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Join us for an insightful webinar on antimicrobial resistance (AMR), tailored for experts in clinical development.

https://www.appliedclinicaltrialsonline.com/act_w/future-of-amr

Join us for an insightful webinar on antimicrobial resistance (AMR), tailored for experts in clinical development. We’ll kick off with an introduction to AMR, setting the stage for a deeper understanding of this critical issue. Following this, a summary of key highlights from the September 26 high-level meeting at the United Nations on AMR will be provided, offering a sneak peek into the latest developments and discussions in the field.

The session will culminate in a dynamic panel discussion, featuring leading voices from scientific and regulatory backgrounds. Experts will delve into the challenges faced in clinical research and explore innovative ways to advance AMR. Don’t miss this opportunity to engage with thought leaders and gain valuable insights into the future of AMR solutions.




Latest developments: Learn about the most recent advancements and discussions from the AMR meeting at the United Nations, providing insights into global efforts and strategies to combat AMR.


Innovative approaches: Delve into innovative approaches and breakthroughs to advance scientific understanding.


Future directions: Explore the challenges and opportunities in clinical research for AMR therapeutics, with expert perspectives on overcoming regulatory hurdles and ensuring compliance to drive progress.


Principal – Antimicrobial Resistance, Global Public Health

is an infectious disease epidemiologist by training with over 15 years of experience working in the field of antimicrobial resistance (AMR). She has held positions across several sectors, including health care, academia, public health, and industry. Freeman is passionate about incorporating data into solutions designed to tackle AMR and has spent the majority of her career implementing surveillance systems and optimizing data collection. Freeman is leading IQVIA’s AMR strategy, including the development of innovative solutions and offerings that integrate real-world data into decision-making. She is an advocate for the need for cross-sectoral and transdisciplinary work in addressing the threat posed by AMR and is leading engagements with national bodies and the life sciences industry in support of generating valuable data assets that may be used to provide powerful insights.




Senior Therapeutic Strategy Director, Infectious Diseases and Vaccines

is a senior director of therapeutic strategy in infectious diseases and vaccines, with over 29 years of industry experience spanning both clinical research and R&D formulations. She has a deep focus on respiratory infectious diseases, specifically on community-acquired and ventilator-acquired pneumonias, as well as studies involving antimicrobial resistance and invasive fungal infections in vulnerable HSCT and immunocompromised populations. She has over a decade of experience managing clinical trials and developing therapeutic strategies around VABP/HABP/CABP and VRE/CRE-related infection protocols.


Webinar Date/Time: Tuesday, November 12th, 2024 at 10am EST | 7am PST | 3pm GMT | 4pm CET     

Webcasts: Regulatory, Other

Webcast: Therapeutic Areas

Webcasts: Risk-Based Monitoring

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Navigating the Future of AMR: Insights and Innovations in Clinical Development

IQVIA is transforming clinical trials through patient- and site-focused solutions that enhance site capabilities and improve outcomes throughout the trial process. This session will delve into cutting-edge strategies and technologies aimed at boosting recruitment, providing targeted support to address enrollment and retention challenges in a competitive landscape, and delivering tangible improvements in strategic scenarios and solution recommendations tailored to meet customer needs.

https://www.appliedclinicaltrialsonline.com/act_w/novel-support

Join us for an insightful webinar where we will delve into how IQVIA is transforming clinical trials with patient- and site-centric solutions. This session will highlight the innovative strategies and technologies that empower sites throughout the trial journey, ensuring delivery as promised to the sponsor. Discover how our structured approach enhances recruitment, mitigates risks, and reduces site burden, ultimately leading to successful clinical trials.



Innovative strategies and technologies: 

Explore the latest tools and methods we use to enhance clinical trial processes.


Learn how our solutions help attract and retain participants effectively.


Understand how we mitigate risks and ensure smooth trial operations.


See how our strategies deliver tangible results tailored to your needs.


is a highly accomplished leader with over 30 years of experience in the clinical research industry. She joined IMA in 2023, bringing her knowledge from senior-level roles in global clinical operations, project management, and strategic partnerships at industry-leading organizations. Throughout her career, she has excelled in building and scaling high-performance teams, optimizing processes, and developing strategic alliances that deliver measurable results.

Senior Vice President Patient and Site Centric Solutions 


- With a career spanning over 20 years in the healthcare and life sciences industry, she excels in overseeing large global teams, P&L management, revenue growth, and C-Suite Relationship Management. Alison's expertise extends to Digital & Change Transformation projects in the data and patient space within the Life Science industry. Passionate about creating efficient ways of working and utilizing technology to drive outcomes, she has held executive positions at ICON plc. and Accenture contributing significantly to their patient, site, and data strategy implementation that helped aide in accelerating drug delivery.

Webinar Date/Time: Thursday, November 21st, 2024 at 11am EST | 8am PST | 4pm GMT | 5pm CET    

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Novel Support for Sites: Ensuring Clinical Trial Delivery and Sponsor Confidence

In an interview with Nico Saraceno at DPHARM 2024, Munther Baara, VP strategy and innovation, EDETEK touches on the features an effective AI/ML model can provide and how they benefit data management in trials.

DPHARM 2024: Developing AI/ML Models for Clinical Trials

ACT: You have an upcoming session on “Transforming Clinical Trials: From Reactive to Predictive.” What are some of the main points that will be discussed during the session?

 I'll be talking about the metaphor of travelers. Back in the days—it depends how old you are—you might remember when people used to travel, they'll have a map with them. There'll be a passenger next to them with the highlighter trying to map their route, and if they miss an exit, it will probably take them another hour to come back and then we progressed to the MapQuest, where we printed the map, and then we end up with the GPS, and then we end up with a smartphone, and then became a gamification with ways and now we're getting into the predictive analytics to basically predict how you get to do your next day and probably telling you what you should be doing, etc. I see this like in our clinical trials, there's some companies that are still using the paper, navigating with the highlighter, there's people at the GPS, and once a new study with different endpoints, it becomes harder and harder for them, basically, to navigate and be able to see the big picture.

What I'm going to talk about is very simple concept. We call it the four Cs—connect, collect, conform, and consume. What we've done, we’ve built this whole architecture to allow you to connect to the different data providers at the site level or at the study level, so that gives you the ultimate flexibility. You could use different ADC vendors, central labs, ePRO, new digital endpoints, genomics, data, etc., it doesn't matter. The idea behind it is those data providers will immediately be basically pushing data into what we call our digital data pipeline. Then, once the data starts flowing in, you'll see the data coming in and then conforming to the standard of your liking, whether it's for submission or for data review, and then provide you the final piece, which is what we call the consume piece. That's what's so powerful, because now you have to think about it like, you come to a building, you expect light, electricity, and water, right? You don't think about it. Once we do these hooks into the building, into your clinical trial, into our digital data pipeline, things start flowing, and now you're seeing everything in one place, one version of the truth. There are no delays because typically study managers tell me my study was great—green for six, nine months, the first DMC data monitoring committee meeting, I need to do the cleanup or interim analysis—that's when things start basically going south. It went immediately from yellow to red, and I see I have the wrong patients in the study, etc. We solve this problem from day one. That's the key. Study starts. Data starts flowing in. You're seeing whether you are enrolling the right patients into the study or not.

ACT: From a technological standpoint, what’s required for the development of more accurate AI/ML models?

 That's a good question. I struggle a lot of the time when people talk about AI and machine learning, and I feel like they have not done the foundational pieces that's needed in place. Of course, you need AI and modeling and machine learning, but you’ve got to think about: you need to be able to curate the data, aggregate the data, and you need to make sure the data is validated with high quality, so not only curate, but cleansing of the data. The 4 Cs concept that I described does exactly that because we bring data in real time, we basically run all these validation checks, and we have what we call flexible validation rules that you can do through and point and click. Zero programming involved. It eliminates SAS coding, et cetera, especially when you cross data across different data providers. I can look, for example, like somebody says in the exposure data, we adjusted the treatment, and then I look in adverse events, there is no reason for adjustment, et cetera. I can basically cross reference a lot of data. I can cross reference lab collection with the actual lab results, the ePRO data, with how the patient reported safety versus SAE, so I can do that all in coding and then we can apply algorithms as well to monitor the quality of the data. As the data comes in, you immediately point out where there's weak points or what needs to be addressed and tackled, and you’re cleaning the data as you go. That gives you the foundation to have excellent AI and machine learning, basically models in place in order to respond to this. Without it, your model is going to be weak. You're going to be missing a lot of pieces, and not only that—which I forgot to talk about—is the metadata about the data. In order for machine learning and AI to increase the accuracy of it, you need to have the data about the data and that's what our platform has. We can take raw data, transform it to any target data, like from the EDC collection to the CDISC SDTM format that's ready for submission for the regulatory agencies, so that together is needed in order to be able really to get into the next level.

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Increased interest in cell and gene therapies has led to a greater demand for expertise to help sponsors, CROs, and sites coordinate human gene transfer (HGT) research. Compared to clinical trials with older, more traditional classes of drug products, there are important similarities and differences in requirements for safe and ethical conduct of clinical trials of gene therapy and related modalities.

Transforming Research: The Intersection of Ethics, Biosafety, and Innovation

Artificial intelligence (AI) has had an undeniable impact on society in the past few years, having fundamentally changed how people from diverse industrial sectors do their jobs day to day. Drug discovery has been the focus of pharma attention.

Last month alone, we have seen Insilico announce its lead candidate has met its primary endpoints and is in the process of designing a Phase IIb trial, while a DeepMind spinout, Isomorphic Labs, has just issued £182 million in new shares. It’s also worth looking at the impact of AI for trial management and clinical efficacy, an area still in its infancy, as we think this could grow into an even bigger force, fundamentally transforming how we approach drug development.

There are many areas in which AI could improve speed, reduce complexity, and consequently improve efficiency in workflows and costs. From design and trial start-up to conduct and analysis, there is enormous potential for applications of AI within clinical trials to have a profound impact on human health.

One of the best areas for AI to improve clinical research is in site feasibility and selection. Picking the right sites optimized for the eligibility criteria and trial specifications is perhaps the single most important decision impacting the success or failure of the trial because of the causal impact on enrollment rates.

Trials that don’t enroll quickly enough risk failure to provide the statistical power needed to conclude the drug under investigation is safe and effective, and this remains a top cause of trial failure today. AI prediction engines, which use large language models (LLMs) to read a protocol, understand the context, and select the “best” sites for the protocol, are starting to become available.

Yet these engines are, at best, on par (and sometimes worse) than human performance on the same task. The reason is that there is a lack of available site performance data aligned with the key metrics that would help drive successful predictions.

The key to overcoming this is existing data that can be used for training. We use data pipelines that aggregate KPIs and raw data from past protocols, while obviously being careful to clean these data pipelines to ensure that no sensitive or client confidential information is inappropriately disclosed.

This is something contract research organizations (CROs) and sponsors could learn from and attempt to implement. Another approach to improve reliability is to build the AI model along with publicly available data to make better predictions.

Taking this a step further, we can apply machine learning (ML) techniques that can incorporate feedback from trial outcomes to help improve site feasibility and selection, and the more we do this as an industry, the bigger the data pool and the greater the accuracy.

The next usage for AI is to help shorten timelines before and after the study conduct period. As an example, the protocol authoring process is slow and is ripe for disruption.

Medical writers prepare to author protocol drafts by reading synopses and relevant medical information about the disease being studied, and then start the authoring process with a protocol template using past protocols as examples. What if this research gathering can be conducted at scale by an AI, using a standardized and templated approach?

For example, LLMs can synthesize large amounts of information and, using carefully crafted prompts, can autofill a draft much as a medical writer would. Good training data can make these drafts very accurate and then can be passed to a human editor to ensure a quality protocol is written, essentially removing the grunt work from the equation and leaving trial designers to add experience and finesse to protocols.

It’s like how junior lawyers will create base drafts for senior partners to tailor. Similarly, AI prediction engines can estimate site and patient burdens by comparing protocol drafts to past protocols, ingesting aggregated performance data from those protocols, and summarizing them for the author. The writing team can use these to consider possible design changes to the protocol that would reduce burden and the AI could also suggest such changes.

By our best estimates, we believe that protocol digitization capabilities can reduce build times by as much as 30%, and we invested in this recently. Our approach was to turn unstructured protocol documents into structured Excel files, in which each important element is extracted into a format that is both human- and machine-readable. This Excel file can be loaded into various EDC build systems. It’s a simple idea in many ways but provides the standardization needed to have the capability to build complete models.

The next step is to build a “metadata ingestion capability” into your eClinical platform. Putting the jargon aside, this means extracting structured data elements from the protocol, such as visit schedules, cohorts, and case report form data elements, and feeding an algorithm that can then automatically generate electronic case report forms, visit schedules, edit checks, and other database configurations. Once the data is cleaned in this way, you start to reap the rewards of the technology, but you must build this in from inception.

Using this approach, the database build stage can potentially be completed weeks earlier than before, ensuring systems are ready in production as soon as sites get activated. Another application of AI is to help shorten the time to database lock and delivery of the clinical study report.

For example, in mid-2022, we achieved automatic data standardization to a relational format based on CDASH with SDTM-controlled terminology incorporated. The benefit here is that the automation does about 95% of the mapping that’s required for an FDA submission (fully SDTM).

More generally, having standard data available upfront, and in real time after collection, is a huge boon to data management. This is because it enables you to write edit checks using the standard data instead of study-specific data dictionaries, which leads to much greater reuse of library content and helps find similar and/or numbers of issues in the data with fewer checks needed.

In layman’s terms, it provides greater reusability of code for reports and analyses, meaning the lessons of previous trials stay learned. When you put all this together, you can build a portfolio of interactive reports and pipelines that automate the generation of tables, figures, and listings. Our estimate is that you can reach your analysis-related milestones in half the time it took without such technology.

Another example is risk-based monitoring and quality management (RBQM). Here, the goal is to quickly identify problems in clinical trials, allowing for timely adaptation and intervention when necessary.

AI-enhanced tooling can optimize the risk-based assessment and mitigation plans for a trial, given the protocol, critical data, and processes, by learning from across trials. It can then help automatically detect issues in data quality, compliance, and safety. This real-time monitoring of identified risks is tied back to the mitigation plan and allows for timely intervention, heavily reducing inefficiencies in addressing underlying operational risks in a trial.

Emmes, in fact, famously used risk-based monitoring technology way back with the H1N1 pandemic in 2009, so it has some pedigree. AI models can take this much further and enable us to detect risk across sites or based on specific people involved in trial operations, driving adjustments to the monitoring strategy to ensure the utilization of available resources at the sites with higher risk. The AI acts as the “big brother” always watching, and in clinical trials, that’s a good thing.

While it’s tempting to look at the potential of AI in clinical trials with rose-colored glasses, the reality is that AI tools are still only as good as their training (and prompts, for LLMs). Evaluating the performance and reliability of an AI platform in clinical trials requires building guardrails into AI solutions, which is a must-do from a safety and ethics stance.

For example, engineered prompts can control response quality and independent AI instances can critically review and evaluate correctness and safety risks. Solutions that deliver medical knowledge are designed to ensure the presence of a human reviewer in the loop to ensure patient safety, maintaining the vital human accountability in the process.

From a regulatory point of view, AI doesn’t change the requirements for auditability or traceability in the data, nor does it change who is accountable for each aspect of the clinical trial. The biggest challenge to growing AI use is acceptance and the change required by its users.

In summary, AI will be most effective when it augments human capabilities, and to achieve this, we need to consider not only the technology, but also how it fits into workflows and how to upskill clinical CROs to get the most out of it. Over the next five years, we will see AI solutions design better studies, reduce site and participant burden, accelerate information exchange, reconcile data, and automatically draft patient profiles and clinical study reports.

We should all be rooting for these solutions, as improving technology will allow more clinical trials to be run, deliver results more quickly, and ultimately lead to faster treatments and cures for diseases that impact all of humanity.

Noble Shore, VP, Technology Strategy & Product Adoption, Veridix AI.

From design and trial start-up to conduct and analysis, there is enormous potential for applications of artificial intelligence within clinical trials to have a profound impact on human health.

AI Already Starting to Deliver Faster, Safer, More Effective Clinical Trials

As the clinical trial recruitment landscape continues to evolve, patient recruitment remains a critical challenge. More innovative screening strategies can improve recruitment and enhance the quality of clinical research.

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