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Improved diversity in clinical trials can be achieved by exploring opportunities for research in developing countries that have not historically participated in large-scale, industry-sponsored clinical trials. 

Clinical Trials in Indonesia: Challenges and Opportunities for Industry Sponsors

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.

Latest Conference Coverage

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.

Better Qualification Strategies, Better Patient Recruitment Outcomes

To date, there is limited insight into the perspectives on eConsent in clinical trials of Independent Ethics Committees/Institutional Review Boards, collectively referred to as Ethics Committees (ECs) in this article, as well as into the opportunities and barriers faced by sponsors (commercial, non-commercial) and vendors (technology companies, CROs). The European Forum for Good Clinical Practice (EFGCP) eConsent initiative, comprised of over 50 companies, has conducted two surveys to gain a comprehensive understanding into behind-the-scenes perspectives of ECs and sponsors/vendors. This article represents the key findings of both surveys and detailed results can be accessed through the supplemental information section. 

The importance of the right understanding about eConsent

A correct and aligned understanding of eConsent is crucial to ensure that all stakeholders are on the same page. For this purpose, the EFGCP eConsent initiative has developed a comprehensive glossary of eConsent terms including over 60 harmonized eConsent platform and operational aspects. This resource equips stakeholders with a unified language and the necessary knowledge and terminology.

 The glossary was incorporated in the introduction of both surveys.

To recap, ‘eConsent’ is an overarching term used to represent the traditional informed consent process, supported by one or more digital features.

 A visual representation of this eConsent definition and some examples of digital features are shown in Figure 1. There are many different eConsent models, there is no one-size-fits-all eConsent model. Each study, each indication, each site, and each participant might have its own needs and requirements.

1. EC eConsent survey respondents characteristics

49 EC respondents from 15 different countries completed the EC survey of which 53% were from Europe, and the rest were divided between Russia (20%), North America (18%), and Malaysia (8%), as shown in Figure 2.

The level of experience with eConsent was variable:

61% had reviewed and approved/rejected eConsent.

35% had never been asked to approve eConsent

A substantial proportion of ECs respondents had previously been asked to approve eConsent in North America (78%), in Malaysia (75%), and in Europe (65%). In contrast, only 30% of Russian EC respondents have been asked to approve eConsent.

2. Preserving human Interaction with eConsent: The most important factor for ECs

The most important driving factor for EC respondents (43%) to approve eConsent was preserving the interaction between investigator and participant.

Participant-investigator interaction is a foundational Good Clinical Practice (GCP) of informed consent.

 In addition, while some Health Authority guidelines (e.g. EMA) recommend a physical interaction, a remote interaction can also be justified in some cases, opening alternative modalities for this investigator-participant interaction.

EC respondents further scored patient-centricity (22%) and regulatory acceptance (18%) as important driving factors to approve eConsent. Of relatively smaller relevance for EC respondents was the knowledge that eConsent is accounted for in site guidance documents (2%), that there is an easy access to technology, training and documentation (2%), and that it enables decentralized trials (2%).

3. eSignatures acceptability by ECs: Various factors play a role

The interpretation of eSignature terminology varies greatly across the globe. A handwritten signature drawn by finger or stylus on an electronic device is considered an eSignature according to European eIDAS regulations, but NOT according to FDA regulations. Hence, we recommend describing the signature process in detail to ensure it is clear for all stakeholders.

 In the surveys, we used the eIDAS eSignature terminologies: Simple eSignature, Advanced eSignature, and Qualified eSignature.

The EC survey results showed that eSignature requirements became more stringent when moving from on-site workflows to remote workflows conducted via televisit or via phone call.

As illustrated in Figure 3, an eIDAS Simple eSignature was acceptable:

for Phase I-III interventional studies by

47% of EC respondents when eConsent was conducted on-site

11% of EC respondents when conducted remotely via televisit

7% of EC respondents when conducted remotely via phone call

Regional difference existed with an eIDAS Simple eSignature on-site being acceptable by 53% of European EC respondents versus 33% of North American EC respondents, and 13% of European EC respondents requiring a wet-ink signature versus 22% of North American respondents. More details can be accessed through the supplemental information section.

4. Offering both paper and eConsent: A key recommendation of ECs

The EC survey posed a forward-looking question to gauge the sentiment on providing consent only electronically. 61% of EC respondents did not agree with this. North American EC respondents had the highest score (78%), followed by European (65%), and Russian EC respondents (60%). On the contrary, Malaysia seemed to be more supportive for providing consent only electronically with 75% agreement. The emphasis on patient-driven choices and diversity and inclusion without additional biases is presumed to be driving the results. Providing options for participants and sites is important to avoid creating a new diversity barrier for participant populations that lack basic digital literacy skills.

5. The site: Preferred location to store personal data for ECs

When it comes to data storage and residency requirements, 61% of EC respondents recommended that personal data should be hosted at the investigator site. Regional differences existed with 77% of European EC respondents recommending the on-site hosting of personal data versus 60% of Russian, 44% of North American, and 25% of Malaysian EC respondents.

While EC respondents expressed a preference for on-site hosting, it must be noted that not all sites may have the necessary infrastructure or manpower to support on-site data hosting. Without any doubt, participant data and participant personal data protection falls under the responsibility of the site, but there are also other ways that this can be achieved. For example, by validated, secure and controlled data encryption, data transfer and data access, while maintaining and offering a more harmonized approach and access to eConsent for sites and their participants.

6. Limited impact on monitoring or archival requirements expected by ECs

The majority of the EC respondents indicated that there is no difference in monitoring requirements compared to paper (90%), nor that eConsent archival requirements exceeded GCP requirements (80%). Half of the EC respondents (51%) indicated that they had an ICF guidance document available, of which 60% indicated that no updates were needed to cover the use of eConsent.

Sponsor and vendor eConsent survey: Key highlights

1. Sponsor/vendor eConsent survey respondents characteristics

In total, 42 respondents completed the sponsor/vendor eConsent survey with 67% sponsor and 33% vendor respondents.

As expected, vendors reported a higher eConsent experience compared to sponsors: 36% of sponsor respondents had no eConsent experience versus 7% of vendor respondents.

2. Compliance and patient-centricity: The most important drivers for sponsors and vendors

90% of sponsor/vendor respondents scored improved compliance and quality of the consent process as the most important factors driving the use of eConsent. That number increases to 96% if we consider only the sponsor representative responses. Patient-centricity was another important factor, scoring 86% across the combined sponsor/vendor group and it was even the number one factor considered by vendors. Other important factors, ranging from 50% to 60%, were enabling decentralized trials, integration with other clinical systems, reduction of dropout, and improvement of recruitment rates.

3. Site adoption and regulatory approval concerns: The most significant barriers for sponsors and vendors

Poor site adoption was considered the most significant barrier by sponsor respondents (61%), whereas vendor respondents noted regulatory approval concerns (43%) as the most important barrier. High cost was ranked as the third significant barrier by both sponsors (36%) and vendors (29%). Lack of organizational delivery structure and process (32%), challenges with an eConsent platform (29%), and delay in timelines (29%) were considered as significant barriers by sponsor respondents but limited or not even seen as a barrier by vendor respondents, as illustrated in Figure 4.

4. Video: A valuable yet underutilized eConsent digital feature by sponsors and vendors

The video digital feature perceived the highest score (79%) by sponsor/vendor respondents on the value matrix of digital features, followed by dictionary/glossary (48%), content flags (45%), knowledge checks (33%), and links to other websites (33%). Comment boxes (17%) and chat boxes (10%) were less frequently rated as extremely useful digital features.

The frequency of deployment of digital features did not necessarily mirror the usefulness perception. As shown in Figure 5, 79% of sponsor/vendor respondents considered the video as extremely useful but only 57% had used it. Similarly, some digital features perceived as less useful (e.g. chat box, comment box) were deployed by ~30% of sponsor/vendor respondents.

5. Live video call with investigator: The most common used remote consent authentication methods

Live video call with investigator (43%), two-factor authentication (33%), and a Qualified Trust Service Provider ID verification (21%) were reported by sponsor/vendor respondents as the most commonly used Participant Authentication Methods for remote eConsent. Exchange of random code in conjunction with a phone call (10%) and CAPTCHA (5%) were less commonly used. A high number of sponsor/vendor respondents (45%) also selected “Other,” which included methods such as signatures collected at the site, or post office verification, as well as some respondents indicating that they had no experience with the topic.

Submission requirements aligned among ECs, sponsors, and vendors

Both EC and sponsor/vendor respondents evaluated the EC submission requirements. System privacy and security documentation (69%), attestation that eConsent content is identical to paper content (55%), system-printed PDF of the document (55%), and screenshots of digitized content (51%) were highest scored by EC respondents, followed by story boards of multimedia content (33%) and access to the eConsent platform (29%). Comparable categorizations were given by sponsor/vendor respondents although they gave a higher score for screenshots of digitized consent (67%) and storyboards of multimedia content (48%).

Conclusion: One step closer towards transparency and clarity

Creating transparency in perspectives by directly inquiring with the involved stakeholders, instead of making assumptions on their behalf, is overall considered the best approach but in practice not so often implemented. The survey results presented in this article directly present the views of ECs, sponsors, and vendors.

What we learned is that, not surprisingly, the most important items for ECs are:

ensure the participant-investigator interaction is not impacted

ensure a paper option is available to enable broad inclusiveness

ensure participant data and identity are securely stored and protected

There were clearly differences between European and North American EC respondents, but some were much less profound or not as expected with some examples shown in Figure 6. More examples and details can be accessed through the supplemental information section.

Similarly, sponsor/vendor survey results showed alignment but also differences, especially when it comes to drivers and barriers. For example, ~30% of sponsor respondents considered challenges with an eConsent platform, delays in timelines, and lack in organization delivery structure and process to be a significant barrier, while this was limited or not seen as a barrier by vendor respondents.

To note, survey results represent only a small fraction of the global community. In addition, to date, there are still many different interpretations and misunderstandings of what eConsent is about, which might have impacted the responses given by the different stakeholders.

The survey results described in this article represent another outcome of the EFGCP eConsent initiative, one of the largest and most collaborative industry efforts in eConsent. Other tools developed are:

: covering over 60 eConsent platform and operational aspects terminologies, definitions and examples

: covering recommendations for nine different study documents on various eConsent platform and operational aspects

: providing a 5-process flow to define and design the right eConsent for a particular study and its stakeholders, and to analyze and generate effective and comparable study data on eConsent

Upfront interactions on eConsent with key stakeholders, such as Ethics Committees, remain also very valuable as previously recommended by the involved stakeholders.

Key to remember is that there is no one-size-fits-all eConsent model. Each study, each indication, each participant, and each site might have its own requirements. The suite of eConsent tools listed above is publicly available on the 

 and we hope you will join our journey to bring eConsent to the place it deserves.

The detailed results of the ECs eConsent survey and sponsors/vendors eConsent survey are available at the EFGCP eConsent 

This significant work would not have been achievable without the dedication and support of the EFGCP eConsent database workstream team. We would also like to express our gratitude to all the organizations that participated in the online surveys.

 (EFGCP, TCS) spearheads the EFGCP eConsent Initiative consisting of 6 workstreams. 

 (Biogen) is a co-leader of the EFGCP eConsent Database workstream responsible for this article. 

(Freelance consultant) are members of the Database Workstream.

https://www.appliedclinicaltrialsonline.com/view/econsent-why-language-matters

Glossary of eConsent Terms. EFGCP eConsent website, 5 July 2024. 

https://efgcp.eu/public/EFGCP%20Glossary%20of%20eConsent%20Terms.pdf

ICH Harmonized Guideline, Good Clinical Practice (GCP) E6(R3), Draft version 19 May 2023. 

https://database.ich.org/sites/default/files/ICH_E6%28R3%29_DraftGuideline_2023_0519.pdf

HMA, EC & EMA, Recommendation Paper on Decentralized Elements in Clinical Trials, Version 1, 13 December 2022. 

https://health.ec.europa.eu/latest-updates/recommendation-paper-decentralised-elements-clinical-trials-2022-12-14_en

EU Regulation on Electronic Identification and Trust Services for Electronic Transactions in the Internal Market (eIDAS), July 2014. 

https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2014.257.01.0073.01.ENG

Digital literacy in the EU: An overview. 6 December 2023. 

https://data.europa.eu/en/publications/datastories/digital-literacy-eu-overview

Navigating eConsent Submissions: Who, What, Where and Why? 

https://www.appliedclinicaltrialsonline.com/view/navigating-econsent-submissions-who-what-where-and-why-

eConsent Study Documents Recommendations. EFGCP eConsent website, 5 July 2024. 

Using surveys to collect various viewpoints on eConsent from ethics, sponsors, and vendors.

Lena Low; Martin Lim; Terttu Haring; Serena Chan; David Gebbo; Lucia Rizka Andalucia; Roy Himawan; Indri Rooslamiati; Dona Arlinda

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