Journal for Clinical Studies – ISSN 1758-5678 is published quarterly by Senglobal Ltd.
4 FOREWORD
WATCH PAGES
6 A CRO Should be the Partner that Gives Full Visibility on a Gene Therapy Trials Path
In depth analysis about how CROs can reshape gene therapy trials in ophthalmology. There is a growing importance of CROs as strategic partners as they have valued advantages in transparency, therapeutic expertise, and collaboration. Saqib Parkar of OptymEdge explains the significance and possibilities for enhancing success in gene therapy trials, as well as clinical advice on regulatory limitations and the use of CRO’s.
REGULATORY AFFAIRS
8 Accelerating Safely Toward an Efficient Ethical Review Framework
Ethical review of clinical research, enforced by Institutional Review Boards (IRBs), protects participants but can delay studies and increase costs. Balancing speed with thorough oversight is crucial. WCG’s Kelly Fitzgerald explains how innovations like SMART IRB and clear communication improve efficiency. While delays are costly and external, unbiased ethical review remains essential to prevent misconduct and ensure patient safety.
MARKET REPORT
12 EU Policy Momentum Sets the Stage for Clinical Trial Breakthrough in 2025
Europe’s clinical trial regulations are rapidly evolving, driven by GDPR, the AI Act, and ACT EU. Biopharma firms must navigate tighter compliance, data transparency, and AI oversight. Werner Engelbrecht at Veeva Systems examines how clean, connected data systems and validated AI tools will be crucial for operational efficiency and regulatory alignment.
Rare disease drug trials face challenges due to small, diverse patient populations. Traditional RCTs are often impractical, prompting innovative approaches like real-world evidence, digital biomarkers, Bayesian statistics, and N-of-1 trials. Jennifer Visser-Rogers of Coronado Research highlights how these flexible, patient-centred designs improve efficiency and trial outcomes, accelerating access to personalised therapies while maintaining scientific rigour and regulatory credibility.
The opinions and views expressed by the authors in this journal are not necessarily those of the Editor or the Publisher. Please note that although care is taken in the preparation of this publication, the Editor and the Publisher are not responsible for opinions, views, and inaccuracies in the articles. Great care is taken concerning artwork supplied, but the Publisher cannot be held responsible for any loss or damage incurred. This publication is protected by copyright.
Volume 17 Issue 2 Summer 2025, Senglobal Ltd.
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18 Exploring Updates in Adaptive Trial Safety Monitoring
The FDA’s 2024 draft guidance on Data Monitoring Committees (DMCs) addresses the complexities of modern, adaptive clinical trials. ICON’s Patricia Braschayko, Birgit Geiger and Karen Shaffer discuss how sponsors must adopt clear governance, robust infrastructure, and expert support to maintain trial integrity and regulatory compliance in adaptive designs.
20 Evaluating the Role of Biomarkers in Oncology Research using Multiplex Immunoassays
The impact of integrating biomarkers into oncology research has revolutionised the detection, diagnosis and treatments of cancer. Vanitha Margan of Bio-Rad Laboratories explains the advantages of incorporating biomarkers into clinical decision-making, specifically using multiplex immunoassays to examine dozens of immune markers simultaneously. Margan sites the transformative potential multiplex immunoassays can have how we study and overcome challenges in oncology research.
THERAPEUTICS
24 Prostate Cancer Screening: From PSA Tests to Lateral Flow Tests
Prostate cancer affects one in six UK men, with cases set to double by 2040. Current PSA tests are inaccurate and strain healthcare resources. A national screening programme using accurate, low-cost lateral flow tests could improve early detection, reduce deaths, and decentralise testing. Dave Taylor at Valley Diagnostics explains stresses how urgent action is needed to transform prostate cancer outcomes.
TECHNOLOGY
26 Modern Data Management: The Foundation for Life Sciences Innovation
Data is now central to innovation in life sciences, driving drug discovery, precision medicine, and operational efficiency. As data volumes grow and regulations like the EU AI Act tighten, companies must consolidate fragmented data, ensuring governance, security, and compliance. Rohit Dayama at Cognizant delves into how modern data management systems, enhanced by AI, are vital for accelerating R&D and fostering trust in a rapidly evolving landscape.
28 EMA PMS: Capitalising on Centralised Medicinal Product Data – Which Way Now for Regulatory Leaders?
By 2026, EU pharma companies must align with EMA’s Product Management Services (PMS) under ISO IDMP standards. Despite promises of streamlined, global data harmonisation, challenges like manual data enrichment and system gaps remain. Michiel Stam from MAIN5 highlights how companies should balance short-term fixes with long-term, cross-functional data governance to futureproof compliance and unlock operational, regulatory, and patient care benefits.
30 The Potential for Deep Learning Technology in Clinical Trials
Deep learning technology in clinical trials has the ability to speed up drug development and distribution, ultimately impacting both patients and clinics positively. Muhunthan Thillai of Qureight, highlights the benefits of AI development and the possibilities it offers for clinical trials. Ranging from spotting patterns, linking
disease outcomes, analysing images quickly and improving clinical trial workflow to process and improve outcomes of drug discovery.
CLINICAL
MANAGEMENT
32 More Insight from Fewer Patients: Advancing Rare Disease Trials with the Net Treatment Benefit
Rare disease trials face challenges such as small patient populations and complex, varied outcomes. Traditional single endpoint designs often overlook multidimensional treatment benefits. Tomm Mann of One2Treat highlights that by involving stakeholders early, Net Treatment Benefit (NTB) enhances trial relevance, reduces patient burden, and strengthens decision-making for regulators, health technology assessments, and clinical development.
34 The Rare Voice that Matters Most
Patient and public involvement is vital in rare disease research, ensuring studies reflect real patient needs. Early collaboration with advocacy groups improves study design, recruitment, and outcomes. PHARMExcel’s Katie Howe and Benedicta MarshallAndrew discuss how, with only 5% of rare diseases having approved treatments, patient-led research is essential for driving meaningful, life-improving progress.
36 Basket Trials for Rare Diseases: Where Innovation Meets Unmet Need
Enhancing drug development for rare diseases in the US has historically faced difficulties in the clinical trial landscape from design regulations and small patient populations. Clarivate’s Jaime Gavazzi explains how the use of basket trials could offer a solution to advancing products for the rarest diseases. Outlining the importance of regulatory flexibility and frequent engagement between the FDA and drug developers and the methodology of targeting shared molecule etiologies instead of specific diseases.
LOGISTICS
& SUPPLY CHAIN
38 The Critical Role of Effective Sample Management in Clinical Trials
Effective sample management in clinical trials is essential to guarantee data accuracy and outcomes. The mishandling of specimens can result in the loss of important opportunities, data quality and inaccurate research findings. Andrew Wyatt at Sapio Sciences explains the demand for reliable technology to assist clinical research. Highlighting the benefits adopting Laboratory Information Management Systems (LIMS) and integrated digital tools can have on standardising workflows, minimising errors, and enhancing transparency throughout the entire sample lifecycle.
Welcome to the Summer edition of JCS! This journal is full of diverse, fascinating topics and important points of discussion within the world of clinical studies. Ranging from advancements in clinical trials, to enhancing research on rare diseases and data management innovation in the form of artificial intelligence. This quarterly issue is packed full of informative and thought-provoking research from esteemed expert contributors.
A key topic of discussion in this issue surrounds rare diseases. In particular, evolving methods of improving trial designs, deepening patient involvement, and utilising technological advancements, despite the complexities of rare disease research. From the application of Net Treatment Benefit models and basket trials to the integration of digital biomarkers, and patient-led research, industry experts highlight how innovation and collaboration are reshaping the path toward more effective and inclusive treatments for often overlooked patient groups.
We begin this issue with a feature that analyses how CROs can reshape gene therapy trials in ophthalmology. The article explores how Contract Research Organisations (CROs) can play a pivotal role in advancing gene therapy trials for rare eye diseases. The piece features insights from leading ophthalmologists who stress the importance of experienced, collaborative CRO’s and how they can enhance the success of complex drug delivery, patient recruitment, data analysis, and navigating global regulatory requirements. Saqib Parkar of OptymEdge highlights the need for early, transparent communication between clinicians and CROs to avoid delays and design more patient-centric trials.
A personal favourite of mine is the feature titled More Insight from Fewer Patients: Advancing Rare Disease Trials with the Net Treatment Benefit. I found this article interesting because it highlights a smarter, more meaningful way to run clinical trials in rare diseases. Tom Mann of One2Treat explores the concept of Net Treatment Benefit (NTB) and how it offers a way to combine multiple outcomes, like quality of life, function, and side effects into one clear assessment. I found the articles emphasis on patient involvement in designing trials particularly interesting as it offers a new perspective on how clinical research can be both more efficient and more compassionate and reflect the experiences and priorities of the people living with these diseases.
JCS – Editorial Advisory Board
• Ashok K. Ghone, PhD, VP, Global Services MakroCare, USA
• Bakhyt Sarymsakova – Head of Department of International Cooperation, National Research Center of MCH, Astana, Kazakhstan
• Catherine Lund, Vice Chairman, OnQ Consulting
• Cellia K. Habita, President & CEO, Arianne Corporation
• Chris Tait, Life Science Account Manager, CHUBB Insurance Company of Europe
• Deborah A. Komlos, Principal STEM Content Analyst, Clarivate
• Elizabeth Moench, President and CEO of Bioclinica – Patient Recruitment & Retention
• Francis Crawley, Executive Director of the Good Clinical Practice Alliance – Europe (GCPA) and a World Health Organisation (WHO) Expert in ethics
• Georg Mathis, Founder and Managing Director, Appletree AG
Another article that captured my attention is a piece by Jaime Gavazzi at Clarivate, in which she explores the use of basket trials in accelerating access to life-changing treatments for rare diseases. This article sheds light on disadvantages faced in traditional drug trial designs and how basket trials offer an innovative path for advancing rare disease drug development. Although challenges remain in various forms, this piece expertly concludes why this trial design is essential for progress.
We conclude this journal with an article that highlights the critical importance of effective sample management in clinical trials. This contribution by Andrew Wyatt from Sapio Sciences explains how errors in sample management have become a common issue that can compromise trial outcomes. Imposing robust systems, like Laboratory Information Management Systems (LIMS), and utilising emerging AI technologies to optimise sample management and ensure the reduction of manual errors, can lead to higher confidence in faster results that ultimately benefits patients.
This journal offers valuable insights into progress in clinical trials and how that hinges on adapting advances in modern technologies and integrating patient-centric methodologies. Overall, the contributions on offer in this issue reflect the dynamic changes driving progress in how treatments are developed and delivered. I hope you enjoy this fascinating collection of work as much as I did!
Alice Phillips, Editor
• Hermann Schulz, MD, Founder, PresseKontext
• Jeffrey W. Sherman, Chief Medical Officer and Senior Vice President, IDM Pharma.
• Jim James DeSantihas, Chief Executive Officer, PharmaVigilant
• Mark Goldberg, Chief Operating Officer, PAREXEL International Corporation
• Maha Al-Farhan, Chair of the GCC Chapter of the ACRP
• Rick Turner, Senior Scientific Director, Quintiles Cardiac Safety Services & Affiliate Clinical Associate Professor, University of Florida College of Pharmacy
• Robert Reekie, Snr. Executive Vice President Operations, Europe, AsiaPacific at PharmaNet Development Group
• Stanley Tam, General Manager, Eurofins MEDINET (Singapore, Shanghai)
• Stefan Astrom, Founder and CEO of Astrom Research International HB
• Steve Heath, Head of EMEA – Medidata Solutions, Inc
Ramus Medical
is a part of Ramus Corporate Group. The company is managed under a centralised quality management and has developed an integrated QMS as well as specific standard operating procedures tailored for the clinical trials department that are fully harmonised with the GCP guidelines, and the local and European legislation.
Ramus Medical EOOD is a full-service contract research organisation (CRO) in Sofia, Bulgaria.
The company was created in 2009 as a natural development of the Medical Laboratory Ramus Ltd., the largest privately-owned medical laboratory in Bulgaria.
The company independently manages clinical research projects in Bulgaria and provides partnerships in multinational clinical projects providing a comprehensive range of clinical research services:
Core Services include:
• Medical writing
Our staff has extensive expertise in the preparation, adaptation and translation of a wide range of clinical trial documents that are fully compliant with the Good Clinical Practice (GCP) standards, the client’s specifications and the regulatory requirements.
• Study start-up
We offer full or partial study start-up assistance for different types of studies throughout Bulgaria.
• Regulatory submission
• Project management
• Monitoring
• Data Management
• Pharmacokinetic evaluation
• Biostatistics
• Regulatory advice and services
• Readability User Testing
• Registration of medicinal products on the territory of Bulgaria
• Pharmacovigilance services
• Logistic department
• Destruction of IMPs/IMDs & clinical samples – agreement with PUDOOS
• Archiving services
• DDD activities
Ramus Medical has gained its expertise during the completion of numerous clinical projects carried out over the past decade:
• Phases I to IV drug trials
• Non-interventional studies
• Pilot and Pivotal Medical Device investigations
The clinical trials we conducted facilitated the MA/CE mark granted by various European Agencies/Notified Bodies and Third Country Agencies.
Ramus Medical offers flexible clinical research services in various domains, with extensive experience in fields.
Our team comprises qualified, appropriately trained, experienced, motivated and collaborative professionals and is competent to
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communicate effectively across geographical and cultural boundaries to resolve any arising issues. We adhere strictly to the agreed timelines during the clinical investigations and strive to complete the tasks on time.
Why are we the solution for your projects? Ramus has its own:
Medical and Bioanalytical Laboratory
In 2018 the Medical Centre Ramus was established, located in Sofia, Bulgaria. Up to date, it has three separate locations, one of which is developed as an independent clinical research centre in compliance with the requirements for the phase I unit.
The Medical Centre Ramus allows the conduct of clinical trials in all phases in many therapeutic areas.
The Medical Centre meets all requirements for performing highquality clinical research and is designed to maximise the delivery of high-quality research data and was GCP-inspected.
Ramus Medical retains an extensive database of investigators and sites compiled through years of mutually beneficial collaboration.
Our bioanalytical laboratory is equipped with leveraging state-ofthe-art instrumentation (LC-MS/MS), techniques, and facilities, our team of experts has experience in a broad range of small molecules. Our Analytical laboratories provide method development, transfer, validation, and analysis of preclinical and clinical biological samples. We have extensive expertise in developing sensitive methods for LCMS/MS-qualifying multiple analytes and metabolites.
• Logistical company, certified for hazardous and biological samples transportation
• Clinical site facility and own catering company for hospitalised patients
A CRO Should be the Partner that Gives Full Visibility on a Gene Therapy Trials Path
Ahead of ARVO 2025 next week where Emmes is poised to launch the first dedicated ophthalmology clinical trial technology platform, we spoke with Saqib Parkar, Managing Director at Emmes’ OptymEdge, about how CROs can reshape gene therapy trials in ophthalmology. With only one FDA-approved ocular gene therapy on the market, the stakes are high, and timelines are critical. Ophthalmologists working with Saqib emphasise the growing importance of CROs as strategic partners – valued for their transparency, therapeutic expertise, and collaboration.
As cell and gene therapies redefine treatment possibilities, clinicians play a key role in shaping trials that are not only scientifically sound but also tailored to the unique needs of rare disease patients. In this article we explore the pivotal role they play in helping deliver bespoke clinical trials for gene therapies in eye diseases and the profound benefits these trials bring to both practitioners and their patients.
The Evolving Landscape of Clinical Trials
The field of ophthalmology has witnessed extraordinary progress, thanks to breakthroughs in cell and gene therapies. These innovations offer a lifeline to patients with debilitating eye diseases. Dr. Paul A. Sieving, Director, Center for Ocular Regenerative Therapy at UC Davis School of Medicine, underscores the critical need for these trials, particularly in addressing rare inherited ocular disorders such as the Inherited Retinal Dystrophies (IRDs). He states, ‘these conditions, although individually rare, collectively impact a substantial number of individuals within communities”. The advent of gene therapies has rekindled hope for these patients, providing treatment options where none previously existed.
While it's important to note that results from ocular gene therapy trials have exhibited variability, and only one therapy has received FDA approval thus far, Dr. Ian M MacDonald, a distinguished ophthalmologist from the Department of Ophthalmology & Visual Sciences at the University of Alberta, reminds us that these trials are inherently experimental. “They serve as invaluable learning opportunities for the ophthalmic community, allowing for the refinement of protocols, redirection of research efforts, and, most significantly, unwavering support for patients as they embark on a journey towards potential breakthroughs,” added MacDonald.
The Significance of Gene Therapy Trials
Dr. Sieving has been involved with a gene therapy trial for X-linked retinoschisis (XLRS). XLRS represents just one of many genetic conditions causing progressive vision loss, ranking among the top 10 most frequent conditions within the realm of Inherited Retinal Dystrophies (IRDs). When I spoke with him recently, he agreed that the impact of such eye diseases on communities is substantial
and stressed on the urgency of starting and hopefully completing successful trials. He highlighted why working more closely with CROs, and the relationship between sponsor trial manager and clinician has never been more important. A breakdown here will inevitably lead to delays and, in the worst of cases, can be a highly complicit factor in a trial’s failure – so we owe to patients to ensure this is avoided whenever possible.
But first let’s look at a couple of successes, and Dr. Sieving is hopeful that the approval of Luxturna – will herald in encouragement from clinicians to search out more trials and help these patients through what can be a dauting trial journey. While Luxturna – a Leber congenital amaurosis (LCA) treatment – was approved back in 2017, it remains the only one available and is a historic milestone as the first in vivo gene therapy. Infants and children treated with Luxturna experience vision restoration, offering tangible evidence of gene therapy's potential to address genetic eye diseases.
However, progress in the field since 2017 has been slower than anyone would have liked, and there is a clear need for more Human Gene Therapy ocular trials. What is encouraging as a CRO is the clinicians we work with strongly believe that these trials should be more collaborative. If we can share more expertise – maybe the practical learnings across competing trials that now we are positioned to appropriately leverage this potential. The challenge of course is that gene therapy trials for the eyes come with their unique set of difficulties and requirements, including skilled drug delivery, patient recruitment challenges, lengthy patient follow-up, and intricate trial data analysis. And, while it might be understandable people say ‘you would say that’, I would encourage clinicians to bring in one of the handful of experienced CROs in the space because trials design here are fundamental not only to the patients, but in how to engage with the regulators as we start to see successes.
Challenges in Conducting Gene Therapy Trials
Looking at a few specifics, one of the most prominent challenges we see is delivering the therapeutic gene vector accurately to specific cells in the eye while avoiding damage to healthy surrounding tissues. So, expertise in both the clinic and services associated with supporting these trials necessitates adequately trained staff with the required knowledge and skill sets.
Additionally, designing clinical trials for ophthalmic cell and gene therapies requires meticulous consideration due to the complexity and heterogeneity of eye diseases and the limited pool of eligible patients and how they are individually presenting. This means we must account for genetic variations, disease progression, and treatment strategies rather than one fits all design. Furthermore, selecting appropriate endpoints, dose escalation, dose limiting toxicity, and determining the duration of follow-up are essential for evaluating the therapy's effectiveness.
What Advice Do the Clinicians Give on Working with CROs?
Dr. MacDonald suggests that one area to watch for is that CROs can vary significantly in their approach and interactions with trial sites – so while you might have two CROs working on similar areas and at the same site – it is important to communicate expectations, outcomes and responsibilities at the outset. For example, once a trial is underway both sides are aware of the expected reporting requirements. And again, this is where good trial design can help. So that we design an approach that meets the regulatory burden and does not impart too greater strain on sites and doctors.
MacDonald added that while ‘some CROs are cooperative and easy to work with, others can potentially overwhelm trial sites with repetitive requests, and this is often a cause of friction. Moreover, some CROs may lack a deep understanding of local regulations and nuances which may delay trials.
His advice to clinicians is to try and close any contractual negotiations as early as possible. This can be surprisingly complex, especially in cases where perhaps the CRO has less direct cell and gene experiences, given the inherent difficulties with these trials. Then once this is taken out of the equation, you can then start the process of expediting local Institutional Review Board (IRB) reviews. There is the option of running this activity in parallel if collaborating with a large CRO which understands local requirements.
One other critical challenge for both investigators and CROs is to possess in-depth knowledge of the disease, the physiology of the eye, and the design of suitable outcome measures to demonstrate successful therapy. It’s important to consider before signing up on what experience the CRO has in the disease’s organ-specific characteristic. With the eye there is a huge amount of data that’s generated and it’s critical that it is recorded and analysed carefully.
Recruitment and Patient Engagement
It remains true in nearly all trials but due to the rarity of many eye disorders and specific inclusion criteria, identifying suitable patients can be a daunting task. Dr. MacDonald encourages that clinicians ask both sponsors and CROs to ensure that they help with some of the outreach by designing lay materials that can help explain the process and can help alleviate any apprehensive about a trial or its potential impact on the patient.
However, the real key is to ensure long-term patient engagement and clinicians should explore how CROs approach not only ongoing data collection but the longevity of interactions for e.g. are there learnings from across the trial that could help patients at another site.
Navigating Regulatory Hurdles and Limitations
Navigating the complex regulatory landscape represents another formidable challenge. Regulatory agencies such as the FDA and EMA impose stringent guidelines and rigorous assessments. The process involves meticulous scrutiny of pre-clinical, manufacturing, and clinical data to ensure consistency in manufacturing and to establish the therapy's safety and efficacy profile.
Dr. Sieving when speaking to us, stated an example from the XLRS Trial. These trials were originally planned as an internal trial to be conducted solely at the National Eye Institute. However, as the trial progressed, the need arose to expand to multiple trial sites, including considering sites in Europe This is of course where the challenge really began however, as not only are we now dealing with multiple sites, countries and patients; we are in reality also dealing with diverse regulatory and protocol requirements. So, from a single site hospital trial the XLRS trial now needed constant protocol amendments
and interactions with the FDA. Since each country has its own independent regulatory oversight body, specialised knowledge, and regular coordination was essential for the trial to progress.
Enhancing Success of Gene Therapy Trials
To overcome these obstacles and streamline the path to success in ophthalmology cell and gene therapy trials, strategic steps are essential. Partnering with an experienced CRO that specialise in this field can be a game-changer, but the choice of CRO is paramount.
A CRO that possesses a deep understanding of the disease's natural history right from the study's outset, should be considered. This knowledge forms the foundation for designing effective clinical protocols and selecting appropriate endpoints to assess therapy effectiveness as well as to monitor disease progression.
When evaluating a CRO, consider one that carefully plans dose escalation and cohort management. As that not only helps mitigate risks but also optimises dosage and delivery methods, ensuring the utmost safety for trial participants.
Another suggestion to get a gauge on your CROs experience [and connections] is to ask what they intend to do regarding selecting scientific advisory teams consisting of experts in both ophthalmology and gene therapy. These advisors can play a pivotal role in developing well-informed and robust clinical protocols and helping foresee trial criticalities long before they arrive. A rescue trial for any study is likely to be expensive, complex and lengthy and will worsen the patients’ experience. However, some CROs with experience are often proficient in successfully rescuing trials and that is something that may also be considered when selecting a CRO.
Collaborative Synergy between Academia and Industry
When we spoke with Dr. Paul Sieving, he highlighted the crucial importance of fostering collaboration between academic institutions and commercial enterprises in for gene therapy trials. We believe that in this competitive landscape dominated by biotech and pharmaceutical companies, academic researchers hold a treasure trove of knowledge concerning previously untreatable ocular genetic conditions. Dr. Paul's insights underscore the potential for harnessing this academic expertise to drive success in this field.
Saqib Parkar
Saqib Parkar is the Managing Director of OptymEdge, a recognised leader in visual function endpoints for ophthalmology trials. A qualified optometrist with nearly 20 years' experience, he has shaped over 200 global studies. Today, he is driving OptymEdge's expansion into a broader range of ophthalmic endpoints and cutting-edge technology, including eSource and imaging. Saqib's purpose is to advance clinical research by reducing site burden, improving data quality, and helping bring more efficient, patient-focused solutions to the field.
Accelerating Safely Toward an Efficient Ethical Review Framework
“All organisations, even the most renowned and successful, sometimes make the right things too hard and the wrong things too easy.”1 Sutton and Rao address this problem at an organisational level in their book, “The Friction Project.” I want to apply their concept to our current ethical review framework and discuss where speed should be encouraged and when deliberative slowing is necessary. After a brief discussion of the need for ethical review, we will examine the steps of review and how we might best accomplish applying appropriate speed and caution.
In 1966, Henry K. Beecher published an article detailing 22 examples of unethical clinical research undertaken by wellrespected investigators prior to the regulation of human research in the United States.2 Beecher recommended that the enforcement of ethical standards rest with publishers of scientific articles. After the publication of this article and other public research ethics lapses, the United States established the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (National Commission) and then followed their recommendations in enacting regulations requiring review by an institutional review board (IRB) for grantees under the Public Health Services Act in 1974. These regulations were followed by Food and Drug Administration (FDA) regulations requiring IRBs to conduct ethical reviews of clinical trials in 1981. The regulations establish clear criteria for approval of research involving humans.
Research on humans that is neither federally funded nor regulated by the FDA does not require external ethical review, but as these norms were established, journals and conferences began to require ethical review of research with humans, serving as a secondary gatekeeper as Beecher suggested. While most IRBs are associated with institutions, a robust network of independent IRBs grew in the United States to support researchers without access to institutional IRBs and then expanded to act on behalf of institutions as well. The consensus of the scientific community is that some speed bumps are necessary to protect society from unethical research.
Whether enforcement of ethical norms is conducted by publishers as Beecher suggested, or external review boards as required by U.S. regulations, the scientific community has agreed on the need for an external assessment of the ethics of research. While most scientific training involves some level of anti-bias training for individual investigators, as noted by Samuel Karpen, “...completely unbiased self-knowledge is neither attainable nor desirable, because bias is deeply engrained, and because the mechanisms that cause bias occur below awareness.”3 He writes about biased self-assessment in the context of clinical education, but it transfers well to our problem. He cites evidence that both providing feedback as guidance and establishing concrete assessment criteria are successful strategies in
combating bias. IRBs and the criteria for approval they apply provide a robust check against investigator bias.
Universities, hospitals, governmental organisations, or other private companies establish IRBs for their own use, and independent IRBs accept work on behalf of investigators either privately or on behalf of institutional IRBs. The speed of review varies between these types of IRBs. Based on data in the Government Accountability Office 2023 Report on Institutional Review Boards, review times can vary from as little as one week to as long as 18 weeks.4 For an investigator eager to start their research or a sponsor who needs a return on the investment for a potential drug, time is of the essence and waiting for ethical review can seem like just another hurdle to clear.
There are genuine costs to delaying clinical research studies. Getz et al. report that a one-day delay in conducting a clinical trial amounts to approximately $40,000.5 At that rate, the 17 weeks between IRB approval at the slowest versus the fastest IRB would cost more than five million dollars. In addition to the financial costs of delays, we know that future patients are desperate for therapies that might improve their quality of life or prove to be lifesaving. Speed does matter, and we have a strong societal interest in ensuring medical breakthroughs reach the public as quickly as possible, and that we are not passing on unnecessary costs from delays to the patients who eventually receive those therapies.
If we agree that external review is necessary for safety, but that speed is also important, where should we install speed bumps and where should we erect highways?
If we take a generic, multi-centre clinical trial that involves more than minimal risks as an example, IRB review requires the following:
• Establishing IRB agreements for cooperative research.
• Receiving and reviewing IRB submissions for completeness.
• Scheduling reviews with appropriate expertise.
• Reviewing and deliberating on the ethics of the proposed research.
• Communicating and clarifying outcomes of ethical determinations.
The main value delivered from the review is a carefully considered, external, expert opinion on the ethics of the proposed research. This ethical opinion should be grounded in the criteria for approval established by the regulations. It should not diverge into a secondary scientific review or a critique of the investigators’ research plan with no impact on the risk-benefit ratio of the research. Each individual board member attending the review should have adequate time to review the materials to prepare for the discussion. Then, the group should have adequate time to deliberate any points of disagreement and to either vote or achieve consensus on the outcome and any
requirements of the investigator to secure approval. Ensuring board members are well-trained on the criteria for approval can focus deliberations and keep unnecessary discussion to a minimum.
Organisations can modulate the time it takes to review materials in various ways. For example, many IRBs meet once per month, and those meetings contain many items to be discussed. The items are combined in a batch, scheduled for one meeting, and the board members review all materials prior to the meeting, discuss all items in the meeting, and document their determinations for all items. In many cases, this does not result in the most economical outcome. Smaller, more frequent meetings could reduce the total time to review. Independent IRBs have fewer constraints, and they can use this strategy to reduce total review times without altering the time per review item for the board. In the figure below, the “Holding Time” (purple line) is how long you let the items build up before scheduling them for a meeting. The “Meeting Prep Time” (green line) is the time it takes to package a meeting. Creating the meeting takes a lot of time, but adding additional items takes less time. The “Total Time” of review (light blue line) is a combination of the “Holding Time” and the “Meeting Prep Time.” It has a minimum number of items per meeting, at which point the “Total Time” is most economical. For example, organisations can allocate resources to lower the time it takes to create a meeting and they can also hold meetings often enough to
land in the zone where the total time for review is minimised. Ideally, all the time spent on steps other than review and deliberation would be minimised. Organisations will be constrained by financial considerations that may limit the personnel and technology available to efficiently accomplish the steps outlined above. Organisations like hospitals and universities will also be constrained by internal policies that reflect the institution’s response to varying state and federal laws and their tolerance for risk. Despite the challenges, we see existing solutions and future opportunities for increasing the speed of review without sacrificing safety.
Excellent work has been done to minimise the time spent on administering IRB agreements in support of cooperative research and multi-centre trials. SMART IRB was, until recently, a National Institute of Health (NIH) funded project to streamline the laborious process of negotiating individual institutional agreements necessary for collaboration for each research proposal. Instead, institutions can agree to common terms up front and then use simple one-page agreements for each project. SMART IRB supports research for over 1,350 institutions, 12,000 researchers, and 2,000 staff members across the nation, including the NIH, universities, academic medical centres, community hospitals, commercial or independent IRBs, and others. This solution required shared investment in resources and time up front to ensure each project starts up more quickly.6
Much can be done to improve the process of receiving and reviewing IRB submissions for completeness. While this may seem like unnecessary work, if done well, it saves time in the key step of reviewing and deliberating on the research. The reviewers, whether they are external experts, hospital physicians, or university faculty members, are highly skilled professionals whose time is expensive in either direct costs or opportunity costs. Providing them with clear, organised materials minimises their time spent on understanding the research proposal. Because much of this involves comparing documents for consistency and completeness, this is a natural area for exploring technological solutions like artificial intelligence. If technological and human resources are limited at smaller institutions, IRB offices can focus on ensuring intake forms and processes are clear and concise to minimise confusion, inconsistencies, and misunderstanding.
Technology to support scheduling and hosting virtual meetings
Regulatory Affairs
has improved over the last decade. Another way larger independent IRBs manage this is to establish large boards to allow for more flexibility in achieving a level of appropriate expertise for any given meeting. These IRBs set up arrangements with many members who are unaffiliated with the IRB to serve as members as needed, allowing for maximum flexibility in scheduling meetings. In contrast, hospitals and universities staff IRBs with their own employees and typically include only one unaffiliated member that federal research regulations require. They also tend to operate with meetings at a less-frequent cadence. This is a result of both limited resources like availability of their members and historical practices.
Finally, communicating and clarifying the outcomes of ethical determinations is where the value of the review is delivered to the investigator. This step can be improved through both technology and careful diligence throughout the process. If communication is clear and grounded in the criteria for approval, investigators will understand what is needed to secure approval, and long waiting times between reviews will be reduced.
IRBs contribute to a safe research enterprise by providing an objective, non-biased review of the ethics of the research that is grounded in the criteria for approval that was outlined by the National Commission and codified in federal regulations. Measuring the value of that review is more difficult than calculating the cost of the delay. While an absence of data does not prove the point, we have not seen reports of abuses like those described by Beecher in the decades since the establishment of IRBs. As experimental drugs and devices have reached astounding improvements in human
health, we have yet to see a comparable example of unethical research. We should be looking for new ways to minimise the time and cost of ethical review of research while preserving the value of ensuring participants and the public are as safe as possible.
REFERENCES
1. Robert I. Sutton and Huggy Rao. How Smart Leaders Make the Right Things Easier and the Wrong Things Harder; The Friction Project. (2024).
2. Henry K. Beecher. Ethics and Clinical Research. The New England Journal of Medicine. (1966).
3. Samuel C. Karpen. The Social Psychology of Biased Self-Assessment. American Journal of Pharmaceutical Education. Volume 82. Issue 5. (2018).
4. United States Government Accountability Office. Institutional Review Boards. (2023).
5. Smith ZP, DiMasi JA, Getz KA. New Estimates on the Cost of a Delay Day in Drug Development. Ther Innov Regul Sci. 2024 Sep;58(5):855-862.
6. https://smartirb.org/, visited on 22 Apr 2025.
Kelly FitzGerald
Kelly FitzGerald is the IRB executive chair and vice president of IBC Affairs at WCG where she oversees the review teams of both the IRB and several hundred institutional IBCs. She is responsible for the compliant and efficient operation of both the IRB and IBCs. Kelly joined WCG IRB in 2014 as the director of Expedited Review.
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EU Policy Momentum Sets the Stage for Clinical Trial Breakthrough in 2025
Europe’s regulatory environment is shifting fast. In the clinical trials space, recent years have brought sweeping changes, shaped by initiatives like the General Data Protection Regulation (GDPR), the AI Act, ACT EU (Accelerating Clinical Trials), and the upcoming European Biotechnology law. These evolving frameworks mark a clear step toward tighter oversight and more transparent processes. For biopharma companies, the path forward involves navigating a more complex regulatory landscape, where compliance, data transparency, and emerging technologies are tightly interwoven. With both new challenges and opportunities ahead, adaptation is not optional – it’s essential.
Last year, the European Commission President renewed the EU’s support for strengthening Europe’s life sciences sector, placing emphasis on innovation-first policies and greater regulatory alignment. As these changes begin to take shape, biopharma companies have an opportunity to move beyond box-ticking. By adopting connected data platforms, leveraging AI for operational efficiency, and shifting toward more patient-centric models, compliance can become a source of competitive edge - not just a regulatory obligation.
The Future of Clinical Trials is Being Shaped by Regulation
The regulatory shift is redefining how clinical trials operate. Since 2022, the EU has moved toward greater harmonisation of regulatory requirements, aiming to reduce administrative burdens while maintaining high ethical and scientific standards. The Clinical Trials Regulation (CTR) has already standardised submission and approval processes across EU member states. Now, data and AI regulations are set to reshape compliance frameworks and digital capabilities.
These regulations are paving the way for a more connected, efficient, and transparent clinical trial ecosystem. Companies are rethinking their approach to technology, data governance, AI integration, and compliance strategies to advance with agility in the evolving regulatory environment. A critical component of this shift is establishing a unified platform approach that facilitates the exchange of data from clinical, regulatory, safety, and quality functions and lays the groundwork for useful applications of AI. Many organisations still operate in silos, where compliance is treated as a separate function rather than an integrated part of the development process. The ability to break down these silos and create real-time data visibility will be a key differentiator by accelerating drug development to support patient outcomes.
Unlocking Practical AI Use Cases in Clinical Research
AI in clinical trials is often discussed in broad, futuristic terms, but its practical applications are already making an impact. Rather than focusing on theoretical advancements, biopharma companies can
identify where AI could add real value. For example, AI could help enhance data quality by detecting anomalies and inconsistencies, ensuring more reliable clinical outcomes. Predictive analytics could help transform patient recruitment, identifying eligible participants faster and improving trial diversity.
Regulatory agencies are now considering how AI should be validated within clinical settings. The AI Act, for instance, proposes specific requirements for high-risk AI applications, including transparency, robustness, and human oversight. However, the effectiveness of AI in these areas is only as strong as the underlying data. A well-integrated, high-quality, clean data foundation is essential for AI-driven insights to deliver real value. Moving forward, organisations should continue balancing innovation with accountability, embedding AI into clinical workflows in a way that is both effective and compliant.
Why Clean, Transparent Data is Key to Next-generation Compliance
As data transparency becomes a central theme in EU regulations and the updated ICH E6(R3) guideline, companies are shifting toward a more structured approach to data governance. The industry is moving beyond simply collecting large datasets to ensure data integrity, auditability, and regulatory compliance remain at the forefront. The latest EU regulations demand end-to-end visibility of clinical trial data so that all study records are traceable and compliant. Companies proactively developing data governance frameworks can mitigate compliance risks before they arise.
The accuracy and integrity of clinical trial data is not just a regulatory requirement – it is critical to patient well-being. Providing regulators with real-time access to high-quality data will be a key factor in securing faster approvals and reducing the risk of compliance-related delays. With the increasing use of remote monitoring and decentralised trials, unifying data sources will be critical for regulatory adherence.
Putting Patients First with AI-driven Innovation
One of the most promising aspects of AI in clinical research is its potential to enhance patient recruitment and monitoring. Historically, patient enrollment has been a major bottleneck in drug development. Sponsors with the right data can now use AI to identify patients and engage with them faster. Digital biomarkers and remote monitoring could allow real-world data collection without requiring frequent site visits. Personalised patient engagement strategies improve retention and study adherence by reducing the burden on patients, which ultimately increases trial efficiency and diversity. Leveraging AIdriven patient insights can also enhance trial design by identifying potential dropout risks early so study teams can make real-time adjustments. This level of adaptability will be crucial in efficient recruitment for trials that also maintain high patient engagement throughout the study duration.
Finding the Balance Between Tech Innovation and Regulatory Pressure
While connected technologies and AI offer improved efficiencies, they also introduce new regulatory complexities. Biopharma companies should strike the right balance between automation and compliance, ensuring that processes meet EU ethical guidelines and transparency requirements. One emerging challenge is disclosure management. Under GDPR and upcoming EU transparency requirements, companies must ensure that sensitive clinical trial data is shared responsibly. Connected technologies can help streamline compliance reporting, enhance regulatory filings, and manage public disclosures.
Furthermore, EU regulators increasingly emphasise the need for verifiable AI models in study documentation, adverse event detection, and protocol optimisation. Companies should proactively integrate validated AI workflows into their clinical operations, ensuring they remain both compliant and competitive. This makes a unified clinical data foundation even more vital to ensure regulatory readiness and maximise the impact of AI.
Regulatory Change as a Springboard for Innovation
The regulatory landscape this year will bring a mix of complexity and opportunity for biopharma companies. Those that adopt AI to boost efficiency, build transparent compliance frameworks, and enhance
Preparing for what's ahead means acting now. By investing in technology to build connected clinical functions, organisations can meet rising regulatory expectations while laying the groundwork to adopt technologies like AI. Compliance doesn’t have to be a constraint. When approached strategically, it becomes a launchpad for operational modernisation and increasingly personalised patient experiences, with the end goal of bringing new treatments to patients faster.
Werner Engelbrecht
Werner Engelbrecht has extensive experience in the pharmaceuticals and life sciences industry, across a range of roles, with his career spanning over twenty years. In his current role as Senior Director Strategy at Veeva Systems, Werner heads up a team that is dedicated to using digital transformation to navigate the complexities of clinical trials and speed up development of new medicines.
patient engagement will be well positioned to lead the next wave of clinical research.
Beyond Two-Group Randomised Controlled Trials: Advancing Rare Disease Drug Trials
Rare diseases, also known as orphan diseases, affect an estimated 300 million people worldwide, yet each individual condition impacts a relatively small patient population, typically fewer than 1 in 2,000 individuals in the European Union or similar thresholds globally.1,2 Despite individual rarity, the combined burden is substantial.
Even though there is a growing interest in orphan drug development, many pharmaceutical companies remain cautious about investing in rare diseases due to significant scientific, financial, and operational challenges. The small patient populations inherent to rare diseases make it difficult to conduct robust clinical trials and limit the potential for commercial return. The pathway to regulatory approval is often complex due to limited natural history data, lack of standardised clinical endpoints, and challenges in trial recruitment. The high cost of research and development, combined with increasing scrutiny from payers over cost-effectiveness and long-term value, mean that many companies prioritise broader indications with more predictable pipelines and larger markets, where return on investment is more assured.
Globally, initiatives such as the International Rare Diseases Research Consortium (IRDiRC) and regional frameworks like the EU Regulation on Orphan Medicinal Products have sought to stimulate research and development through incentives and international collaboration.3,4
Despite progress, rare disease trials often require innovative, flexible designs to address small sample sizes and ensure meaningful, patient-centred outcomes.5 Traditional clinical trials are built on well-established endpoints, randomised controls, and large sample sizes. These luxuries are rarely available for clinical trials of rare diseases. Consequently, conventional statistical approaches are often inappropriate, and this has prompted a push towards methodological innovation.
Real-world Evidence and Digital Biomarkers
The rise of digital health technologies and electronic health records has opened new opportunities for real-world evidence (RWE) collection. In rare disease trials, given the scarcity of eligible patients, fragmented data, and variability in disease presentation, leveraging real-world data (RWD) sources enables researchers to better understand disease natural history, refine inclusion criteria, and construct external comparators or synthetic control arms.
Electronic Health Records, in particular, can provide longitudinal insights into patient outcomes, treatment patterns, and comorbidities. These are critical for designing meaningful endpoints and contextualising trial findings, as well as facilitating patient identification and recruitment.
Digital health technologies and digital biomarkers further enhance rare disease trials by offering more continuous, objective, and patient-centric measures of disease progression and treatment response. Wearables, mobile apps, sensor-based tools, and patientreported outcomes can capture continuous, high-resolution data, providing a more nuanced assessment of how patients experience their condition in everyday life. Digital biomarkers, derived from sensors or imaging, can serve as surrogate endpoints or augment traditional measures. In diseases where standard clinical endpoints are poorly defined or hard to measure, digital biomarkers can fill critical gaps by offering novel endpoints that are both sensitive and scalable.
Synthetic and External Control Arms
Recruiting control groups in rare disease trials can be ethically and logistically challenging. Withholding potentially life-altering treatment from patients in a control group may be ethically problematic and recruiting sufficient numbers for a standard randomised controlled trial (RCT) may not be feasible. A synthetic control arm leverages existing data, such as patient registries, natural history studies, or real-world evidence (RWE), to construct a comparator group instead of enrolling participants into a traditional placebo or standard-ofcare arm. When validated rigorously, these external comparators can substitute for placebo arms, reducing patient burden, improving statistical power, and expediting trial timelines.
Regulatory agencies such as the FDA and EMA have shown increasing openness to synthetic control arms in rare disease contexts, particularly when supported by appropriate methodology and transparent data curation practices. Still, concerns remain around bias, confounding variables, and data standardisation, emphasising the need for continued innovation in statistical techniques and data governance to ensure credibility and regulatory acceptance.
Propensity score matching, inverse probability weighting, and other causal inference techniques are often used to adjust for baseline differences between the treatment group and the external control group, ensuring comparability between treated and control populations. For example, propensity score matching pairs treated patients with similar individuals from historical datasets based on key covariates, such as age and disease severity, helping to mimic the balance of a randomised trial. These methods aim to reduce bias and confounding, thereby increasing the validity of comparisons and strengthening the case for treatment efficacy in the absence of a traditional control arm.
Master Protocols
Master protocols in clinical trials refer to innovative trial designs that allow the simultaneous evaluation of multiple interventions, treatment combinations, or patient populations under a single overarching protocol. In rare diseases, where patient scarcity is a critical challenge, platform trials maximise resource utilisation.
Master protocols streamline the clinical trial process by enabling the efficient assessment of multiple therapies, reducing patient recruitment time, and optimising resources. They typically come in three forms:
• Umbrella trials: focused on different treatments for a single disease,
• Basket trials: testing one treatment across different diseases with a shared molecular target, and
• Platform trials: which considers the continual evaluation of multiple treatments or interventions within the same trial, enabling new therapies to be added as the trial progresses based on emerging data.
Master protocols offer a powerful way to maximise data use. By increasing flexibility and adaptability, as well as sharing control groups, master protocols can streamline and accelerate the development of personalised therapies, bringing innovative treatments to patients more rapidly.
Bayesian Statistics
Bayesian statistics has gained traction as a flexible and powerful alternative to frequentist methods, especially in rare disease settings where data is limited. Unlike traditional frequentist methods that rely solely on trial data, Bayesian approaches integrate prior information – such as historical data, RWD, earlier phases of a trial, or expert opinion – into the analysis. This "borrowing strength" enhances the information available and enables more robust inferences with fewer patients. It allows for more precise estimates of treatment effect and can significantly reduce required sample sizes.
Another application of Bayesian statistics is adaptive trial designs, which enable real-time updates of probability estimates. This allows for dynamic adaptions as data accrue, such as stopping early for efficacy or futility, or adjusting randomisation ratios to favour more efficacious treatments.
Bayesian hierarchical models allow for sharing of information across related subgroups or trial arms, which is especially useful in diseases with multiple phenotypes. By nesting patients within subgroups, researchers can improve inference when data are sparse and model both shared and unique subgroup characteristics.
Research and Development
Bayesian approaches also provide probabilistic interpretations of outcomes, such as estimating the likelihood that a treatment is more effective than control, or estimating the likelihood that treatment surpasses a clinically meaningful threshold. This is particularly useful in single-arm trials, which are used in rare disease settings where direct comparison to a placebo group isn’t possible. These probabilitybased interpretations are also often more intuitive for decisionmakers, including clinicians, regulators, and payers.
Response-adaptive Randomisation
Response-adaptive randomisation (RAR) is an innovative trial design that dynamically adjusts the allocation of patients to different treatment arms based on accumulating outcome data. Interim results are used to indicate which treatment is more efficacious and the RAR assigns more patients to treatment arms which demonstrate greater benefit, reducing allocation to treatments that are less effective or harmful. In rare disease settings, RAR offers an ethical and efficient alternative to traditional fixed randomisation allocations.
Because rare disease trials often struggle with recruitment and involve severe or life-threatening conditions, traditional RCTs with equal allocation may be viewed as ethically questionable or practically inefficient. By minimising the exposure to ineffective therapies, RAR trial designs improve recruitment and retention rates.
RAR designs not only enhance the chance that patients receive potentially effective therapies but also helps generate stronger evidence of efficacy by concentrating enrolment in successful arms while minimising exposure to inferior options. By favouring more effective treatments, RAR can lead to faster identification of beneficial therapies, potentially shortening trial duration and speeding up regulatory approval.
While these designs are more complex to plan, analyse, and regulate, advancements in statistical modelling and simulation tools are making RAR increasingly viable and regulatory agencies are also showing growing interest in these methods.
N-of-1 Trials
For ultra-rare conditions or highly personalised treatment approaches,
Research and Development
N-of-1 trials, where a single patient serves as their own control, are gaining interest.
Statistically, these trials require careful within-subject comparisons to reduce bias and account for variability over time. Key statistical considerations include ensuring adequate washout periods between treatments to avoid carryover effects and using time-series or mixed-effects models to analyse repeated measures within individuals. While each N-of-1 trial may be underpowered to detect broader treatment effects across populations, aggregating multiple N-of-1 trials using Bayesian hierarchical modelling or metaanalysis techniques allows for inference at both the individual and population levels.
Personalised trial designs, which adapt trial elements to suit individual patients – such as tailored dosing, dynamic inclusion criteria, or genotype-driven treatment selection – require flexible statistical frameworks capable of handling heterogeneity and small sample sizes.
Despite challenges, personalised trials are increasingly positioning themselves as a vital tool in the era of precision medicine, particularly for patients with rare or ultra-rare conditions who may never be eligible for traditional randomised controlled trials.
Conclusions
Novel statistical methodologies and innovative trial designs are transforming rare disease drug development. Traditional RCTs, that are often rigid and resource-intensive, are ill-suited to small, heterogeneous patient populations. But they are giving way to more flexible, datadriven approaches that can better accommodate the unique realities of rare conditions. By embracing Bayesian frameworks, responseadaptive designs, RWE, and synthetic control arms, researchers are discovering new ways to extract meaningful insights from limited datasets while maintaining scientific rigour and ethical integrity.
The integration of electronic health records and digital health technologies further enriches the clinical evidence base, enabling more patient-centred evaluations of therapeutic impact. These tools not only support more intelligent trial design but also foster inclusivity by reducing the burden on patients and their families.
As these methodologies continue to evolve and gain regulatory endorsement, they offer an exciting new landscape not only for rare diseases but for reimagining drug development more broadly. The insights gained and intelligent use of data will result in clinical trials that are not only quicker and more cost-effective, but also fairer, more ethical, and more attuned to the needs of patients across all conditions.
REFERENCES
1. EURORDIS. (2023). What is a rare disease? https://www.eurordis.org/
2. Nguengang Wakap, S., Lambert, D. M., Olry, A., et al. (2020). Estimating cumulative point prevalence of rare diseases: Analysis of the Orphanet database. European Journal of Human Genetics, 28(2), 165–173. https://doi. org/10.1038/s41431-019-0508-0
3. International Rare Diseases Research Consortium (IRDiRC). (2023). Our mission. https://www.irdirc.org/
4. Kakkis, E. D., O’Donnell-Tormey, J., Trochim, W. M., et al. (2015). A proposed regulatory framework for clinical development and approval of drugs for rare diseases. Orphanet Journal of Rare Diseases, 10, 61. https://doi. org/10.1186/s13023-015-0274-1
5. Thompson, R., & Wong, M. H. (2017). Precision medicine and rare diseases: Opportunities and challenges in the genomics era. Journal of Rare Disorders, 5(1), 1–10.
Jennifer Visser-Rogers
Professor Jennifer Visser-Rogers is the Chief Scientific Officer at Coronado Research. She is an accomplished consultant with a broad portfolio of achievement. Jennifer made the move into the industry after a career in academia which included the University of Oxford where she was Director of Statistical Consultancy Services. She has a strong record in the development of novel methodologies in the design and analysis of clinical trials, working alongside other statisticians, clinicians, data scientists, industry experts, and regulators. More recently, Jennifer has applied her expertise to industry, leading research teams in the CRO space and remaining at the forefront of innovation. She has experience of growing expert strategic consulting groups, developing some of the most influential and talented thought leaders. A popular science communicator, Jennifer has made numerous media appearances and regularly works with journalists and government to improve the understanding of statistics. She was the 2020 winner of the annual HealthWatch award and named one of the Twenty Women in Data and Tech in 2024 by Women in Data®. A former Vice President of the Royal Statistical Society, Jennifer is the President of the International Biometrics Society, British and Irish Region, Director for Communications and External Affairs at Statisticians in the Pharmaceutical Industry (PSI), and Vice Chair of the Florence Nightingale Museum.
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Research and Development
Exploring Updates in Adaptive Trial Safety Monitoring
The role of Data Monitoring Committees (DMCs) has never been more critical in ensuring patient safety and trial integrity, especially as clinical trials become more complex and globalised. In response to these evolving challenges, the FDA released its long-awaited 2024 draft guidance on DMCs, marking the first update since 2006. This draft reflects the growing prevalence of adaptive trial designs, which allow for real-time modifications based on interim data. This innovative agility is a powerful tool in fast-moving therapeutic areas like rare diseases and precision medicine. However, adaptive designs also introduce challenges that fall under the purview of DMCs, including statistical methodologies, interim decisionmaking and maintaining trial integrity.
For sponsors with adaptive trials underway or in their pipeline, understanding these regulatory updates is essential to ensuring compliance while optimising trial efficiency. This article unpacks the key changes in US Food & Drug Administration (FDA) draft guidance on DMCs as it relates to adaptive design trials, providing practical insights and strategies for advanced safety oversight in the modern clinical trial landscape.
The Rise of Adaptive Trial Designs
Over the past decade, adaptive trials have gained traction as a strategic alternative to traditional fixed designs. Adaptive trials allow for prospectively planned modifications such as dose adjustments, treatment arm additions or early stopping to optimise resources, reduce timelines and enhance patient outcomes. Essentially, they allow us to do better as we know better. This flexibility is particularly valuable in areas like rare diseases, where patient populations are small, or in emerging infectious diseases, where rapid responses are required.
Amidst a collective increase in complexity across clinical research, adaptive elements like statistical methodologies, interim decisionmaking and maintaining the trial’s blind have become more complex as well. In addition to operational burden, more frequent protocol changes and amendments increase the potential for unintended bias and require sophisticated statistical safeguards. Meanwhile, all modifications must be properly documented for regulatory authorities and scientifically justified to ensure credibility. The FDA’s updated draft guidance reinforces the role of DMCs and addresses the challenges of adaptive trials.
Adaptation Committees: A New Framework for Flexibility
To address the nuanced requirements of modern clinical trials, the FDA’s updates reinforce the role of DMCs while introducing adaptation committees as a complementary oversight mechanism. The FDA introduces two approaches to adaptation committees: either integrating them into existing DMCs or forming separate committees. Both strategies demand expert planning to address statistical and operational challenges.
Integrated DMC models take on adaptation responsibilities alongside safety monitoring, streamlining oversight. The FDA recognises that this method is better suited for simpler designs, such as group sequential designs, where the adaptations are less complex
and align closely with the DMC’s standard safety oversight functions. Otherwise, it could unnecessarily increase workload and complexity.
Separate adaptation committees can be composed of statisticians and other experts with deep knowledge of adaptive trial methodologies, offering a sharper focus on the technical aspects of adaptation decisions. An independent committee allows for specialised expertise dedicated to interim decision-making and modifications. This structure also aids in compliance and relieves some planning pressure from the DMC as the FDA discourages DMCs from proposing design changes after reviewing unblinded data, and an adaptation committee would remove this complication.
For sponsors, choosing the right structure depends on the complexity of the trial, the frequency of adaptations, and regulatory expectations. Highly adaptive trials with frequent modifications may benefit from a separate adaptation committee, whereas simpler trials with occasional adjustments might function efficiently under an integrated model.
Addressing Adaptive Trial Design Elsewhere
The specific challenges of safety and data monitoring for adaptive trials is layered into other areas addressed in the updated FDA draft guidance, including charters, statistical analysis and reporting.
Charter Challenges
DMC charters have grown longer and more detailed to accommodate the more intricate methodologies, including multiple interim analyses, complex decision rules and heightened safety monitoring requirements associated with modern clinical trials.
In developing a DMC charter, it can be difficult to strike the right balance between providing comprehensive detail and maintaining a document that is practical and easy to navigate. Exceedingly intricate charters can slow decision-making or create confusion, while oversimplification risks omitting critical procedural details. For adaptive trials, this balance must incorporate a wider range of potential scenarios and document corresponding decision rules. Developing a successful charter requires collaboration with knowledgeable statisticians and trial design experts to ensure all contingencies are covered, which can be a time-consuming and iterative process.
Analysis and Reporting
Trials involving vulnerable populations, novel therapeutic modalities or high safety risks may require more frequent interim analyses. As noted in the FDA draft guidance, this reflects the need for real-time access to high-quality data to ensure that DMCs can review and act on interim results without delays. This necessitates enhanced data monitoring systems to flag and resolve discrepancies quickly in conjunction with streamlined data transfer protocols to ensure smooth and secure data transmission between trial sites and the DMC.
For trials implementing adaptive designs or early terminations, the FDA requires sponsors to provide justification for modifications, including outlining the statistical basis for decisions, referencing prespecified criteria from the DMC charter and Statistical Analysis Plan (SAP).
Research and Development
Practical Implications
Preparing for these changes will help sponsors align with regulator goals of enhancing the safety monitoring, transparency and efficiency in clinical trials. Consider the following key points for successful implementation:
• Clarity and Governance – Clearly define the scope, authority and decision-making processes for DMCs and adaptation committees to avoid overlap or conflicts. Outline communication protocols to avoid information lag.
• Data Integrity – Secure data-sharing infrastructure is paramount to facilitating timely, confidential data transfers for ongoing safety monitoring. For example, we use a secure and controlled digital infrastructure to protect unblinded data while improving efficiency.
• Statistical Expertise – The FDA recommends incorporating simulation studies to explore hypothetical scenarios and assess the robustness of adaptive decision rules. Consulting with
biostatisticians familiar with adaptive and Bayesian designs can ensure an appropriate level of complexity is accounted for in the interim analyses.
• Partner with Experts – An experienced CRO can provide strategic guidance and operational support to sponsors adapting to this evolving regulatory landscape, ensuring that innovation does not come at the expense of oversight. For example, ICON’s investment in a dedicated DMC unit provides a secure, firewalled digital infrastructure with centralised processes that minimise risk and avoid delay while experienced specialists assist no matter the size, scope or design of the trial.
Continuity and Compliance
The FDA’s 2024 draft guidance reflects a shift in how adaptive trials are governed, reinforcing the need for both flexibility and rigorous oversight. With the introduction of adaptation committees, sponsors must carefully evaluate their monitoring frameworks to comply with evolving regulatory expectations. Sponsors that embrace proactive governance and optimised oversight models will be best positioned to accelerate drug development without compromising compliance.
Karen Shaffer
Karen Shaffer is the Director of Biometrics (DMC Unit). She is a registered nurse with over 25 years of clinical research experience spanning small biotech and large pharmaceutical companies. Her diverse roles have included positions in clinical sites, medical information, drug safety, and over 15 years specialising in Data Monitoring Committees (DMCs). Karen has managed over 100 DMCs for government-led studies, major pharmaceutical firms, and small biotech clients. She currently oversees a team of 18 project managers, statisticians, and programmers across multiple therapeutic areas.
Birgit Geiger
Birgit Geiger is the Director of Independent DMC and Endpoint Adjudication (IDEA). She is a registered nurse with 20 years of clinical research experience, including 16 years in Data Monitoring Committee (DMC) management and 8 years in Pharmacovigilance and Regulatory Affairs. Since 2015, she has led the ICON DMC team across Europe, India, and Japan, providing portfolio oversight for major pharmaceutical companies and small biotech firms. Birgit excels in organisational skills, effective risk management, and process improvement, consistently delivering high-quality results in fastpaced environments.
Patricia Braschayko
Patricia Braschayko is a Senior Manager at Biostatistics. She has 19 years of biostatistics and programming health research experience spanning pre-clinical to Phase III clinical trials, including 7 years in management roles. She has led studies for federal, large pharma, and small biotech clients across a wide variety of indications. She is dedicated to protecting patient safety by ensuring data review committees are provided with accurate, complete, timely data to support trial oversight.
Research and Development
Evaluating the Role of Biomarkers in Oncology Research using Multiplex Immunoassays
The discovery and integration of cancer biomarkers into oncology research has transformed how cancer is detected, diagnosed, and treated. As precision medicine continues to evolve, biomarkers have become essential tools supporting personalised cancer treatment, providing key insights not only about tumour biology but also about patient-specific factors that influence therapeutic outcomes.
Fundamentally, biomarkers are measurable indicators of biological processes or responses that reflect the underlying state of disease and may include nucleic acids, proteins, or circulating tumour cells, and can be isolated from a range of specimens such as tumour tissue, blood, saliva, or urine. The diversity of biomarker types mirrors the complexity of cancer itself, necessitating tailored approaches for different tumour types and stages. As such, the integration of biomarkers into clinical decision-making offers several key advantages. Early detection of diagnostic biomarkers can improve survival rates by enabling intervention at more treatable stages. Predictive biomarkers help clinicians identify which patients are likely to benefit from a given therapy which is especially important when it comes to precision medicine approaches, such as selecting an appropriate targeted immunotherapy. Prognostic biomarkers, by contrast, provide information about the disease outcome irrespective of treatment and are useful for stratifying patients in clinical trials or planning long-term care.1
Immunotherapy Milestones and Challenges
The emergence of immunotherapies has not only transformed cancer treatment but also deepened our understanding of the tumour microenvironment (TME) which is a dynamic and heterogeneous network of stromal components, including immune cells, extracellular matrix, and signalling molecules. This microenvironment actively interacts with tumour cells, shaping the cells’ ability to proliferate, metastasize, and evade immune surveillance. As such, immune evasion mechanisms, including altered expression of immune checkpoint receptors and their ligands, may be central to cancer progression.2
Cancer immunology, a field dedicated to decoding these tumour–immune interactions, has driven the development of innovative therapeutic strategies, including monoclonal antibodies, immune checkpoint inhibitors (ICIs), CAR T-cell therapies, and cancer vaccines.3 Among the most transformative developments in oncology has been the rise of ICIs, which unleash cytotoxic T cells against tumours by blocking regulatory pathways like PD-1/PD-L1 and CTLA-4.4 While these therapies have led to durable remissions in subsets of patients with melanoma, non-small cell lung cancer (NSCLC), and other malignancies, a significant proportion of patients experience limited or no clinical benefit.5–6 Similarly, CAR T-cell therapy has shown curative potential in hematologic malignancies but has faced challenges in solid tumours due to limited trafficking, antigen heterogeneity, and immunosuppressive TMEs.7 Moreover, potent T cell activation can trigger cytokine release syndrome (CRS), a life-threatening inflammatory response characterised by uncontrolled cytokine release, and other immune-related adverse events (irAEs).8 This variability in
response has intensified the search for immune-related biomarkers capable of guiding immunotherapy use.
Biomarkers Provide Insights into the Tumour-immune Landscape and Therapy Success
Immune-based biomarkers are particularly attractive because they reflect the functional state of the immune system both within the tumour microenvironment (TME) and systemically.9 These biomarkers may include immune checkpoint proteins (e.g., PD-1), cytokines and chemokines, circulating immune cell subsets, and more. For instance, elevated PD-L1 expression on tumour or immune cells has been correlated with better outcomes in patients receiving PD-1/PD-L1 inhibitors, although not universally across all tumour types.6
Similarly, cytokines are increasingly recognised as important indicators of immune activation or suppression. Measuring these cytokines in plasma or serum offers a minimally invasive window into systemic immune activity and has shown promise for both prognostication and early toxicity detection. For example, some studies have indicated elevated IL-6 and TNF-α have been associated with immunosuppression and poor prognosis,10–11 whereas increased levels of IFN-γ and IL-12 correlate with enhanced immune response and improved outcomes following ICI treatment.12
The limitations of single-analyte assays in immuno-oncology are becoming increasingly apparent. Measuring one biomarker at a time using traditional ELISA-based immunoassays might be the method of choice for single-protein measurement but fail to capture the multidimensional nature of immune responses, particularly in the TME where dozens of signals interact simultaneously. Unlike static genetic markers (e.g., gene mutations), soluble proteins are dynamic and responsive to changes in disease state or therapy. This complexity
Figure 1. Schematic of key peripheral immune cells associated with immunotherapy response. MDSC, myeloid-derived suppressor cell; NK, natural killer; Teff, effector T cell; Tmem memory T cell; Treg, regulatory T cell. Taken from Nixon et al. 2019 (CC BY 4).9
Research and Development
demands a shift toward high-throughput, multiplexed technologies that can provide a comprehensive view of immune activity using limited clinical samples. Capturing immune dynamics across multiple pathways simultaneously has the potential to help guide treatment modifications and early detection of immune-related adverse events.
Multiplex Immunoassays Offer a Holistic Approach to Immune Biomarker Profiling
Multiplex immunoassays enable the simultaneous quantification of multiple soluble biomarkers, such as cytokines, chemokines, growth factors, and checkpoint proteins in a single well, with certain bead-based assays using only 12.5 µL of sample. Some of the most widely used biomarker research platforms are based on the xMAP technology, which relies on color-coded magnetic beads, each conjugated to a distinct antibody to capture different analytes.13 During the assay, these beads are mixed with a sample, and a detection antibody is added to form a sandwich complex. Flowcytometry based systems are then used to identify the distinctly colour-coded beads and quantify fluorescence, allowing simultaneous measurement of multiple analytes in a single reaction, depending on the multiplex assay panels. Compared to traditional ELISAs, multiplex assays significantly reduce sample and reagent consumption, while increasing throughput and actionable biological insight.
Preclinical and Translational Research Applications of Multiplex Immunoassays
As immune-oncology research grows increasingly complex, multiplex assays have been particularly well-suited to preclinical studies where sample volume (e.g., mouse plasma) is limited, and in clinical studies where serial monitoring of dynamic immune changes is essential. Additionally, multiplex immunoassays have been instrumental in identifying cytokine signatures that correlate with treatment response as described in the following case studies.
I. Predicting Immunotherapy Response
Understanding the cytokine landscape in multiple myeloma
(MM) can provide insights into disease progression and treatment response. Robak et al. investigated the cytokine and chemokine profiles in patients with MM undergoing bortezomib-based therapy.14 The study measured serum levels of various cytokines and chemokines using multiplex immunoassays. Findings revealed that certain cytokines, such as MIP-1α and IL-9, were associated with disease characteristics and treatment outcomes. For instance, higher levels of MIP-1α and lower levels of IL-9 levels were linked to better treatment responses, while elevated levels of IL-1Ra and IL-8 correlated with bone involvement. These observations underscore the potential of multiplex cytokine profiling in monitoring disease status and tailoring therapeutic approaches in MM.
Next, the authors conducted a study to assess whether pretreatment serum levels of specific cytokines could predict overall survival in patients with MM treated with bortezomibbased regimens.15 The study analysed serum samples from patients with MM prior to treatment, measuring levels of various cytokines using multiplex immunoassays. The results indicated that higher pretreatment levels of IL-13 and IL-4 were associated with improved overall survival, while elevated levels of IL-1 receptor antagonist (IL-1Ra) negatively impacted survival outcomes. These findings suggest that pretreatment cytokine profiling via multiplex immunoassays can provide valuable prognostic information, potentially guiding therapeutic decision-making in MM.
II. Early Cytokine Profiling in CAR T-cell Therapy
In another study, researchers developed a humanised mouse model to investigate the pathophysiology of CRS.16 This model involved the engraftment of human immune cells into immunodeficient mice, followed by administration of CAR T-cells targeting specific tumour antigens. The model successfully replicated key features of CRS observed in patients, including elevated levels of proinflammatory cytokines. Multiplex immunoassays were employed to quantify a panel of human cytokines in the serum of these mice.
Research and Development
The analysis revealed significant increases in cytokines such as IL-1, IL-6, and TNF-α following CAR T-cell infusion, mirroring the cytokine profiles seen in human CRS cases. Furthermore, the study demonstrated that blockade of IL-1, IL-6, and TNF-α signaling, using anakinra, tocilizumab, and adalimumab respectively, lead to CRS symptom alleviation without affecting the anti-tumour activity of CAR T-cells.
This research emphasises the utility of multiplex immunoassays in dissecting the complex cytokine networks involved in CRS. By providing a comprehensive cytokine profile, these assays facilitate the identification of key inflammatory mediators and potential therapeutic targets.
III. Circulating Cytokine Profiles Predict Benefit of Immune Checkpoint Inhibitors in NSCLC
ICIs have transformed the treatment landscape for NSCLC, yet predicting patient response remains challenging. Hu et al. explored whether circulating cytokine and chemokine profiles could serve as noninvasive biomarkers for predicting clinical benefit from ICI therapy.17 The study involved analysing plasma samples from patients with advanced NSCLC undergoing ICI monotherapy or combination therapy. Using multiplex immunoassays, distinct cytokine and chemokine profiles were identified, that correlated with clinical outcomes. Specifically, certain profiles were associated with poorer progression-free (PFS) and overall survival (OS), suggesting their potential utility in guiding treatment decisions. For example, in patients receiving ICI monotherapy, a high level of IL-6 at pretreatment, or an elevated posttreatment level of IL-6, IL-8, FGF2, CXCL10, CCR1, PDFGB, TNF, and APEX1 was associated with poor PFS, while an increased level of CXCL10 was associated with poor OS. In the ICI combination treatment cohort, a high level of IL-12, IL-17A, FGF2, VEGF, and APEX1 at pretreatment and an increase of CCL2 posttreatment were associated with poor PFS. A high pretreatment level of IL9, FGF2, PDFGB, CCL4, TFGB, and APEX1 and a posttreatment increase of IL-13, CSF2, and CCL2 levels were associated with poor OS of patients receiving combination therapy.17 These results highlight the promise of multiplex cytokine profiling in predicting the benefits of ICI therapy for patients with NSCLC.
These examples exhibit the value of multiplex technologies in both preclinical and clinical research settings. As oncology continues to expand, the ability to track multiple immune biomarkers simultaneously and comprehensively will be key to advancing personalised treatment and improving patient outcomes.
Conclusion
Despite their transformative potential, immunotherapies are hindered by resistance, toxicity, and variable patient responses. Deeper insight into the tumour-immune system interactions is essential to overcoming these challenges. Multiplex immunoassays empower researchers with the ability to examine dozens of immune markers simultaneously, yielding a holistic view of the TME landscape. The integration of multiplex immunoassays into oncology research represents a significant advancement in how we study and manage cancer. Compared to single-analyte assays, these platforms enable a high-throughput, broader view of immune activity, capturing the complex interplay of cytokines, chemokines, and soluble immune modulators that shape therapeutic outcomes.
REFERENCES
1. AlDoughaim M et al. (2024). Cancer Biomarkers and Precision Oncology: A Review of Recent Trends and Innovations. Clinical Medicine Insights.
2. Kim S K & Cho S W. (2022). The Evasion Mechanisms of Cancer Immunity and Drug Intervention in the Tumor Microenvironment. Frontiers in pharmacology, 13, 868695. https://doi.org/10.3389/ fphar.2022.868695
3. Waldman A D et al. (2020). A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nature reviews. Immunology, 20(11), 651–668. https://doi.org/10.1038/s41577-020-0306-5
4. Lipson E J & Drake C G. (2011). Ipilimumab: an anti-CTLA-4 antibody for metastatic melanoma. Clinical cancer research : an official journal of the American Association for Cancer Research, 17(22), 6958–6962. https://doi. org/10.1158/1078-0432.CCR-11-1595
5. Pardoll D M (2012). The blockade of immune checkpoints in cancer immunotherapy. Nature reviews. Cancer, 12(4), 252–264. https://doi. org/10.1038/nrc3239
6. Bagchi S et al. (2021). Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance. Annual review of pathology, 16, 223–249. https://doi.org/10.1146/annurevpathol-042020-042741
7. Cappell K M & Kochenderfer J N. (2023). Long-term outcomes following CAR T cell therapy: what we know so far. Nature reviews. Clinical oncology, 20(6), 359–371. https://doi.org/10.1038/s41571-023-00754-1
8. Kornblau S M et al. (2010). Recurrent expression signatures of cytokines and chemokines are present and are independently prognostic in acute myelogenous leukemia and myelodysplasia. Blood, 116(20), 4251–4261. https://doi.org/10.1182/blood-2010-01-262071
9. Nixon A B et al. (2019). Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential?. Journal for immunotherapy of cancer, 7(1), 325. https://doi.org/10.1186/s40425-0190799-2
10. Weber R et al. (2021). IL-6 as a major regulator of MDSC activity and possible target for cancer immunotherapy. Cellular immunology, 359, 104254. https:// doi.org/10.1016/j.cellimm.2020.104254
11. Dixit A et al. (2022). Targeting TNF-α-producing macrophages activates antitumor immunity in pancreatic cancer via IL-33 signaling. JCI insight, 7(22), e153242. https://doi.org/10.1172/jci.insight.153242
12. Garris C S et al. (2018). Successful Anti-PD-1 Cancer Immunotherapy Requires T Cell-Dendritic Cell Crosstalk Involving the Cytokines IFN-γ and IL-12. Immunity, 49(6), 1148–1161.e7. https://doi.org/10.1016/j.immuni.2018.09.024
13. Graham H et al. (2019). The genesis and evolution of bead-based multiplexing. Methods (San Diego, Calif.), 158, 2–11. https://doi.org/10.1016/j. ymeth.2019.01.007
14. Robak P et al. (2020). Cytokine and Chemokine Profile in Patients with Multiple Myeloma Treated with Bortezomib. Mediators of inflammation, 2020, 1835836. https://doi.org/10.1155/2020/1835836
15. Mikulski D et al. (2021). Pretreatment Serum Levels of IL-1 Receptor Antagonist and IL-4 Are Predictors of Overall Survival in Multiple Myeloma Patients Treated with Bortezomib. Journal of clinical medicine, 11(1), 112. https://doi.org/10.3390/jcm11010112
16. Leclercq-Cohen G et al. (2023). Dissecting the Mechanisms Underlying the Cytokine Release Syndrome (CRS) Mediated by T-Cell Bispecific Antibodies. Clinical cancer research : an official journal of the American Association for Cancer Research, 29(21), 4449–4463. https://doi.org/10.1158/1078-0432.CCR22-3667
17. Hu Y et al. (2023). Distinct circulating cytokine/chemokine profiles correlate with clinical benefit of immune checkpoint inhibitor monotherapy and combination therapy in advanced non-small cell lung cancer. Cancer medicine, 12(11), 12234–12252. https://doi.org/10.1002/cam4.5918
Vanitha Margan
Vanitha Margan, is the Global Product Manager at Bio-Rad Laboratories. She has diverse experience spanning from clinical laboratory scientist, sales, product management, and marketing in clinical diagnostics and life science fields. Her specialties include clinical chemistry, diabetes monitoring, and immunology.
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Prostate Cancer Screening: From PSA Tests to Lateral Flow Tests
The need for a UK national screening programme to enable early detection of prostate cancer is now more important than ever, with one in six men receiving a prostate cancer diagnosis in their lifetime.1 Regular screening will ensure the early detection of the disease and allow for timely treatment, ultimately improving patient outcomes. According to The Lancet Commission on prostate cancer, cases are projected to double from 1.4 million per year in 2020 to 2.9 million by 2040,2 highlighting the need for immediate action.
In the UK alone, around 12,000 men die every year from prostate cancer.3 This could be dramatically reduced by the introduction of earlier widespread testing and subsequent earlier interventions.
Public interest and media attention have surged around the call for a national prostate cancer screening programme. Notably, Sir Chris Hoy is advocating for the introduction of national screening, particularly for those with a family history of the disease, even if they are under 50.4 Rishi Sunak has joined Prostate Cancer Research UK as an ambassador to advocate for the introduction of a national screening programme for highrisk men.5
PSA Test
The current initial testing method for prostate cancer is the prostate specific antigen (PSA) blood test. PSA is a protein produced by normal as well as malignant cells of the prostate gland. PSA levels in the blood can be elevated as a result of both prostate cancer and several benign conditions, including benign prostatic hyperplasia (BPH) and prostatitis. The established PSA test does not test for the presence of prostate cancer specifically but rather measures the level of PSA present in the blood. It does not effectively detect prostate cancer at an early stage.
If an abnormally high level of PSA is revealed by the test, then the patient is typically referred for additional testing such as: an internal examination, a Magnetic Resonance Imaging (MRI) scan, a biopsy, and additional genetic tests.6 All of these testing methods add significant extra costs, causing a strain on the healthcare system.
In the UK, the National Health Service (NHS) estimates that three in four men with a raised PSA level will not have cancer.7 In the United States, the PSA test is not recommended for routine prostate cancer screening in the general population and the accuracy levels of the PSA test have been found to be extremely low.8 False-positive test results are common with PSA screening, with only about 25 percent of men who eventually have a biopsy due to an elevated PSA level being diagnosed with prostate cancer.9
Improved prostate cancer testing methods are urgently needed. Several tests are in development, primarily using blood, however, all need lab processing. These molecular tests require expertise, are logistically complex, time-consuming, and expensive.
Point of Care Testing for Prostate Cancer
An alternative testing method that could overcome these challenges is a multi-biomarker variant of the Lateral Flow Test (LFT), which can be used, and deliver results, at the point of use within minutes.
LFTs, widely recognised for their role in COVID-19 testing, are becoming increasingly versatile. One major advantage of LFTs is that the public is already familiar with self-testing due to their widespread use during the pandemic. Designed for rapid detection of specific molecules, LFTs incorporate highly specific antibodies within a plastic cartridge, removing the need for specialised, costly equipment.10
Such a test will be able to detect early-stage prostate cancer as well as distinguishing prostate cancer from BPH. LFTs would only require a naturally expressed urine sample rather than an invasive extraction of blood which would require a medical professional. This type of testing is much more accessible, user-friendly and quick, encouraging younger men to self-test for prostate cancer.
Detecting prostate cancer at an earlier stage increases the success rate of effective interventions. Biomarker LFTs provide 95 percent accuracy, are low-cost and easy-to-use in comparison to the current process. This method could be implemented to decentralise testing and support preventative health for men.
The Success of National Screening: Cervical Cancer
Evidence shows that national cancer screening programmes in the UK have been highly effective and are predicted to have saved thousands of lives. One notable example is cervical screening, available to anyone with a cervix between the ages of 25 and 64. This test detects human papillomavirus (HPV) infections and facilitates the identification of changes to cells in the cervix which could develop into cancer if left untreated. Cervical screening currently saves at least 2,000 lives each year in the UK.11
With the nationwide implementation of HPV primary testing, cervical cancer rates are expected to decline, with more lives predicted to be saved. In England, HPV primary screening is estimated to reduce cervical cancer cases in those aged 25–64 by 23.9 percent, preventing unnecessary procedures and improving patient outcomes.12 The evident success of the cervical screening programme further reinforces the importance of introducing a national screening programme for prostate cancer to increase early detection of the disease and reduce the mortality rate.
The Future of Improved Screening Methods
The urgent need for improved prostate cancer screening methods
cannot be overlooked as there is widespread attention on the call for a national screening programme and cases continue to rise globally. The limitations of the PSA test can be addressed with lateral flow tests which are more accurate, accessible, and cost-effective. Given the proven success of national screening programmes, introducing a nationwide screening programme for prostate cancer could enable earlier detection, save lives, and alleviate pressure on healthcare systems.
REFERENCES
1. https://www.cancerresearchuk.org/health-professional/cancer-statistics/ statistics-by-cancer-type/prostate-cancer, visited on 26 Mar 2025.
2. https://www.mrcctu.ucl.ac.uk/news/news-stories/2024/april/prostatecancer-cases-expected-to-double-worldwide-between-2020-and-2040new-analysis-suggests/, visited on 26 Mar 2025.
3. https://www.cancerresearchuk.org/health-professional/cancer-statistics/ statistics-by-cancer-type/prostate-cancer, visited on 2 Apr 2025.
4. https://www.sunderland.ac.uk/more/news/university-news/2024/ prostate-cancer-screening-chris-hoy/, visited on 26 Mar 2025.
5. https://www.prostate-cancer-research.org.uk/rishi-sunak-joins-prostatecancer-research-as-an-ambassador/, visited on 26 Mar 2025.
6. https://www.nhs.uk/conditions/prostate-cancer/diagnosis/, visited on 27 Mar 2025.
7. https://www.royalmarsden.nhs.uk/what-most-accurate-test-prostatecancer, visited on 27 Mar 2025.
8. https://www.cancer.gov/types/prostate/psa-fact-sheet, visited on 27 Mar 2025.
www.journalforclinicalstudies.com
10. https://www.theguardian.com/science/2025/mar/09/how-lateral-flowtests-are-becoming-a-diagnostic-gamechanger, visited on 26 Mar 2025.
11. Landy, R., Pesola, F., Castañón, A. et al. Impact of cervical screening on cervical cancer mortality: estimation using stage-specific results from a nested case–control study. Br J Cancer 115, 1140–1146 (2016). https://doi. org/10.1038/bjc.2016.290
12. Castanon A, Landy R, Sasieni P. By how much could screening by primary human papillomavirus testing reduce cervical cancer incidence in England? J Med Screen. 2017.
Dave Taylor
9. Grubb RL 3rd, Pinsky PF, Greenlee RT, et al. Prostate cancer screening in the Prostate, Lung, Colorectal and Ovarian cancer screening trial: Update on findings from the initial four rounds of screening in a randomized trial. BJU International 2008; 102(11):1524–1530. [PubMed Abstract]
Dave Taylor, CEO, Valley Diagnostics, is an entrepreneur with international experience working in IoT, and complex web and app development. He entered lateral flow and med tech as founder and COO of Bond Digital Health, developer of a lateral flow data capture and data management platform. Valley Diagnostics was the logical extension to this and gathers a number of key colleagues with expertise in the field of biomarker research and lateral flow development.
Email: dave.taylor@valleydiagnostics.co.uk
Modern Data Management: The Foundation for Life Sciences Innovation
Data in life sciences is more than a mere byproduct of research; it is a driving force behind innovation. By harnessing extensive datasets, organisations can speed up drug discovery, refine precision medicine, and improve operational efficiency. This shift has transformed data management into an essential cornerstone rather than just a technical tool. Although artificial intelligence (AI) continues to captivate the industry, the true cornerstone of successful digital initiatives lies in mastering data control.
Yet, without proper oversight, the risks are substantial. For instance, the EU’s AI Act imposes severe penalties on life sciences companies deploying non-compliant systems. In this context, data is no longer merely a resource but a core strategic asset requiring active protection and management.
The Growing Data Opportunity
The scale of data in life sciences is staggering. Pharmaceutical firms partner with thousands of study sites and tens of thousands of trial participants. A study by Tufts University found that Phase III clinical trials now generate an average of 3.6 million data points, tripling the volume collected a decade earlier. Amid this deluge of information, ensuring timely access to the right data is crucial to optimise R&D efforts, while reducing the time spent on data preparation and management.
Fragmentation compounds the challenge. A 2024 survey by Informatica shows that 41% of organisations have 1,000 or more data sources and nearly 60% use an average of five tools to manage them. As data increases in volume and becomes more fragmented, the need for a single consolidated solution to manage it becomes even clearer.
By consolidating data, companies can unlock numerous benefits. Sales teams can better understand trends and factors influencing their target accounts, procurement can leverage improved data visibility to collaborate more effectively with vendors and partners, and corporate M&A teams can achieve faster time to value by integrating an acquired company’s data, systems and applications more efficiently. As a result, drug discovery becomes more efficient, outcomes become more predictable and the insights from analytics become more reliable. At its core, success depends on robust data management practices that prioritises quality, accessibility, governance and protection.
Overcoming Regulatory Challenges
Life sciences is a highly regulated industry, requiring compliance with international standards, such as the CIOMS International Ethical Guidelines, ICH E6 Guideline for Good Clinical Practice and PhRMA Principles for Clinical Trials and Communication of Results.
The introduction of the European Union’s AI Act, which came into force on 1st August 2024, adds another layer of complexity. Managing requirements across overlapping regimes makes establishing a single source of truth for patient data not just best practice but a business imperative. Effective data management is especially crucial to meeting the AI Act's stringent governance and traceability requirements.
Consolidating data management under one platform allows life sciences firms to define clear responsibilities, policies and processes while streamlining workflows. A unified approach to metadata management ensures a consistent application of data protection and privacy measures, enabling companies to automate compliance and navigate the complexities of evolving regulations with confidence.
Preparing for AI Adoption
Research from Cognizant and Oxford Economics forecasts that enterprise AI projects will leap from today's experimentation to a period of confident adoption by 2026. The life sciences industry must prepare for this. Proper stewardship of patient and intellectual property data is a foundational element of any digital healthcare initiative, particularly when using AI, where transparency and reliability of outputs remain ongoing concerns.
AI can also play a transformative role in data management by automating complex tasks such as data extraction, classification and validation. This improves accuracy and accelerates compliance. In this sense, AI and data are interdependent—AI needs highquality data, and modern data management increasingly relies on AI-driven efficiencies.
Scalable software with integrated AI engines can take on the heavy lifting of data ingestion, integration, governance and quality management. Advanced tools also allow non-technical users to access data products tailored for specific use cases like drug discovery, fostering greater collaboration and innovation.
For example, we worked with a global pharmaceutical brand who needed to advance its multi-year strategy to migrate its onpremises solutions to the cloud. Moving to the next phase required a single, trusted source of data for analytics that could be leveraged across the enterprise. The firm implemented an AI-powered SaaS data management and governance solution to consolidate data management and shift their legacy supply chain analytics to the cloud. As a result, development processes were simplified, delivering notable reductions in cost and time. Five million records could be loaded in 4 hours, vs. 19 hours required by the previous system. Since deployment, the new cloud-based data management solution has helped cut the time to manage new orders by 50%.
Establishing Trust and Taking Control
Life sciences is no stranger to innovation, but today its eureka
moments depend on data – data that is high-quality, compliant, secure and governed. Data management in a highly regulated global industry remains complex and costly. Companies must break down silos, integrate external data and comply with evolving regulatory landscapes. They also need to handle growing complexity in data types, formats and storage, while ensuring integrity and security. This is essential to foster trust and an environment of innovation.
Achieving this requires modernising data management to empower R&D teams to unlock new possibilities. From streamlining drug discovery processes to precisely targeting discreet patient populations, robust data systems can drive impactful change. As advanced analytics and generative AI drive a new wave of innovation, it's essential that data management systems rise to meet the industry’s rising demands for speed, quality, compliance and innovation.
RESOURCES
1. EU AI Act: How to Create an Effective Data Governance Strategy for Your Organization - https://www.informatica.com/resources/articles/eu-aiact-data-governance-strategy.html
Rohit Dayama is a Global Client Partner within the Life Sciences practice at international professional services company Cognizant. With over 20 years of experience in consultancy services, he has specialised in the design and execution of complex digital transformation initiatives, leveraging AI and tech innovation to help leading pharma companies bridge the gap between technology and sciences, grow their businesses, and improve patient outcomes. At Cognizant since 2017, Rohit has been serving multi-national clients as a strategic partner in establishing world-class digital capabilities, managing a variety of business transformations and achieving strong account growth.
EMA PMS: Capitalising on Centralised Medicinal Product Data – Which Way Now for Regulatory Leaders?
By 2026, IDMP compliance will become more challenging for all pharmaceutical companies operating in the EU, due to the broader adoption of the IDMP data model. Now, all eyes are on the EMA Product Management Services (PMS) – or they should be. EMA PMS is essentially a centralised platform, designed to streamline the management and exchange of medicinal product information across the EU, and beyond. MAIN5’s Michiel Stam considers the pharma industry’s best next steps.
The “bigger picture” benefits of the ISO Identification of Medicinal Products (IDMP) - international standards for uniquely identifying and describing medicinal products - have been well articulated over the years. Yet it is through the imminent rollout of Product Management Services (PMS) by the European Medicines Agency (EMA) that this wider reality will begin to take shape – in the European Union at least. After much grappling with intricate vocabulary standardisation and compliant data structuring, pharma companies marketing products in the EU will start to glimpse the potential (ultimately) of more tightly linking authorised medicines, product pack and data carrier IDs with the latest marketing status details, electronic patient information and summaries of product characteristics.
As a centralised European resource, holding extensive information on both EU-wide and nationally authorised products, EMA PMS is designed to streamline the management and exchange of medicinal product information across the EU and beyond. Like the older and more established eXtended EudraVigilance Medicinal Product Dictionary (xEVMPD), PMS (its successor) will form a comprehensive database enabling the consistent and accurate identification of medicines internationally. It will also support pharmacovigilance and assist in regulatory activities. Crucially, PMS will manage product data based on the ISO IDMP standards, ensuring greater harmonisation and richer detail in the information registered and maintained.
For pharma companies/marketing authorisation holders (MAHs), being ready for and compliant with PMS is paramount, yet continues to present several challenges. Many of these are linked to data preparation, most notably enrichment of existing data through xEVMPD or the PMS user interface to ensure quality, completeness and accuracy and consistent use of IDMP-compliant terms.
Where We Are Now
EMA’s current timescales specify use of PMS for data enrichment related to critical medicines by the end of 2025, and the end of 2026 for non-critical products, so there isn’t long to bring data in line. Already, EMA is encouraging submissions of information on the ingredients and strengths in product compositions based on Module 3 of the registration dossier, rather than on local Summaries of Product Characteristic (SmPCs) which can vary in their terminology. Meanwhile submission of data carrier identifiers is supported as of Q2 this year.
Up to now, much of the data enrichment work to existing submissions has had to be done manually however, via EMA’s
Product Lifecycle Management (PLM) Portal. Once EMA has established a fully operational application interface (API), registered industry and network users will be able to view and edit medicinal product data directly via their own database systems. Currently the API is available in read-only mode, allowing users to view (but not edit) data. EMA is gradually rolling out edit functionality, allowing registered users to modify specific datasets related to non-centralised marketing authorisation.
Until the full specifications have been finalised (full “write” capabilities are due sometime from 2026 onwards, with a minimum viable product expected towards the end of this year), both software vendors and the pharma industry remain somewhat in limbo. Yet, as with previous phases of EMA’s IDMP rollout and associated guidance, waiting for concrete requirements is risky, so companies will need to take a middle ground and progress as and where this makes sense.
Knowing Where to Aim: Scale and Scope
What is known is that the scale of companies’ compliance capabilities will be critical, globally. Beyond the EU and Europe more widely (including Switzerland), the national health authorities in the US, Brazil and Canada are among those that have committed to embracing ISO IDMP standards with a view to harmonised global medicines definitions and information exchange. Global harmonisation was the original vision for the standards, after all. The closer and “truer” MAHs can stay to pure ISO standards, then, the better their chance of large-scale interoperability, automation and seamless compliance down the line (versus having to cater for multiple variations in requirements by region or country).
The other safe assumption is that inter-departmental collaboration will become increasingly important to realise the ultimate scope of IDMP’s ambitions. It is well accepted that, for IDMP to succeed and deliver appreciable value, associated efforts must be seen as more than a regulatory undertaking. So, while most IDMP data is currently derived from Regulatory source documents as a legacy of documentdriven processes (an approach that fulfils most of the needs of EMA Iteration 1), this is not a sustainable strategy.
Achieving IDMP data’s full potential, and delivering real business value, requires a company-wide effort. Much of the core data originates in other domains, such as R&D, Clinical, PV, Medical Affairs, and Industry. Not least for the sake of efficiency, it follows that data should be managed at the source within the function that generates it (rather than extracted from documents retroactively).
Making this change will demand strong cross-functional data governance, and technical- and semantic interoperability. Starting with data domains that show visible cross-functional impact is a good approach. These successes can then serve as positive examples of what is possible, to demonstrate progress and justify ongoing collaboration.
PMS & Internal Data Alignment Challenges
At a more technical level, there are other data challenges beyond
the current lack of API connection, the absence of software support, and of a holistic view across product data. As companies begin to navigate the initial transition to PMS, they are encountering a data mismatch between xEVMPD, SIAMED (EMA's internal database), and evolving PMS data – discrepancies resulting from migration and from pack-size splitting, for instance. A general lack of control over data quality and sources is compounding the issue, in addition to the EU centricity of the immediate IDMP implementation.
A lack of current compliance with the FHIR standard (standing for Fast Healthcare Interoperability Resources – designed to facilitate reliable exchanges of healthcare information between different systems) is also presenting complications. Currently, FHIR messages extracted through the PMS user interface are not FHIR compliant, requiring manual workarounds to ingest them. This situation is likely to persist until the PMS API FHIR v5 upgrade, which isn’t due until 2026 at the earliest.
Moreover, is the reality that nothing is set in stone. EMA is known to be updating some of the reference terminology in the controlled vocabularies in the Referentials Management Services (RMS) system, for instance – around special precautions for storage and shelf life for materials, with an expectation that this will be ready for PMS implementation. This ongoing evolution of PMS’s scope, added to system changes, migration and synchronisation challenges, service desk dependency and ongoing life cycle management in different source systems, add further data alignment challenges.
Finally, reliance on transitional xEVMPD/SIAMED-based processes and a short-term PMS roadmap (which lacks a defined 'To-Be’ Target Operating Model) could present a barrier to companies looking to progress with strategic planning and effective data enrichment.
An “IDMP readiness” survey commissioned by Pistoia Alliance last year confirmed pharma’s perceptions about barriers to fully harnessing the benefits of the standards.2 It found that manual data collection; data silos; and a lack of data integration across systems present the biggest hurdles in product data management.
Companies’ intentions are clear though. The same respondents (senior Regulatory professionals) highlighted the importance of overcoming cross-functional data integration issues, where longterm plans included integrating regulatory data with supply chain
and manufacturing systems – typically within the next year or two – underpinned by IDMP standards adherence.
Working Around Obstacles that Persist
Given all the potential, yet the remaining practical barriers to progress, the pharma industry needs to balance the strategic with the tactical in planning its next steps.
In its broadest definition, ISO IDMP promises extensive benefits for all, and that applies as much to internal pharma efficiencies as to healthcare providers and patients who can expect a safer and more convenient experience following extensive harmonisation and enrichment of medicinal product information. Strategic and practical operational benefits for pharma include enhanced data quality; simplified, centralised compliance; improved operational efficiency (via robust, centralised data management); risk mitigation; and future readiness. PMS will play a significant role in enabling all of this, making it imperative to put in the groundwork now.
REFERENCES
1. EMA Write PMS API implementation Guide, Version 1 - European Medicines Agency, January 2025: https://www.ema.europa.eu/en/ documents/regulatory-procedural-guideline/european-medicinesagency-write-pms-api-implementation-guide_en.pdf
2. Accelerating Digital Transformation in Pharma with IDMP: An industry benchmark report on the status of IDMP standards implementation in Pharma and the role of the IDMP Ontology for accelerating digital transformation, Pistoia Alliance, 2024: https://marketing.pistoiaalliance.org/hubfs/IDMP%20 Pistoia%20Alliance%20Report%202024%20(5).pdf
Michiel Stam
Michiel Stam is a management consultant and senior regulatory expert at MAIN5 with 15 years of experience in Regulatory Information Management (RIM) and IDMP. MAIN5 is a European consulting firm specialising in digitally-enabled change for Life Sciences R&D organisations.
The Potential for Deep Learning Technology in Clinical Trials
Deep learning technology can be very strong at identifying complex patterns within large data sets and mapping them to simple classifications like diagnosing a certain disease, or whether a disease will progress, and how soon. This information is incredibly useful in the process of clinical trials and drug development, where the end goals are to test and discover if treatment is working and how well.
Using deep learning tech in clinical trials is the ideal application to speed up drug development and get more effective drugs to the clinic faster, ultimately impacting patients positively. To understand medical treatments effectively, especially for conditions like lung and heart disease, there are often large and complex data sets that need rigorous processing, a hard task for any clinician or even team clinicians to undertake.
With current developments in AI, we now can spot patterns, link disease outcomes, and analyse images at super-speed across hundreds and thousands of data points. Not only this, but deeplearning has the potential to improve clinical trial workflow and processes and improve outcomes of drug discovery.
Clinical Trial End Goals –Is There a Need for Deep Learning?
Clinical trials are the cornerstones of medicine, having been implemented since the 1900s, and are a crucial process in drug development and medicine. The end goal of a clinical trial is to provide valuable insights about the effectiveness of a treatment and whether or not the treatment makes a significant positive impact on a person’s health.
Historically, we have been able to map data and disease outcomes through software tools, spreadsheets and frameworks. While we have been able to create and administer effective new medicines, there is clearly a need to improve the processes, especially for diseases where there are little or no effective therapies, and problems remain in those patient populations. The process of going from an identified target to an approved drug can take over 10 years and cost over a billion dollars in lung diseases, and less than 5% of drugs make it through from early stage to approvals.
During a clinical trial, it is common to track clinical endpoints such as overall survival of a patient, progression-free survival or quality of life. Clinicians will also use data from the trial to generate hypotheses for future trials, such as biomarker responses and inform aspects of drug discovery. This can be tricky to track, complex, and multi-faceted, and it may include many different types of data. Deeplearning tech can be programmed to identify patterns across different forms of data, perfect for mapping multiple endpoints at once, and reducing human error.
Deep Learning is Good at Analysing
Medical Images
We've recently seen a surge in deep learning applications to analyse medical images, particularly in CT scans, and the results have been impressive.
Deep-learning-based models gave significant benefits in detecting abnormal findings in CT scans and in emergency departments,1 improving diagnostic performance, especially for less experienced clinicians. 2 Researchers have also developed deep learning models to predict the severe progression of COVID-19 based on CT scan images, achieving high discriminative ability.3
In lung diseases, we have seen how automated models have also shown to rapidly segment CT scans in Idiopathic Pulmonary Fibrosis (IPF), providing prognostic near and long-term information, which could be used in routine clinical practice or as key trial endpoints.4
AI can be the ‘Data Curator’
Data used in clinical trials and drug development can often be fragmented across different systems, with many parameters to process and curate together at once. Deep learning, as we know, is an excellent data curator and can support us with this problem.
Deep learning platforms are usually created by expert teams of computer scientists working closely with biologists and doctors to define what parameters are most useful and important for the bespoke platform, if the platform isn’t built well, it won’t do the best job. Bringing together excellent minds in both computation and medicine and focusing on shared goals is key in driving tech integration in medicine.
Another important feature of this is the quality and process of curating the data. Data processing and integration is the key to success and must be conducted properly. This will stem from appropriate stakeholder and database integration, creating and building the right infrastructures, and establishing trusted relationships with healthcare providers who house much of this data. There needs to be an open dialogue and transparency about how data will be used (for patients) and realistic explanations for clinicians and scientists on the challenges that need to be addressed.
During this process, there are vast amounts of sensitive patient data being handled, and tech companies must safeguard this data with the appropriate frameworks. Features of this could include encryption, access controls, and monitoring systems to ensure that patient clinical trial data remains confidential, secure, and compliant with all relevant regulations.
Having standards and frameworks will also reduce blockers to innovation in AI and ML and enable the data to be curated properly and effectively. Clinical applications will develop more rapidly, and the process will be better for ongoing and future R&D and utilisation of the data.
Some Diseases are Particularly Hard to Treat and Understand
Let’s take a real example to illustrate the complexities of disease and the potential for technology solutions. Idiopathic pulmonary fibrosis is a debilitating lung disease, which is notoriously hard to diagnose and treat. 1% of all deaths in the UK are due to lung fibrosis, and currently, there is no cure. Only two drugs have gained widespread approval for patient use: pirfenidone and nintedanib, and many patients experience significant side effects from these medications.
Understanding the disease is usually carried out by assessing CT scans and assessing changes in fibrosis, lung volume, lobar flow, and blood biomarkers.5 Although this reveals information about the disease, a group of 100 radiologists looking at the same CT scan would provide varied conclusions; almost half of patients with IPF are misdiagnosed.
And the absence of treatment is not through lack of trying. There is substantial ongoing research from the medical community for this disease. Between March 2023 and March 2024 alone, over 35 clinical studies for IPF were either initiated or planned. These studies predominantly focus on Phase 2 and Phase 3 trials, representing the critical stages where potential treatments are tested for efficacy and safety in larger patient populations.
The variation between individual patients also contributes to difficulties in treating this disease. Each person displays the disease differently, and this diversity makes it challenging to develop universally effective treatments. What works for one subgroup of patients may have limited or no effect on another, complicating the design of clinical trials and the interpretation of results.
Despite the intense research focus and numerous ongoing trials, progress in treating lung diseases like pulmonary fibrosis remains frustratingly slow. There is clearly a need for alternative tools to support clinicians, understand the disease, and discover new treatments.
Improving and Automating the Workflows and Patient Selection
As well as difficulties in understanding certain diseases, some of the most notable challenges in clinical trials are barriers related to regulatory systems, patient recruitment, and lack of budget and skilled staff for conducting clinical trials.6
80% of clinical trials face delays due to recruitment issues, leading to cost overruns.7 Deep learning methods can be useful for the task of cohort selection and patient recruitment. They can be used as a filter for cohort selection for any clinical trial with a minimum of human intervention, thus reducing the cost and time of clinical trials significantly.8
We are now seeing an increasing number of pharmaceutical companies integrate AI to reduce trial failures and delays, and rising investments and collaborations are estimated to drive the growth of this market. This area is another interesting application of AI and could reap substantial benefits for improving efficiency in clinical trials.
Deep Learning “Platform” Tech Integration is Already Happening
How are platform technologies being integrated into clinical trials now, and do they have the potential to become long-term solutions? AI-driven drug discovery for large-scale candidate screening for early-stage drug discovery is already happening on a wide scale, with a multitude of partnerships between biotech discovery machines and pharma and increased attention in this field.
Using AI beyond early-stage discovery, particularly within clinical trials themselves, is becoming increasingly common in drug development too. The AI-based clinical trials solution provider market size is expected to reach around USD 6.89 billion by 2034.9
In the field of lung disease, deep learning tech was recently deployed for the first time in a major progressive pulmonary fibrosis trial, and continued partnerships are allowing the deployment and evaluation of AI for treatment development for lung disease.10
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Realising the Potential While Considering Cautions
Although there is clearly potential for deep learning, we, of course, need to approach this with caution. It’s imperative that we consider patient safety, address concerns around data biases and safeguard effectively. The end goal is always to protect patients, and that should be a primary focus at all times.
But with proper integration, the potential is exciting. If success continues, we could be looking at new treatments for very complex diseases and translating this to other disease areas, and even preventing them from happening in the first place. The industry might even look to integrate deep learning with AI-enabled wearable devices and sensors for even more accurate data collection and analysis throughout the trial process.
Strong partnerships and collaborations between tech companies and healthcare will be key in ensuring this is carried out effectively, but this is an exciting time for the industry. I’m looking forward to seeing us realise the potential of deep learning in lung disease and beyond.
Muhunthan Thillai, CEO, Qureight, has over 20 years of experience in thoracic medicine and is a board-certified Pulmonologist. He qualified as a doctor from Imperial College London and undertook specialist medical training in Oxford, where he was appointed as a member of the Royal College of Physicians. He was awarded a PhD in molecular immunology from Imperial College London before being appointed as a consultant physician. He was previously Director of Interstitial Lung Diseases at Royal Papworth and Addenbrooke’s Hospitals Cambridge UK and is currently an Honorary Associate Professor at the University of East Anglia.
Muhunthan Thillai
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More Insight from Fewer Patients: Advancing Rare Disease Trials with the Net Treatment Benefit
Rare disease clinical trials face a confluence of challenges: limited patient populations, heterogeneity in disease progression, and often a lack of established outcome measures. Yet the stakes involved are exceptionally high. For the over 300 million people living with a rare disease worldwide,1 most of whom lack access to effective therapies, each trial represents a vital opportunity – not just to generate evidence, but to shape treatments that meaningfully improve the patients’ lives.
Traditional clinical trial designs, typically focusing on a single primary endpoint, are often ill-suited for this complex task. They frequently simplify the multidimensional reality of how patients, caregivers, and clinicians define meaningful treatment benefit into a single dimension. For instance, a therapy may slow disease progression but negatively impact quality of life; it may show modest improvement in the main endpoint yet substantially improve fine motor functions or reduce intolerable side effects. In rare diseases, where patient numbers are limited and the burden of participation is high, trials must do more than test hypotheses – they must produce data that reflects what matters most to those affected.
The Net Treatment Benefit (NTB) emerges as a patient-centric, statistically rigorous approach that allows for the prioritisation and integration of multiple outcomes into a single, interpretable measure of treatment effects.2 When coupled with early engagement from patients, investigators, and key experts to define outcome hierarchies, NTB offers a practical path to trials that are both more efficient and more aligned with real-world needs.
The Challenge of Endpoint Selection in Rare Diseases
One of the most persistent bottlenecks in rare disease trial design is the selection of an appropriate primary endpoint. In common conditions, regulatory precedent and existing clinical guidelines typically point the way. In rare diseases, the path is often uncharted.
Consider Pompe disease or Duchenne muscular dystrophy. Patients, families, and clinicians may prioritise very different outcomes depending on the disease stage: respiratory function, ambulatory capacity, ability to feed independently, fatigue, or even cognitive symptoms in syndromic variants. Designing a trial around one of these clinical outcomes risks overlooking the others – and worse, dismissing a therapy that offers multidimensional benefit simply because it falls short on a single axis.
This issue becomes more acute when regulators require “hard” clinical outcomes, such as time to death or forced vital capacity, that may not be the most relevant for early- or mid-stage patients. Many rare diseases progress slowly or unpredictably, making it difficult to observe changes in a single outcome within the limited duration of a trial.
By forcing sponsors to choose one outcome as the sole measure of success, traditional designs risk misrepresenting the true value of an intervention. This not only complicates regulatory evaluation but can discourage further investment in promising therapies.
Why Net Treatment Benefit Is a Game-changer
Net Treatment Benefit, grounded in the methodology of Generalised Pairwise Comparisons (GPC), offers a solution to these challenges. Rather than selecting a single endpoint, NTB enables trials to incorporate multiple outcomes – each assigned a position in a predefined hierarchy reflecting clinical and patient priorities.
In essence, NTB calculates the difference between the probability that a randomly selected patient in the treatment group does better across the prioritised outcomes than a randomly selected patient in the control group, and the reverse. This yields a single, interpretable statistic that reflects the totality of the evidence.
The statistical advantages are compelling. By incorporating multiple relevant outcomes into the analysis simultaneously, NTB makes fuller use of the collected patient data, effectively capturing more comprehensive information about treatment effects. This is especially critical in rare disease trials, where small sample sizes are the norm. More efficient use of available data means improved power to detect clinically meaningful differences – potentially with fewer patients or shorter trial durations.
Specifically in the rare disease domain, a post-hoc analysis of the randomised, double-blind, phase 3 COMET trial, prioritising the primary (forced vital capacity) and secondary outcome (6MWT), provided evidence of efficacy of avalglucosidase alfa therapy (n = 51) over alglucosidase alfa (n = 49) in Pompe disease, while the original analysis failed to significantly show superiority of treatment on the primary endpoint.3
Prioritising Outcomes with Stakeholder Input
What truly sets NTB apart is not just its statistical sophistication, but its ability to formalise clinical and patient preferences in the design phase of a trial.
In rare diseases, the need for such an approach is acute. Disease burden varies widely across individuals, and the diversity of symptom trajectories makes a one-size-fits-all endpoint inadequate. Engaging stakeholders early – patients, caregivers, site investigators, and treating clinicians – enables trial sponsors to co-create outcome hierarchies that reflect the lived experience of the disease.
Structured preference elicitation methods, such as discrete choice experiments or ranking exercises, can yield clear insights into which outcomes matter most and in what order. However, these traditional approaches can be cumbersome, often requiring large numbers of respondents. Innovative methods are therefore needed to simplify
the process and reduce the burden, especially in rare diseases with limited patient populations.
By building consensus around outcome prioritisation upfront, sponsors not only create trials that are more meaningful – they reduce the risk of post-hoc disputes about relevance and increase the likelihood that trial data will resonate with regulators, payers, and clinicians.
Reducing the Burden on Patients and Families
Rare disease trial participants and their families often carry a disproportionate burden: frequent travel, complex assessments, and uncertainty around the value of their contribution. Any opportunity to streamline trials without compromising scientific integrity is not just a design consideration – it’s imperative.
NTB can reduce this burden in two important ways. First, by increasing statistical efficiency, NTB-based designs may require fewer patients to reliably detect whether a treatment is truly effective. Second, by allowing multiple outcomes to contribute to the primary analysis, NTB helps ensure that more of the collected data is meaningfully used, reducing waste and enhancing the value of each patient assessment.
Moreover, NTB allows the inclusion of clinically meaningful thresholds – minimum differences that matter to patients – in the analysis. This means that only differences considered meaningful are used to distinguish between outcomes, while smaller, less relevant differences are treated as neutral. This helps the analysis focus on what truly matters and adds another layer of patient-centricity, ensuring that the trial’s conclusions reflect not just differences, but meaningful ones.
Supporting Regulatory and HTA Pathways
While the NTB has yet to become a standard primary analysis method in rare disease regulatory submissions, it is already well established and familiar to regulators in other therapeutic areas.
The ATTR-ACT trial for transthyretin amyloid cardiomyopathy used an NTB-like approach to prioritise time to death over time to hospitalisation – highlighting how multidimensional benefitrisk profiles can be formalised in regulatory-grade evidence.4 As regulatory agencies continue to emphasise patient-focused drug development (PFDD),5 particularly for conditions where unmet need is high, there is a growing appetite for approaches that reflect the realworld complexity of treatment benefit.
Importantly, NTB is also well-suited for health technology assessments (HTAs). These bodies are increasingly requiring quantitative evidence of value beyond clinical efficacy – especially in Europe and Canada, where quality-adjusted life years (QALYs) and other composite measures are common. Because NTB summarises multiple prioritised outcomes into a single interpretable measure, it aligns well with the demands of HTA dossiers and payer value frameworks.
In rare diseases, where treatments are often high-cost and subject to scrutiny, demonstrating comprehensive benefit-risks balance quantitatively is critical not only for approval but for access.
Fostering
Adoption and Continuation
of Development
An often-overlooked benefit of NTB in rare diseases is its potential to de-risk development decisions. When phase 2 trials are underpowered due to small sample sizes, NTB can detect more signal from limited data. Sponsors can make better-informed go/no-go decisions, reducing the likelihood of prematurely abandoning promising therapies or investing heavily in interventions with narrow appeal.
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In turn, this supports better engagement with investors and partners. A clear, well-structured NTB analysis – grounded in patient and clinician priorities – can be a persuasive element in fundraising and partnership discussions. It also supports clinicians in understanding which patients are most likely to benefit, based on outcomes that mirror their own treatment goals.
Conclusion: Making Rare Disease Trials Work for Patients
For decades, rare disease trials have struggled under the weight of conventional clinical trial methodologies not designed for their constraints. The use of a single endpoint often obscures meaningful multidimensional benefits. It increases the likelihood of inconclusive results, slows development, and most importantly, can fail to serve the patients who volunteer their time, energy, and hopes.
Net Treatment Benefit, supported by robust stakeholder engagement in the selection and prioritisation of outcomes, offers a viable, scalable, and scientifically rigorous solution. It allows for the integration of what matters most – survival, function, quality of life, and tolerability – into a single evaluative framework. And in doing so, it makes trials more efficient, more informative, and more aligned with real-world treatment decisions.
As the rare disease community continues to push for faster, more meaningful innovation, the integration of NTB into early trial design is not just a statistical refinement. It is a strategic imperative – one that places patients, not endpoints, at the centre of progress.
REFERENCES
1. Rare Diseases International - https://www.rarediseasesinternational.org/ living-with-a-rare-diseaseRare Diseases International – https://www. rarediseasesinternational.org/living-with-a-rare-disease
2. Buyse, M., Verbeeck, J., Saad, E.D., Backer, M.D., Deltuvaite-Thomas, V., & Molenberghs, G. (Eds.). (2025). Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781003390855https://doi.org/10.1201/9 781003390855https://doi.org/10.1201/9781003390855
3. Verbeeck, J., Dirani, M., Bauer, J. W., Hilgers, R. D., Molenberghs, G., & Nabbout, R. (2023). Composite endpoints, including patient reported outcomes, in rare diseases. Orphanet Journal of Rare Diseases, 18(1), 262.
4. Maurer, M. S., Schwartz, J. H., Gundapaneni, B., Elliott, P. M., Merlini, G., Waddington-Cruz, M.,...& Rapezzi, C. (2018). Tafamidis treatment for patients with transthyretin amyloid cardiomyopathy. New England Journal of Medicine, 379(11), 1007-1016.
5. FDA, Patient Focused Drug Development Series - https://www.fda.gov/drugs/ development-approval-process-drugs/fda-patient-focused-drug-developmentguidance-series-enhancing-incorporation-patients-voice-medical
Tom Mann
Tom Mann is a Clinical Solutions Engagement Lead at One2Treat. He brings over 15 years of experience in tech start-ups and scaleups, where he played a pivotal role in driving customer engagement, marketing initiatives, and strategic partnerships. At One2Treat, Tom's expertise and fresh perspective are invaluable as we continue to develop solutions that integrate key patient-relevant outcomes into a single holistic treatment assessment. With a strong background in SaaS companies, Tom has a deep understanding of customer needs. His ability to translate those needs into actionable solutions will be essential in expanding One2Treat’s reach and impact within the clinical research landscape. At the same time, he ensures that our approach remains both innovative and patient-focused.
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The Rare Voice that Matters Most
Who better to shape clinical research in rare disease than those living with the conditions. Patient and public involvement in clinical research is crucial for ensuring that clinical research is aligned with the actual needs and experiences of those affected by the diseases, particularly rare diseases. These conditions often lack robust research data due to small patient populations, making patient insights invaluable. Early engagement with patients can influence so much from the study design to recruitment strategies and ensure that the outcomes measured are relevant to those living with the disease.
As a first step collaboration between all parties involved in the study (the pharmaceutical company, Clinical Research Organisation (CRO), and patient advocacy group) is essential from the outset of clinical trial development, especially for rare diseases where patient needs are often underrepresented. Rare disease patients experience unique challenges, and without their input, studies can overlook critical aspects of their daily realities, such as symptom variability, treatment burden, and practical considerations like travel to site, overnight stays, finance etc.
Hearing the patient voice is becoming more and more important as funders realise the importance of having this patient engagement to ensure the treatments coming through will provide an improved quality of life for the patients.
When Patients Lead, Progress Follows
Patient advocacy groups bring an in-depth understanding of these nuances, helping trial designers develop protocols that are not only scientifically robust but also people centred. Engaging with these patient groups early enables researchers to align study objectives with what truly matters to patients, leading to more effective recruitment, retention, and ultimately, meaningful trial outcomes that better reflect patient needs.
Rare Disease Research Partners (RDRP) is a UK-based specialist research and trial logistics provider and a subsidiary of the MPS Society patient organisation. The MPS Society supports children and adults with MPS (mucopolysaccharide disorders) and related conditions by raising awareness, fundraising, and providing vital support to its members.
In 2005, the MPS Society noticed a growing gap in rare disease research. While research was increasing, there was little understanding of the particular complex needs of rare disease patients and how this affected their ability to take part in clinical trials. Patients faced practical, financial and emotional barriers to joining or remaining on a study. The usual “big trial” model was not working in populations where you may only have 10 eligible patients, spread geographically across several countries whose everyday lives alone are difficult to navigate without the added demands of taking part in a trial.
These patients needed specialist support based on a sound understanding of their needs and for trials in rare disease, retention
is vital. The loss of just one patient, not only impacts the trial but also denies the patient access to potentially life-changing treatment.
RDRP was created to fill this gap, leveraging the MPS Society’s expertise in rare diseases and its belief in understanding each patient’s unique needs. The goal? To make trial participation as smooth and stress-free as possible. This approach improved the experience for patients and families while boosting retention rates.
Today, RDRP continues to collaborate with patient organisations and the industry, championing patient-focused research to shape clinical trials and create resources that truly serve the rare disease community.
CROs and Advocacy: A Formula for Patient-centric Success
A CRO can significantly influence a sponsor at the protocol development stage by advocating for patient involvement and connecting the sponsor with relevant advocacy groups, especially in rare disease studies. Working with a CRO that has established relationships with rare disease advocacy groups is particularly beneficial, as it allows for immediate access to critical patient insights and trusted networks. This partnership will influence the development of study protocols that are sensitive to patients' unique needs, improving recruitment and retention. Additionally, this close collaboration with advocacy groups helps ensure study endpoints are meaningful, generating outcomes that align to the priorities of the patient and their families and also strengthening the study’s credibility and relevance.
Practical Ways a CRO can Influence Patient Involvement
• Ensure patient feedback is incorporated into trial protocol designs utilising NHS Patient and Public Involvement and Engagement (PPIE) groups at the outset. They can feed into patient logistics (i.e. phone calls and remote assessments vs hospital attendance to reduce travel burdens) and can help identify additional endpoints such as Quality of Life (QoL) that can complement clinical endpoints. They can also help with recruitment promotion for the study.
• Working with sites that have experience of conducting rare disease trials. These sites have a deep understanding of the patient journey and can offer flexibility over hospital assessments.
• Developing patient-friendly recruitment materials. Consider using plain language and accessible format and make the material visually appealing. Consider using video as a more engaging media.
• Encourage the use of lay persons (patient advocacy) to attend steering committee meetings and provide patient newsletters as the trial progresses to keep patients and their families informed of progress
• Ensure patient feedback after trial finishes so the patient can access results.
Patient Engagement Strategies for Rare Disease
By working closely with patient organisations, we can encourage patients to take part in research through social media as well as
working with clinicians at specialist centres. It is also important that where patients and their families take part in research, they are updated as to the outcome of that research. The feedback we receive from our patient populations is that it is very frustrating that they give their time to a piece of research only to never find out how that research is going to benefit the community. This demotivates patients and families to take part in future research.
The Future of Clinical Research in Rare Disease
Currently, only 5% of rare diseases have treatments that are approved, and the UK is falling behind other nations in terms of access to these therapies.
In the UK alone, more than 3.5 million individuals are affected by one of over 7,000 rare diseases, while the numbers rise to 30 million in Europe and 300 million globally. A significant portion – around 80% – of these conditions are hereditary, with a considerable impact on children; tragically, 30% of affected children do not survive past their fifth birthday.
These chronic and complex conditions impose substantial challenges on patients, families, and caregivers, leading to reduced life expectancy and quality of life. The physical and mental health issues associated with these diseases severely affect everyday activities and overall well-being.
So how do we tackle the unmet needs of the rare disease community?
The answer is research – specifically, research that is guided and informed by those who have firsthand experience with these
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conditions. Engaging patients and their families in the research process ensures that studies are relevant and focused on the real challenges they face.
Yvanne Enever, CEO at PHARMExcel said:
“Addressing the unmet needs within the rare disease community requires a strong emphasis on research – specifically, research that is informed and guided by the experiences of those directly impacted by these diseases. CROs and Pharma must get better at not just hearing the patient voice but acting on it”.
Bob Stevens, CEO at the MPS Society said: “Together with invaluable CRO partners like PHARMExcel, we can help Sponsors to understand the benefits of involving patients in research right from the start. We can only do this by getting in the room with Sponsors and encouraging patients to tell their stories. We can also continue to lead by example, by showing the quality of data available when you ask the right questions and have the right people doing the data collection and analysis.”
If you would like to get involved in any of the initiatives mentioned or you have a project you need support on, please contact us: info@pharmexcel-cro.com and info@rd-rep.com
Benedicta Marshall-Andrew
Benedicta Marshall-Andrew (Bennie) is the Head of the Clinical Trial Support Team at Rare Disease Research Partners (RDRP), where she has worked for over a decade. Although she entered the field with no prior experience in rare diseases, Bennie has since developed a deep commitment to this area of research and can no longer imagine working anywhere else. She takes great pride in the impact her team has made in improving the experiences of patients and their families or caregivers, ultimately contributing to more effective clinical trial outcomes.
Katie Howe
Katie Howe is an experienced marketing leader with a 25+ year career spanning sectors including clinical research, cyber security, publishing, and non-profits. As Head of Marketing at PHARMExcel, she leads strategic brand development and communications for a growing CRO. Her career highlights include managing high-performing marketing teams, driving digital engagement, launching first-tomarket campaigns, and delivering measurable business impact for SMEs and global organisations alike. Katie is known for her practical, insight-driven approach and has previously held key roles at Bulletproof, Enactor Retail Tech, the IET, and St Clare Hospice. She thrives on turning complex ideas into compelling marketing strategies.
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Basket Trials for Rare Diseases: Where Innovation Meets Unmet Need
Rare diseases, which affect >30 million individuals in the United States (US), have historically taken a back seat to more common diseases such as cancer, diabetes, and heart disease in the clinical trials landscape, primarily due to trial design challenges in limited patient populations. Through public meetings and behind-the-scenes engagement with patients, the US Food and Drug Administration (FDA) has heard that regulatory flexibility is paramount to advancing drug development for rare diseases. Many of these diseases share molecular etiologies, and patients with those diseases could equally benefit from treatment with a single agent. Some of the most debilitating rare diseases affect the most vulnerable and cherished population – children. However, clinical trial design regulations and very small patient populations complicate the initiation of studies for these rare diseases. The use of basket trials – an approach that has led to the approval of groundbreaking oncology products – could be one solution to faster, more successful development of products for the rarest of rare diseases by targeting shared molecular etiologies instead of specific diseases.
Innovative Clinical Trial Designs for Rare Diseases
After the passage of the 21st Century Cures Act in 2016, innovative regulatory approaches paved the way for master protocol designs for oncology clinical trials. In the FDA’s 2022 Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics: Guidance for Industry, a basket trial is defined as “a master protocol designed to test a single investigational drug or drug combination in different populations defined by different cancers, disease stages for a specific cancer, histologies, number of prior therapies, genetic or other biomarkers, or demographic characteristics.”1 The agency emphasises that because master protocols are so complex, it is important that they are “well designed and well conducted” to ensure patient safety and to collect quality data that could support product approval.2
The rare disease community has had its eye on the basket design for years. Great efforts have been made to mirror this process for rare conditions such as mitochondrial diseases and diseases with mutations in the same genes that can result in different phenotypes, mutations in different genes that can affect the same pathway, and the same mutation types in different genes.3 During a presentation at the Drug Information Association 2024 Global Annual Meeting, Philip Brooks, PhD, National Institutes of Health, stated that despite the presence of thousands of rare diseases throughout the world (>7,000 in the US), far fewer etiologies exist. Given that there are four main gene mutation types – abnormal RNA splicing, dominant mutations, missense mutations, and nonsense mutations – it could be possible to develop clinical trials that target molecular etiologies instead of specific diseases.
Despite the promise that basket trials show, several challenges delay their initiation, such as small patient populations, the clinical heterogeneity of rare diseases, identification of methods to measure response, the complicated process of enabling extrapolation from
one condition to another, and varying safety concerns given the variability among patient populations. One of the major roadblocks is funding, a common difficulty in rare disease drug development given the lack of profit for drug developers of products that treat small disease populations. Enrolling participants with rare diseases from all over the world is a clinical trial barrier that many disease communities face, but rare diseases are often undiagnosed or occur in locations with minimal healthcare access.
However, the FDA has increased efforts to engage patients in the drug development cycle by launching initiatives such as patient-focused drug development and collecting patient preference information. Accumulating patient experience data, which is described in section 569C(c)(2) of the Federal Food, Drug, and Cosmetic Act as data collected by any persons that are “intended to provide information about patients’ experiences with a disease or condition,” is crucial for understanding the lengths to which patients are willing to go to obtain a cure for their rare disease.4 Given the progressive nature of many rare diseases, time is valuable. Randomised placebocontrolled trials can be devastating for a rare disease patient whose disease has progressed while participating in a study and receiving placebo. In this close-knit scientific community, use of a placebo is considered unethical due to the lack of other treatment options for rare diseases. Thus, creative and efficient approaches to clinical trial design are necessary.
The Rare Disease Endpoint Advancement Pilot Program, which fulfills a commitment under the seventh iteration of the Prescription Drug User Fee Act (PDUFA VII), allows the FDA to engage with sponsors frequently to advance novel endpoints and determine efficacy for rare diseases that do not have accepted endpoints. A common recommendation to sponsors from the FDA at public meetings is to communicate with the FDA early and often, even during the pre–investigational new drug application stage, to avoid delays in conducting clinical trials and receiving approval for new drug applications and biologics license applications.
In the FDA’s Office of Orphan Products Development, the Clinical Trials Grants Program funds clinical trials for rare diseases and encourages the use of innovative clinical trial methods, such as basket trials.5 The agency’s Center for Drug Evaluation and Research launched the Center for Clinical Trial Innovation in May 2024, which provides a central hub supporting innovative approaches to clinical trials that are designed to improve the efficiency of drug development. In May 2022, the agency hosted a public meeting to discuss advances in rare diseases and explore “regulatory fitness” in rare disease clinical trials. During the meeting, presenters highlighted rare diseases that could potentially benefit from and be appropriate candidates for
basket trials, including mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) and Leber hereditary optic neuropathy plus (LHON-Plus). The FDA’s accelerated approval pathway has also contributed to significant scientific advancements in rare diseases. Despite these efforts, few products have been approved by the agency based on results from basket trials, but several are under investigation for various rare diseases.
Glycerol Tributyrate
MELAS and LHON-Plus are two rare progressive neurodegenerative diseases with clinical neurological symptoms that are overlapping and divergent. They both have no cure. According to the National Organization for Rare Disorders, MELAS is inherited from the mother, and most cases (approximately 80%) are caused by variants in the mitochondrially encoded tRNA leucine 1 (MT-TL1) gene.6 Individuals with MELAS syndrome can have stroke-like episodes, seizures, memory loss, dementia, muscle weakness, and difficulty with physical activity. They typically have normal development early in childhood but develop learning difficulties, frequent headaches and vomiting, hearing loss, peripheral neuropathy, and short stature between ages 2 and 15 years. Treatment for MELAS consists of managing symptoms.
In the case of LHON-Plus, which is also inherited from the mother, the primary symptom is sudden, painless loss of central vision. The United Mitochondrial Disease Foundation explains that the “plus” is attributed to patients with non-vision symptoms that could be associated with their LHON variant (e.g., muscle weakness, peripheral neuropathy, tremors, migraine, cardiac issues, bladder issues).7 Initial vision loss can appear at any age, and no treatments are available to slow or reverse the vision loss in LHON-Plus patients.
The George Washington University has planned a parallel-arm, non-randomised, dose-escalation, open-label basket exploratory phase 1 clinical study (NCT06792500) to evaluate glycerol tributyrate, a novel small molecule-based therapy in patients with MELAS and LHON-plus. Participants will undergo simultaneous enrollment in 2 disease-based arms to evaluate the safety and potential for efficacy of daily oral doses of glycerol tributyrate. This first basket trial for MELAS and LHON-Plus will provide a “blueprint” for other rare mitochondrial diseases, Anne Chiaramello, PhD, the principal investigator of the study, stated.8
Setmelanotide
In December 2024, Rhythm Pharmaceuticals, Inc, announced that the FDA approved an expanded indication for Imcivree (setmelanotide) to include younger children in the originally approved indication.9 Setmelanotide is indicated to reduce excess body weight and maintain weight reduction long term in patients aged ≥2 years with syndromic or monogenic obesity due to Bardet-Biedl syndrome (BBS) or genetically confirmed deficiency in pro-opiomelanocortin (POMC), including proprotein convertase subtilisin/kexin type 1 (PCSK1), or leptin receptor (LEPR) deficiency. POMC, PCSK1, and LEPR (collectively called PPL) and BBS deficiencies are rare melanocortin-4 receptor (MC4R) pathway diseases with characteristics that include hyperphagia, impaired satiety, persistent and abnormal food-seeking behaviors, and early-onset obesity.
This approval was based on results from an open-label phase 3 basket trial (Venture; NCT04966741) to evaluate the efficacy, safety, and tolerability of setmelanotide via subcutaneous injection over 1 year of treatment in 12 pediatric participants aged 2 to <6 years with obesity due to either biallelic variants of PPL or BBS.9 Participants were assigned to 1 of 2 baskets: the PPL group or BBS group. Of the 12 participants who completed the study, 10 (83%) had a ≥0.2-point
Clinical Management
reduction in body mass index (BMI) Z-score per World Health Organization methodology at week 52 (95% confidence interval [CI]: 58.7, 99.8). The mean percent change in BMI from baseline at week 52 was -18% (standard deviation [SD] = 13) in the overall safety population. The mean percent change in BMI at week 52 was -26% (SD = 11) in participants with POMC or LEPR deficiency and -10% (SD = 9) in those with BBS. All adverse events were mild or moderate; the most common were skin hyperpigmentation, vomiting, nasopharyngitis, upper respiratory tract infection, and injection site reactions.
Hope for Rare Disease Basket Trials
Funding, patient engagement, and scientific breakthroughs are necessary to finally give a voice to patients who have remained in the background of drug development due to the rarity of their diseases and a lack of successful clinical trials and treatments. Regulatory flexibility and frequent engagement between the FDA and drug developers could be the key to finally unlocking treatments for those who currently cannot be cured.
REFERENCES
1. Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry. Food and Drug Administration. https://www.fda.gov/media/120721/download
2. FDA Modernizes Clinical Trials with Master Protocols. Food and Drug Administration. https://www.fda.gov/media/125556/download
3. Zanello G, Garrido-Estepa M, Crespo A, et al. Targeting shared molecular etiologies to accelerate drug development for rare diseases. EMBO Mol Med. 2023;15:e17159. https://www.embopress.org/doi/full/10.15252/ emmm.202217159
4. Federal Food, Drug, and Cosmetic Act. 21 USC § 360bbb–8c (1938). https:// www.govinfo.gov/content/pkg/COMPS-973/pdf/COMPS-973.pdf
5. Clinical Trials Grants Program. Food and Drug Administration. https://www. fda.gov/industry/orphan-products-grants-program/clinical-trials-grantsprogram
6. MELAS Syndrome. National Organization for Rare Disorders. https:// rarediseases.org/rare-diseases/melas-syndrome/
7. LHON. United Mitochondrial Disease Foundation. https://umdf.org/lhon/
8. Rare Disease Clinical Trial Reaches Major Milestone. The George Washington University. https://anatomy.smhs.gwu.edu/news/rare-disease-clinical-trialreaches-major-milestone
9. Rhythm Pharmaceuticals Announces FDA Approval of IMCIVREE® (Setmelanotide) for Patients as Young as 2 Years Old. Rhythm Pharmaceuticals, Inc, Website. https://ir.rhythmtx.com/news-releases/ news-release-details/rhythm-pharmaceuticals-announces-fda-approvalimcivreer-0
10. Argente J, Verge CF, Okorie U, et al. Setmelanotide in patients aged 2–5 years with rare MC4R pathway-associated obesity (VENTURE): A 1 year, open-label, multicenter, phase 3 trial. Lancet. 2025;13(1):29-37. https://www. thelancet.com/journals/landia/article/PIIS2213-8587(24)00273-0/abstract
Jaime Gavazzi
Jaime Gavazzi is a Principal Content Analyst for the Cortellis suite of life science intelligence solutions at Clarivate. Her previous roles include writing and editing for books, online magazines, educational coursework, government proposals, and government regulatory publications. Her primary assignments at Clarivate include reporting on FDA drug/device advisory committee meetings and drug approvals. She is also living with a rare disease and waiting for a cure.
Email: jaime.polychrones@clarivate.com
The Critical Role of Effective Sample Management in Clinical Trials
Mislabelled vials or mishandled specimens in clinical trials can compromise data accuracy, potentially affecting patient safety and outcomes. With increasing study size and complexity across multiple locations and partners, accurate and consistent sample handling is crucial.
Effective sample management necessitates a structured system to track the diverse range of samples collected and analysed at each step in order to maintain data integrity and adhere to stringent regulatory and scientific requirements. Failures in sample management can result in delayed regulatory reviews, incorrect research findings, diminished data quality, and lost opportunities.
Consequently, clinical research teams are increasingly looking for technology that can standardise workflows, minimise errors, and enhance transparency throughout the entire sample lifecycle.
Common Challenges in the Sample Journey
Clinical trial sample management encompasses multiple complex stages from collection to analysis, each with unique operational and technical demands where even minor errors can significantly impact data reliability.
A major recurring problem is the lack of real-time visibility during sample transit between collection sites, testing labs, and long-term storage. Reliance on paper manifests and basic tracking numbers, often disconnected from lab systems, hampers efforts to monitor sample location, condition, and deviations in real time. This lack of timely data often leads to reactive responses to delays or temperature fluctuations, potentially resulting in compromised or lost samples and invalid results.
Sample labelling at the point of collection is particularly vulnerable, especially in global trials where infrastructure and training can vary widely.1 While the use of one-dimensional and two-dimensional barcodes is now common, errors still occur when barcoding procedures are inconsistently applied or not properly integrated with clinical workflows. Clinical staff may still manually label vials and enter metadata, particularly in cases where barcode systems are unavailable, misaligned with protocols, or not correctly linked to a LIMS. Smudged, incomplete, or improperly formatted labels often go unnoticed until samples reach the lab, at which point resolving discrepancies becomes more difficult. These early-stage issues can lead to misidentification, unusable samples, and delays that disrupt trial timelines.
Storage and inventory management present further challenges. Samples are often stored for extended periods under tightly controlled conditions to maintain stability, but inventory errors or environmental control lapses can render samples unusable and jeopardise study integrity.
Accuracy and thorough documentation are paramount during processing and analysis. Every step, including reagent application and instrument usage, must be recorded and traceable. Deviations from standard procedures require logging and justification. However, many labs still rely on inconsistent manual record-keeping. Incomplete records can hinder verification or retesting, potentially compromising data quality, increasing audit risks, and leading to regulatory findings if protocol compliance cannot be demonstrated.
Finally, the lack of system integration exacerbates all these problems. Sample data is often fragmented across various platforms, creating administrative burdens and hindering real-time oversight.2 Sponsors and CROs often lack a unified, reliable source of information regarding sample location, handling, and protocol adherence, directly impacting study outcomes and regulatory confidence.
Harnessing Technology to Improve the Sample Journey
To overcome these difficulties, many clinical trial teams are adopting Laboratory Information Management Systems (LIMS) and integrated digital tools.3 These solutions automate routine tasks, ensure end-toend sample quality, and maintain traceable records.
Such systems offer a central platform for managing and analysing sample data throughout its lifecycle. By automating data entry, labs can decrease human errors, standardise procedures, and maintain consistent documentation. Real-time updates allow all stakeholders to have an accurate, shared view of progress as samples are scanned, logged, and transferred between users or departments.
Lab Informatics Supports Sample Management in Clinical Trials
Modern LIMS are essential for maintaining consistency and control throughout the entire sample lifecycle. Each sample is assigned a unique digital identifier and meticulously tracked from its collection through storage to its final disposal. These systems automatically record critical information such as dates, locations, personnel involved, and actions performed, establishing a clear and auditable chain of custody. This rigorous tracking helps laboratories meet stringent regulatory standards like GCP and FDA 21 CFR Part 11.
A critical feature enabling this traceability is barcoding. Barcodes, either one-dimensional or two-dimensional, are applied at collection and linked to LIMS records, enabling automated data capture at each handoff and streamlining identification. This automated data capture at each transfer point significantly streamlines sample identification, minimises transcription errors, and allows authorised personnel across different departments to easily update sample information via scanning. Barcoding also enhances inventory management by providing alerts for low stock levels and simplifying audit preparations.
The adoption of LIMS can have evident practical benefits in various real-world scenarios. For instance, a laboratory grappling with
issues of misidentified samples could implement a dedicated LIMS to automate labelling and tracking. This would result in a significant reduction in manual handling, improved data reliability, and more efficient overall lab operations. Similarly, in other scenarios, by successfully transitioning from disparate legacy systems to a unified, modern LIMS, this integration would streamline workflows, lower maintenance costs, and provide laboratory managers with real-time visibility into production and performance metrics across all sites and instruments.
Third-party laboratories handling substantial sample volumes often encounter challenges with inconsistent workflows. This issue could be addressed by implementing a LIMS to automate repetitive tasks and ensure adherence to regulatory requirements. This would lead to faster turnaround times and greater consistency across diverse projects.
The Future of Sample Management in Clinical Trials
The integration of artificial intelligence (AI) is emerging as a promising advancement in sample management, especially with the rise of decentralised trials and increasing data volumes. Unlike traditional LIMS systems focused on past events, AI offers capabilities to predict, prevent, and optimise future processes.4
AI's application in predictive analytics is particularly significant. By examining historical LIMS data, AI tools could identify patterns, such as sample types susceptible to degradation under specific conditions. This enables proactive adjustments to storage protocols, enhancing planning and preventing the loss of valuable materials.
Beyond prediction, AI could optimise workflows by detecting inefficiencies like processing bottlenecks or underutilised equipment, allowing for better resource allocation and increased throughput. In quality control, AI can automatically review sample records for anomalies or missing information, alerting users to potential problems before they impact results or compliance.
In reporting, AI can distil complex datasets into concise summaries, enabling faster decision-making across trial phases and study sites.
The future of sample management involves enhanced connectivity through the integration of LIMS with other platforms, including SDMS, EHRs, and cloud-based data lakes. This will provide scientists with richer, real-time context for interpreting results. AI will increasingly leverage this integrated data to support real-time monitoring, risk assessments, and adaptive workflows.
Despite these advancements, challenges such as data standardisation across laboratories, LIMS interoperability, and validation within regulated settings need to be addressed. Nevertheless, with a strong data foundation provided by LIMS, AI holds the potential to transform sample management from a reactive approach to a proactive and dynamic function.
Building a Robust Foundation for Clinical Trials
Effective sample management is critical to maintaining the integrity and reliability of clinical trials. Errors such as mislabelling, undocumented handling steps, or missing data can jeopardise study timelines, compromise data confidence, and pose risks to scientific validity.
A modern LIMS acts as the backbone of a clinical research environment, digitally capturing every step from sample collection to analysis and disposal and assigning each specimen a unique
Logistics & Supply Chain
identifier tied to detailed metadata. This helps ensure that no sample is misplaced, mislabelled, or improperly processed.
As discussed, a core feature is barcoding, which simplifies identification, supports accurate tracking, and reduces transcription errors. Whether using one-dimensional or two-dimensional barcodes, labs can scan samples at each handoff, automate data capture, and enable real-time visibility across departments. This not only improves collaboration but also ensures inventory accuracy and supports compliance with audit requirements.
LIMS platforms also integrate with laboratory instruments and other software and storage solutions to give broader visibility across teams and increase the speed of data analysis and reporting. The ability to monitor storage conditions, track instrument calibration, manage reagent usage, and enforce procedural SOPs from a single system reduces errors and improves regulatory readiness.
While a variety of sample management tools exist, many fall short by operating in isolation, lacking integration with instrument data, workflow tracking, or broader lab systems. These disjointed solutions can limit transparency and increase the risk of errors. What distinguishes a modern, integrated LIMS is its ability to bring together all aspects of lab operations on a unified platform. Sample management is a central component, but its value is amplified when combined with connected data flows, real-time monitoring, and predictive capabilities through AI. This integration is what transforms LIMS from a digital record-keeper into a strategic tool that supports higher quality, efficiency, and reliability in clinical research.
REFERENCES
1. Wang, L., et al. (2024). A-179 Improving specimen labeling errors in the pediatric emergency department at a tertiary care hospital. Clinical Chemistry, Volume 70, Issue Supplement_1.
2. Sampson, R., et al. (2022). An integrated approach to improve clinical trial efficiency: Linking a clinical trial management system into the Research Integrated Network of Systems. Journal of Clinical and Translational Science, 6(1):e63.
3. Alhammad, L.A., et al. (2023). The impact of laboratory automation on efficiency and accuracy in healthcare settings. International Journal Of Community Medicine And Public Health, 11(1), 459–463.
4. Xiaoran, L., et al. (2024). Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review, Journal of the American Medical Informatics Association, 31(11), 2749–2759.
Andrew Wyatt
Andrew Wyatt, Chief Growth Officer, Sapio Sciences has over 30 years of expertise in commercially scaling global software companies, from NASDAQ listed companies to privately held businesses. At Sapio, Andrew is responsible for growing the company internationally, drawing on his deep understanding of the life sciences and technology industries. Prior to Sapio Sciences, Andrew was the COO of Lumeon.
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