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—your fast track to the latest insights in clinical research operations. In under three minutes, we’ll recap top stories, highlight expert interviews, and keep you current on what’s moving the industry. Let’s get into it.

On this episode, we’re sharing highlights from a recent conversation with Luke Wilson, senior director for biotech and pharma services at Thermo Fisher Scientific, on how data visibility, integrated systems, and remote monitoring are transforming clinical trial operations.

 why data visibility is now a critical driver for biotechs. With aggressive timelines and tight budgets, sponsors need actionable data from across the supply chain to make informed, real-time decisions that protect patients and keep trials on track.

 the role of remote monitoring in accelerating decision-making. By maintaining a live connection to sites, sponsors can track patient safety more closely, improve trial outcomes, and reduce burdens on site staff—especially when paired with advanced devices and digital platforms.

, are key to staying inspection-ready in an era of evolving regulatory expectations. Breaking down data silos ensures consistent capture and management, streamlining sponsor access and smoothing the path through FDA inspections.

When it comes to barriers to real-time oversight, Wilson 

 to data silos as the most persistent challenge. Overcoming them requires better system integration, along with built-in analytics and dashboards to deliver live insights that stakeholders can act on immediately.

Finally, for decentralized trials, Wilson 

 best practices such as unified data management platforms, clear SOPs, and strong governance and escalation pathways to maintain operational alignment and regulatory compliance throughout the trial lifecycle.

For more on this and other developments in clinical research, visit us at 

Podcasts

In this episode of the Applied Clinical Trials Brief, we spotlight a recent video interview with Luke Wilson of Thermo Fisher Scientific in which he shared insights on how data visibility, remote monitoring, and integrated systems can streamline inspections, improve patient safety, and keep decentralized trials compliant and on track.

ACT Brief Episode 3: Breaking Down Data Silos to Accelerate Clinical Trials

On this episode, we’re spotlighting our three most-viewed articles from the past week—covering regulatory shifts, vaccine funding changes, and the future of digital transformation in clinical trials.

Recent Complete Response Letters to Replimune and Capricor

 are sending a clear signal: the FDA is turning up the heat on clinical trial design. In both cases, the agency cited inadequate or incomplete trial structures—surprising the sponsors despite earlier communications. Industry leaders warn this marks a shift toward heightened oversight that could impact everything from patient recruitment to timelines. The FDA’s own transparency push, including publishing over 200 CRLs from recent years, reinforces the message: tighten your study designs, document every interaction, and expect less leniency in late-stage approvals.

the US Department of Health and Human Services is winding down mRNA vaccine work under BARDA

, canceling 22 contracts worth $500 million. The move shifts funding toward vaccine platforms that officials say are safer and more resilient to viral mutations. Programs at Emory University, Tiba Biotech, and several industry partners are being terminated or scaled back, with some late-stage projects allowed to finish. The decision also halts funding for Moderna’s H5N1 bird flu vaccine, despite positive early trial results. Critics warn the cuts could slow progress in critical vaccine research and disproportionately impact trials involving minority populations.

digital tools and AI are rapidly transforming clinical research

—accelerating data collection, streamlining processes, and expanding patient engagement options. But with innovation comes complexity. The author of this article highlights five priorities for navigating this shift: strengthening data governance, creating trusted AI frameworks, implementing risk-based processes, upskilling clinical teams, and fostering cross-industry collaboration. The takeaway: technology should enable better science, not dictate it.

In this episode of the Applied Clinical Trials Brief, we recap our three most-viewed stories of the previous week with a look into the FDA’s heightened scrutiny of trial design, the wind-down of federal mRNA vaccine programs, and how digital innovation is reshaping the clinical research landscape.

ACT Brief Episode 2: FDA Oversight, Vaccine Funding Cuts, and the Digital Future of Clinical Trials

Liz Beatty, co-founder and chief strategy officer, Inato, discusses:

The role of technology in improving site/sponsor relationships

How increased operational costs are impacting the industry

In this episode of the Applied Clinical Trials Podcast, Liz Beatty, co-founder and chief strategy officer, Inato, discusses a number of topics around site engagement including community-based sites, the role of technology in improving site/sponsor relationships, how increased operational costs are impacting the industry, and more. 

Improving Relationships and Diversifying the Site Selection Process

In this Q&A, Rohit Nambisan, CEO of Lokavant, and Jonathan Crowther, head of predictive analytics, Pfizer, explore how AI is transforming study feasibility, regulatory review, and trial execution.

Q&A: How AI Is Reshaping Clinical Operations and Study Feasibility

A unified technology approach improves study efficiency and data collection.

AI-Powered Non-Interventional Research Delivers What DCTs Promised

Despite ambitions to streamline regulatory review, FDA’s Elsa platform has been prone to hallucinations, prompting internal scrutiny and questions about AI reliability and governance.

FDA’s Elsa AI Tool Raises Accuracy and Oversight Concerns

A new global report finds that clinical research teams are making progress but still lack the skills and tools needed to keep pace with growing trial complexity and emerging technologies.

 Empowering the Workforce of the Future: The Trends and Training That Will Drive the Future of Drug Development

Why a fundamental reimagining of how clinical studies operate is still necessary to achieve a true paradigm shift—and shed the cycle of reliance on incremental gains.

The 50-Year Technology Drought in Clinical Trials: How AI Teammates Will Finally Bring Us Into the Modern Era

In this video interview, Meri Beckwith, Co-CEO of Lindus Health, shares practical ways clinical teams can strengthen trial protocols for expedited programs—such as incorporating control groups where possible, leveraging synthetic and real-world data, and adopting adaptive trial designs to reduce regulatory risk.

Future-Proofing Trial Protocols to Avoid CRLs

, Meri Beckwith, Co-CEO, Lindus Health, discussed the evolving perception of breakthrough designation amid the FDA’s recent 

 issued to Replimune and Capricor. He emphasized that breakthrough designation does not necessarily guarantee approval, but offers quicker decision access and investor signaling. Beckwith also stressed the importance of agility in trial design, advocating for adaptive trials and control groups where ethical. To mitigate regulatory unpredictability and avoid costly CRLs, he highlighted the need for future-proofing trial protocols with adaptive randomization and synthetic control groups.

ACT: What changes would you recommend clinical teams make now to future-proof trial protocols—especially when pursuing expedited programs—to avoid costly CRLs down the line?

It's pretty obvious, just given the FDA’s comments here, but control groups where possible. Now, obviously, I do believe that a control group in Replimune’s case would have been unethical, and I think you'd struggle to get an IRB or an ethics committee to sign off on anything like a kind of a conventional RCT with a one-to-one randomization ratio. Now, there are constantly-improving kinds of options around match placebo controls often using AI to create synthetic data, or using a growing trove of real-world evidence to create that match control. I don't believe Replimune included that. That is a relatively low-cost option to try and include, so that is something that sponsors could look into, but it's very different in a space like cardio metabolic or type 2 diabetes, a more chronic condition, it would be very appropriate to include a control group. But again, there are also options which technology enables for us now, like adaptive trials, where you could have an adaptive randomization ratio and start with a one-to-one ratio, and as you notice an effect on the control group, reduce the number of participants who are randomized onto a placebo. Using technology now, you can do that without unblinding the study teams to the results, so those options do exist. Again, relatively low-cost ways of ensuring or preventing these CRL letters. If I were a clinops leader, I'd look into them. It's something that we've been doing for some of our sponsors now. Certainly, we hope that in the near future, every clinical trial will be adaptive by default, and I think there's really no reason not to.

Videos

What the Future Holds for Clinical Trials as AI and Digital Twins Become More Embedded

, Jon Walsh, founder and chief scientific officer of Unlearn, discussed how AI-designed therapies and digital twin technologies are reshaping the clinical trial landscape. While the fundamental principles of clinical testing remain consistent, Walsh explained that AI-discovered drugs often warrant deeper attention to specific biomarkers and safety signals in early development. He also highlighted how digital twins—virtual models of trial participants—can significantly improve trial efficiency, reduce control group sizes, and enhance the precision of treatment effect estimates. As regulatory agencies like the FDA and EMA begin to offer clear frameworks for integrating AI into drug development, Walsh emphasized the importance of transparency, reproducibility, and interpretability in model design and implementation.

ACT: Looking ahead, how do you envision the clinical trial landscape evolving in the next 5–10 years with digital twins and AI more deeply embedded in the process?

One key aspect is the broader adoption of these technologies in clinical studies. We’re currently working with a number of sponsors, primarily in oncology and CNS, but also in other therapeutic areas. We’d like to see this grow, with more organizations using digital twins and AI to improve clinical trials.

In randomized studies, these tools can help make trials more efficient and patient centric. For example, they can increase patients’ chances of receiving treatment by reducing the size of control arms, while maintaining the quality of evidence. It’s essential that sponsors, patients, clinicians, and regulators can all trust the results generated with digital twins.

Another major shift is the ability to run more innovative trial designs. This could include adaptive designs or open-label studies earlier in the development process, supported by digital twins. These approaches can help drugs reach the market faster, give more patients access to treatments sooner, and still allow for high-quality decision-making. Overall, we hope to see continued adoption of these methods across the industry.

 AI-designed therapies can present unique characteristics in clinical trials compared to traditionally developed treatments, often requiring closer scrutiny of biomarker signals and safety data early on. However, the fundamental process of clinical testing remains consistent. Digital twin technology helps make these trials more efficient and insightful by modeling individual trial participants under standard of care, offering a virtual comparator without needing additional enrollees.

Digital twins work by creating a probabilistic model of how a specific patient would respond to standard treatments over time. Rather than enrolling new participants, sponsors gain deeper insights into existing ones, improving the precision of treatment effect estimates and boosting trial power. In randomized controlled trials, this added statistical confidence can reduce participant numbers or accelerate timelines. In open-label studies, digital twins can serve as virtual control arms, allowing meaningful comparisons when a real control group is not feasible.

Regulatory agencies like the FDA and EMA are providing clear guidance on the use of AI and digital twins in drug development. They emphasize defining the model’s context of use, evaluating its risks, and proving its reliability. The companies building digital twins must ensure model development is transparent, reproducible, and interpretable—tracking how models were built, and which data were used, while safeguarding against unblinding.

Looking ahead, digital twins are expected to become a foundational part of the clinical trial landscape, particularly in neuroscience and CNS trials. They offer the promise of more adaptive, efficient, and patient-centric designs that can accelerate drug development and reduce reliance on traditional control groups, while maintaining—or even improving—the quality and reliability of clinical evidence. As adoption grows, digital twins could reshape how sponsors design trials and regulators evaluate new therapies.

Jon Walsh, founder, chief scientific officer, Unlearn, explains how AI and digital twins are helping clinical trials become more efficient, patient-centric, and capable of supporting innovative study designs over the next decade.

Enhancing Clinical Trial Integrity Through AI and Digital Twin Technologies

ACT: How do you ensure transparency and reproducibility when using AI and digital twins in clinical research?

One of the key components of the FDA’s seven-star framework is the ability to evaluate and demonstrate that a model appropriately addresses risks and is aligned with its context of use. In practice, this means that when you build the underlying machine learning models that power digital twins, and when you apply them in studies, the development process must be traceable.

You need to fully understand the data that goes into building the model, how the model was developed, and the decisions made throughout that process. Then, when generating digital twins for a trial, you must be able to demonstrate that this generation occurs in a trustworthy, pre-specified manner. For example, the data must exist ahead of unblinding and cannot be used by sponsors to unblind their own trial data. By satisfying certain criteria around the chain of custody and generation of data, you can trust the results of the analysis.

We have worked hard to build software and systems that enable this level of transparency. Another critical component is interpretability. Regulators are rightly concerned about understanding how predictions are made by AI models in drug development. Applying interpretability tools to digital twins—especially those derived from complex models—helps identify which variables are most important, what drives disease progression, and whether the outputs make sense in the overall clinical context.

AI-designed therapies can present unique characteristics in clinical trials compared to traditionally developed treatments, often requiring closer scrutiny of biomarker signals and safety data early on. However, the fundamental process of clinical testing remains consistent. Digital twin technology helps make these trials more efficient and insightful by modeling individual trial participants under standard of care, offering a virtual comparator without needing additional enrollees.

Jon Walsh, founder, chief scientific officer, Unlearn, explains how AI and digital twin technologies are being applied to improve data transparency, ensure reproducibility, and strengthen the reliability of clinical trial outcomes.

Understanding FDA and EMA Guidance on AI and Digital Twin Applications in Trials

ACT: What are the regulatory implications of using AI-generated data or digital twins in trials?

Regulators have been very clear in their positions and guidance on how to use AI in drug development more safely, effectively, and in line with their expectations. For example, the FDA released guidance earlier this year outlining a seven-step process for applying AI in this context. Similarly, EMA has been developing frameworks, such as its 2024 reflection paper, which emphasize the importance of context of use.

Sponsors need to clearly specify how a model will be used in drug development, identify potential risks and impacts to the study, quantify those risks, and have a plan to address them by evaluating the model under defined conditions.

So far, the primary way we use digital twins in randomized controlled trials has been fully aligned with regulatory guidance. From the outset, we designed our approach to help randomized trials achieve higher statistical power, in a manner consistent with both EMA and FDA expectations. We have received positive feedback from both agencies—through EMA qualification programs and open discussions with the FDA, where we’ve even published their comments.

When it comes to other trial designs, such as open-label studies—where there is more risk and obtaining a control group is often not feasible—there are still clear ways to use digital twins within the FDA’s framework. These methods allow regulators to assess the risks and interpret evidence appropriately, even in these more challenging contexts.

Jon Walsh, founder, chief scientific officer, Unlearn, explains how regulators are clarifying best practices for integrating AI and digital twins into clinical research.

Video Series

In this video interview, Luke Wilson, senior director, biotech, pharma services at Thermo Fisher Scientific, shares key strategies for maintaining operational alignment and compliance in decentralized trials including unified data platforms, aligned SOPs, and clear governance and escalation pathways between sponsors and supply chain partners.

Best Practices for Operational Alignment in Decentralized Trials

, Luke Wilson, senior director, biotech, pharma services, Thermo Fisher Scientific, discussed the role of data visibility in clinical trials, emphasizing the need for actionable data to meet aggressive timelines and budgets. Remote monitoring is crucial for quick decision-making and patient safety, reducing site burdens and leveraging advanced technology. Integrated systems help study sponsors meet evolving regulatory expectations by removing data silos and streamlining inspection processes. Barriers to real-time trial oversight include data silos and the need for integrated systems. Best practices for decentralized trials include unified data management, clear SOPs, and governance pathways to ensure operational alignment and regulatory compliance.

ACT: As trials become more decentralized and data sources multiply, what best practices can ensure operational alignment and regulatory compliance throughout the trial lifecycle?

Yeah, so I think again, unified data management platforms, that's probably number one, establishing clear SOPs and training, again, between sponsor and supply chain companies, making sure that the teams are highly aligned. Another thing that may seem tactical, but it's super important, is creating clear governance and escalation pathways. I think, again, this is something that I really want to double click on, that as an important component of a collaboration between again, sponsor and supply chain partner.

The Power of Digital Twins in Predicting Outcomes for Clinical Trials

ACT: Can you explain how digital twins work in the context of clinical trials and what makes them especially suited for evaluating AI-discovered drugs?

Digital twins is a term borrowed from engineering. In that field, you might have a digital twin of a bridge or a jet engine to understand how the physical object changes over time, and how to maintain or improve it.

In clinical trials, we apply the same concept to individual patients. A digital twin is a model of what’s expected to happen to a patient if they’re receiving standard of care or a specific therapy. It predicts their outcomes probabilistically and models the patient comprehensively over time.

For every patient in your trial, you create a digital twin that simulates what would happen to them under standard of care. This doesn’t mean adding new patients to the trial—it means gaining more insight into the patients already enrolled. Think of a digital twin not as a new participant, but as additional data about each existing participant.

In a randomized trial, your ability to detect efficacy depends on how precisely you can estimate the treatment effect—that is, the difference between the treatment and control arms. Digital twins help here by providing more detailed information about each participant, which can be used in statistical analyses to refine the estimate of treatment effect. That increases statistical power and allows sponsors to run trials more confidently—and potentially with fewer participants.

In early-phase trials or open-label studies, digital twins can also serve as a comparator arm. For example, if everyone in the study is receiving the experimental treatment, the digital twin can model the counterfactual—what would have happened if the patient were on standard of care. That allows you to estimate treatment effect even at the individual level, without having to enroll a separate control arm.

Jon Walsh, founder, chief scientific officer, Unlearn, explains how digital twins provide patient-level predictions that enhance trial precision, reduce enrollment needs, and support AI-driven drug development.

In this video interview, Luke Wilson, senior director, biotech, pharma services at Thermo Fisher Scientific, outlines the key barriers to real-time oversight in clinical trials and explains how integrated systems, built-in analytics, and live dashboards can empower clinops leaders to make faster, better-informed decisions.

Breaking Down Data Silos to Enable Real-Time Trial Oversight

ACT: What are the most common barriers to achieving real-time trial oversight? How can clinops leaders overcome them?

Again, the one that keeps cropping up is data silos. Silos create complexity in a highly complex environment that we're in at the moment. Also, this is something that we encourage our biotech customers to really think about is how do we integrate into their systems to enable the successful outcome of their trials? I'm also thinking about how do we prioritize tools and built-in analytics and dashboards, again, so we're giving people live information that they can leverage.

Using Digital Twins to Improve Efficiency and Accuracy in Clinical Research

ACT: How are AI-designed therapies fundamentally different from traditionally developed treatments when they enter clinical trials?

At Unlearn, we help sponsors run faster, more efficient trials, and we’re agnostic to the type of treatment. What we do is build digital twins of clinical trial participants—models that simulate how patients would respond to standard of care.

That said, when you're working with an AI-designed therapy in a trial, there are often specific considerations. For example, the compound may have been designed to target a particular mechanism of action, so you might pay closer attention to biomarker signals in early-phase studies than you would with a traditionally developed drug. Additionally, these AI-designed therapies may benefit from more advanced computational safety models, allowing for a greater focus on efficacy even in early trials.

In those scenarios, we use our technology to help detect early signals of efficacy and enable faster decision-making. This helps sponsors determine more quickly whether a compound is viable in terms of both safety and efficacy.

Jon Walsh, founder, chief scientific officer, Unlearn, explains how AI-designed therapies and digital twin technology are accelerating clinical trials, improving data precision, and reshaping early-phase drug development.

In this video interview, Luke Wilson, senior director, biotech, pharma services at Thermo Fisher Scientific, discusses how integrated systems and data transparency enable sponsors to meet evolving regulatory expectations and streamline FDA inspections through enhanced operational readiness.

How Integrated Systems Help Biotechs Stay Inspection-Ready

ACT: What role do integrated systems play in helping sponsors stay inspection-ready and meet evolving regulatory expectations?

 You mentioned regulatory. I think the market is changing rapidly in front of us. If you think about how data is captured, how data is managed within certain regions and even therapeutic areas, it’s really important that we remove some of those silos to enable data to be captured and logged in an appropriate way that then can be provided to the sponsor. We actually had a recent example with a customer, they were a small biotech, and they were about to have an FDA inspection, and they commented on how some of our teams at Thermo Fisher Scientific captured the data, how we provided it to them. It really helped streamline the process of that FDA inspection, and resulted in a very successful inspection for them, which is critical. You imagine biotechs, they've got to have partners who can enable them to be inspection ready to your question, and it's really important that we support them on that journey with our expertise within Thermo Fisher Scientific.

In this video interview, Luke Wilson, senior director, biotech, pharma services at Thermo Fisher Scientific, explains how clinical operations teams can use remote monitoring and connected technologies to reduce site burden, respond faster to emerging data, and improve both patient safety and trial outcomes.

How Remote Monitoring Enhances Trial Efficiency and Patient Safety

ACT: How can clinical operations teams leverage remote monitoring to make faster, more informed decisions without compromising data quality or patient safety?

It's really important with clinical trials at the moment that we have the opportunity to act quickly on data. Again, patient safety is critical. If we think about certain sites that we have to closely monitor the performance and the safety of the patient, it’s really important that we can leverage remote monitoring, so there's that live connection with sites. If we think about improving the patient outcome and the patient experience, this is front of mind, but also secondary to that, where I think it's really important that we think about the clinical trial sites as well, and reducing the burden as much as possible. That coupled with some of the latest and greatest technology, where there are certain devices and platforms that we can leverage, I think this seems to be an expanding area of the market and an expanding opportunity to optimize clinical trials.

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