you to compare and contrast to the treatment arm. The good thing with real-world data and leveraging it for these type of control arms is that the volume is very, very large, and the quality and the longitudinality of the data is pretty good as well. If anything, the compare and contrast, the treatment arm versus a non-treatment arm can actually be a high-quality fashion as well. These are some of the key trends that we're seeing as we move into 2025 in terms of helping improve clinical research by leveraging real-world data.

In this video interview, Sujay Jadhav, CEO of Verana Health, discusses how the use of unstructured data can help improve clinical research in 2025.

2025 Trends: Using Unstructured Data in Clinical Research

I would say, as we move into 2025 there's two generic themes that I see in terms of how real-world evidence and real-world data is going to be utilized in clinical research. I think one is around the data and expanding the type of data that we're using for clinical research. The second one is what I like to bucket it under, helping productionize, using external/synthetic control arms in the overall process. When you're looking at the data side of the house, historically, a lot of the data that has been used for clinical research, a little bit more mainstream, has been more structured data, seeing a good movement into leveraging unstructured data. There's different types of unstructured data, things such as physician notes, as an example, is an unstructured data source that we are leveraging in terms of helping facilitate improved clinical research. There's also other unstructured data sources, different data sources like imaging data as well, and we're starting to use, and see usage of imaging data quite a bit in terms of helping identify patients with certain diseases, understanding their disease progression as well, and then rolling that into clinical research. The good thing about imaging data is it's like an unbiased data source. From that perspective, the credibility, the quality of including that is very, very good.

Data Management

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