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1rem

How biopharmas are advancing their pharmacovigilance operations.

Establishing the Right Foundation for Patient Safety 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

Compliance with state telemedicine requirements is imperative, as not only will most clinical trial sponsors contractually require such compliance, but non-compliance may also subject the practitioner to licensure violations and liability.

Telemedicine Considerations When Conducting Decentralized Clinical Trials 

The emerging solution helps assess how much a therapy being evaluated in a clinical trial can benefit an individual patient.

New Study Demonstrates First-Of-Its-Kind Artificial Intelligence Platform, TrialTranslator

Unlocking the full potential of artificial intelligence requires these stakeholders to ensure their data are accessible and secure. 

How Medical Writing and Regulatory Affairs Professionals Can Embrace and Deploy Generative AI at Scale

In this video interview, Dipanwita Das, CEO & co-founder at Sorcero, looks back to 2024 and discusses some of the industry’s greatest advancements in using artificial intelligence from the previous year.

Clinical Technology Advancements in 2024

, 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: Looking back at 2024, what advancements in clinical technology are you most proud of the industry for achieving?

Well, I think a lot of advancements and progress has been made in the patient matching and recruitment. We've successfully seen industry use AI (artificial intelligence) to analyze electronic health records to more effectively identify eligible trial participants and I think that's really important. Also, finding the right participants before you're able to target them. That's the other point that's been really difficult to do, is that personalization and the targeting. Real-time data monitoring saw a lot of progress as well in using AI to stay abreast in real time, instead of being able to circumvent potential hurdles and issues as they show up, rather than six, eight, 10 months after the effect, which would add absolutely years to the length of a trial and the complexity thereof, and also make it more expensive. We've seen a lot of progress in design optimization, where you're actually using more historical trial data to reflect on how future trials of similar compounds or similar therapies can be optimized and of course, the dream of decentralized trials, diversity needs a lot of work to actually be instrumented and implemented and I think investment in more decentralization and hybrid trials, we've seen some great progress in that as well in the course of 2024.

Videos

In this video interview, Dipanwita Das, CEO & co-founder at Sorcero, highlights how artificial intelligence, real-time monitoring, and historical data can aid in optimizing trial design. 

Technology’s Role in Trial Design Optimization

ACT: What types of technology do you think clinical research experts can benefit from the most in their goal of improving patient outcomes?

 Technology, of course, is a huge role in transforming all of 

, artificial intelligence (AI) certainly, but I think in the next one or two years, trial design optimization is where I'm excited to see the use of more historical data at scale, particularly to optimize inclusion/exclusion criteria, use of AI, and better data science and EHR analysis for patient recruitment, making sure they're actually covering all of the patient populations that need to be represented. Better real-time monitoring: real-time monitoring is just really, really difficult right now, so making sure that there is a certain degree of automated detection of data quality issues, etc., and of course, using AI for literature review protocol writing, as well as automating documentation. In the medium to long term, there are lots of other spaces in which AI and technology overall will play a huge role, from synthetic control arms to adaptive trial designs as well as digital biomarker developments, or even the idea of in silico trials, so there's a lot of exciting things coming up, but I think in the next one to two years, optimizing design, recruiting patients, and real-time monitoring is going to go a long way.

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

, 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 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 AI and its role in optimizing trial design.

Leveraging Artificial Intelligence Alongside RWE/RWD

, 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 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, Jane Myles, program director, Decentralized Trials & Research Alliance (DTRA), looks forward and shares her thoughts on future trends with clinical technology.

The Future of Decentralized Trial Elements 

, Jane Myles, program director, Decentralized Trials & Research Alliance (DTRA), discussed trends and challenges in decentralized clinical trials (DCTs) including misconceptions and the rapid adoption of DCT elements. Myles also highlighted DTRA's initiatives on site adoption, operational planning, and the integration of digital health technologies.

ACT: Looking forward five years, what do you think the state of decentralized clinical trials (DCTs) will be?

I think it's a yes and. First, I do see a growing slope of adoption and I don't think that's just because I'm a wild optimist. I think that's just because as people age, being digital-first is becoming normal. I know there are lots of concerns about older participants using this technology. By the way, they're accepting it pretty well, maybe more so than other sectors of the population, so I think we're going to see that the gap will narrow. The other thing is, I think that patients are actually going to demand that trial activities reflect how they would act in their daily life. I've been believing that for a long time, but I'm starting to see that's coming.

The other thing that's going to evolve is the use of AI (artificial intelligence) behind the scenes as we run trials as a whole. I'm not sure exactly how that's going to integrate into the DCT domain, but I could imagine a world where you might have chat bots that were able to provide on demand tech support in the language of the participant in real-time to a script that had been approved by the IRB. Imagine, instead of waiting on a phone line, you were able to get at least two levels of questions resolved with an AI bot, and then if you really had a human need, you could connect to that person. That's one way.

I also think actually digital health technologies, DHTs are increasingly—radically increasingly—being used in trials, and they fit so well with DCT methodologies, we're going to see more and more of that pairing. Yes, we are going to send you a continuous glucose monitor, and yes, you are going to be reporting your insulin values, and yes, we're going to see you time and again for some of the other assessments, but you can do all your labs at your local lab. I guess what I imagine is a future where technology as a whole is going to shift the way that trial methods are used.

In this video interview, Jane Myles, program director, Decentralized Trials & Research Alliance (DTRA), highlights how DTRA is working with sites and sponsors to gauge the feasibility of site-validated technology use.

Using Site-Validated Technology 

ACT: What are some initiatives DTRA is currently working on? What excites you the most?

Then the third initiative on site adoption has been fascinating. The work will continue in 2025 and it has been focused on what would it take for sites to be able to use the technology they've already validated and qualified at their network or location, instead of using sponsor-defined technology for the same solution. I'm not talking about a CTMS for patient scheduling, or all of the regulatory documents. I am talking about things like eConsent. We might think in longer terms about eSource, but when I hear from sites that they have used their own version of eConsent for 800 or more studies, it is hard for me to understand why they would be asked to accept an external eConsent platform by the sponsor. Now, the sponsor wants to ensure the data congruity and integrity is there, and that's really what we're trying to define; what would sites need to be able to show to sponsors to help the sponsors feel that the risk of having multiple solutions has been mitigated? It's really brain bending, because it's truly flipping the whole decision-making framework upside down. That's what we've been working on. We're going to continue some of that work in 2025 including trying to set out what are the questions you would ask sites if you were trying to get their input on the operational plan, and when, also, is there a way to collect high-level insights on the experience from site on using these technologies, perhaps by therapeutic area or region? I don't know the answer to that question. We're just scoping that work, and then we're going to dive in a little different direction into the world of how would HCPs be integrated into clinical research without making them investigators, aligned again, to those guidance documents coming from FDA, both for DCT and for what I call point-of-care trials. First, what do they think about that? Has anyone asked them, “Is this even a viable possibility, given the metrics and the constraints of the healthcare system and how it works?” Then, if it's a yes, what do they need in order for this to work?

In this video interview, Jane Myles, program director, Decentralized Trials & Research Alliance (DTRA), highlights challenges with technology adoption including how sites may be asked to work with a variety of different providers.

Managing Clinical Technology Overload

ACT: From a decentralized clinical trial (DCT) perspective, what are some best practices to ensure that stakeholders don’t feel overwhelmed by technology?

Now there are some other reasons people feel overwhelmed. Honestly, there are a lot of different providers out there, and especially as a site, you may be asked to work with many different providers, not even within the same study, but across the suite of studies you're doing. The technology burden feels higher when it feels like—I'm making this up now—the eConsent solution looks different when you work with vendor ‘X’ versus vendor ‘Y,’ but actually the core capabilities and the GCP (Good Clinical Practice) and data requirements are exactly the same. You still have to be trained on both to use them as a site member. Some sites have actually asked if there should be a consolidation, or a narrowing of the available options for these different methodologies. I'm strongly opposed to that, simply because that's not how this whole industry will work. However, I do think that over time, we're going to see some more common best practices and norms, even if we don't get to a state where you might have one training program that applies across every eConsent solution.

I think the other thing that's going to be really interesting here on tech adoption is that as the FDA inspectors and investigators who conduct the inspections go out and learn about these different platforms, they're going to become more familiar and comfortable with the technology too, and probably make recommendations. I'm not saying 483s, I'm saying they're more likely to be able to document anything we need to pay attention to in a guidance document, similar to the one they just released on electronic systems and signatures. Tech adoption is hard, especially when it feels like you're being asked to learn something new every week. I get that.

Managing Clinical Technology Overload From a DCT Perspective

In this video interview, Jane Myles, program director, Decentralized Trials & Research Alliance (DTRA), touches on the adoption of DCT elements and how it can affect study team members.

Adapting to the Increased Use of Decentralized Technologies

I actually think that the sponsors are becoming more familiar with the technology, which is not to say that every person is familiar, and that's, I think, one of the biggest challenges of the change curve of adoption in industry. We have been using some of these elements in trials now for nearly two decades, and yet it may be the first time that the individual study team member is using them, either at the site or at the sponsor study team, so there's always a little bit of friction the first time you use things. Yet, what we're hearing from some of those sponsors is now these elements are routinely included in 80% of their global trials, which does not mean every patient is going to experience every possible DCT element, or every country or site will adopt them all, but they're designing the protocols so that these elements are included from the start. I think that that overwhelming feeling in part, might be related to how many sites and many study team members—not the innovation group—but the study team members had to adopt these methods, kind of on the fly during COVID and if that was your first experience, it did feel really overwhelming.

In the fifth and final part of this roundtable discussion, participants discuss the practice of designing technology with patients in mind and share their concluding thoughts on improving diversity in clinical trials.

Peer Exchange: Technology Accessibility 

In this roundtable discussion, industry experts gathered to address the state of achieving greater diversity in clinical research. The group highlighted a variety of topics including current challenges they are seeing, the use of technology, and more.

Moderated by Otis Johnson, PhD, MPA Co-Founder & Principal Consultant, Trial Equity, the panel featured key insights from Sophia McLeod, Sr. Director, Government Relations, ACRO; Erin Stern, PharmD, Research Support Pharmacist, Mayo Clinic; and Caitlin Brown, PharmD, Neurocritical Care and Emergency Medicine Clinical Pharmacist, Mayo Clinic.

Brown highlighted the progress in women's representation, but also noted challenges with racial and ethnic groups due to historical mistrust, access, eligibility criteria, and language barriers. McLeod emphasized the importance of early diversity planning in protocol design and community engagement. Stern discussed the logistical challenges of decentralized trials and the need for better technology integration. The panel agreed on the necessity of consistent funding, community involvement, and ethical artificial intelligence use to enhance DE&I in clinical research.

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