Volume 18 Issue 3
PEER REVIEWED
Renal Care in the USA Set for Change in 2026 and Beyond Clinical Supply Distribution Strategy: Planning for Flexibility in a Changing Trial Landscape AI in Clinical Trials: State of the Art and Next Steps Beyond Familiar Sites: Rethinking Site Selection to Bridge Clinical Research and Routine Care
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Contents 4
FOREWORD
MARKET REPORTS 6
MANAGING DIRECTOR Mark A. Barker BUSINESS DEVELOPMENT Anthony Stewart anthony@senglobalcoms.com MANAGING EDITOR Alice Philips alice@senglobalcoms.com EDITORIAL Melissa Cavner melissa@senglobalcoms.com DESIGNER Jana Sukenikova www.fanahshapeless.com RESEARCH & CIRCULATION MANAGER Carla Devine carla@senglobalcoms.com FINANCE DEPARTMENT Akash Sharma accounts@senglobalcoms.com PUBLISHED BY Senglobal Ltd.,20–30 Whitechapel Road, London, E1 1EW, UK Tel: +44 (0) 2045417569 Email: info@senglobalcoms.com www.journalforclinicalstudies.com Journal for Clinical Studies – ISSN 1758-5678 is published quarterly by Senglobal Ltd.
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 18 Issue 3 Autumn 2026, Senglobal Ltd.
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Informed Consent Under the Microscope: Protecting Your Data in Trial
For clinical trials, the matter of informed consent is a foundational prerequisite for handling a participant's personal data. It requires the participants to be fully informed about the trial, including what personal data will be collected, how it will be used, protected and shared and who will have access to it. Siti Nuralis binti Abd Muis, Nurul Farzana binti Mohd Amin and Batrisyia binti Abdul Yazid of Clinical Research Malaysia explore the legal dimensions of consent and data governance in clinical trials. 10 The Five Assumptions That Undermine Global Clinical Trials A global trial is not simply one study happening simultaneously in several places. It is composed of several regional programmes, each running under their own regulatory systems, site relationships and often their own timelines, that a sponsor likes to perceive as a single study. Dan Gallagher of Emerald Clinical Trials discusses how these issues can be addressed by properly matching a study's regional, therapeutic and operational needs with the right CRO partner who genuinely has the ability to delivery them and from being honest about which assumptions are built on current data and which were simply carried over from the last study. REGULATORY AFFAIRS 12 The Balancing Act of a Single Pivotal Trial in Ophthalmology Even though the US FDA has indicated that their new default path is a single pivotal trial, the scientific community is still sceptical and hesitant to embrace this approach. It is likewise unclear if a single pivotal trial will be sufficient in non-US markets. Mary Newman and Andrew Pucker of Mintra Health discuss that time will tell whether the single trial approach to registering a product for marketing will be acceptable. Going further to state that in any case conducting a wellexecuted, well-designed trial that is executed robustly will provide the opportunity for the investigational product to become a successful treatment for patients. 18 Pharmacovigilance Under Pressure: Why the Workflows Surrounding Case Management Can No Longer Be Left to Chance Aggregate report scheduling, pharmacovigilance system master file (PSMF) management, PV agreement maintenance, medical term query management and affiliate oversight are still largely managed through spreadsheets, shared drives and email. Gyöngyi Mezey of Qinecsa Solutions discusses that there will have to be a shift to more formal, GxP-validated systems with audit trails built in by design, that log every action, every approval and every change in a way that can be retrieved on demand. THERAPEUTICS 20 Renal Care in the USA Set for Change in 2026 and Beyond Two significant events in the first half of this year have informed new debate around how the standard of care for hemodialysis patients in North America should develop. Sadahiro Kaname of Nipro Corporation discusses the science behind the two treatment modalities, compare the growing body of clinical evidence for each Journal for Clinical Studies 1
Contents and contrast the practical implications for adoption in the North American healthcare system. 24 Advancing Precision Medicine in Oncology: Companion Diagnostic Devices and IRB Best Practices for Identification and Regulatory Assessment Precision medicine has transformed oncology by shifting treatment decisions toward biomarker-driven approaches that use tumourspecific molecular features such as genomic alterations, protein expression, immune signatures and resistance mechanisms to determine eligibility, guide treatment and monitor response. Sharad Adekar of WCG discusses how the framework will help reviewers identify companion diagnostic use, assess regulatory status, evaluate participant risk, strengthen informed consent and document review determinations. CLINICAL TRIAL MANAGEMENT 28 Beyond Familiar Sites: Rethinking Site Selection to Bridge Clinical Research and Routine Care There is an evident supply/demand imbalance in clinical research: the industry's demand for research capacity continues to grow, while the mechanisms through which new capacity enters the system remain weak. Pete Fronte, Ruma Bhagat, Laveina Dash, Jenny Jackman, Kanecia Zimmerman, Karen Lipworth, Kara Bastarache, Susan P. Landis of ACRP discuss how site selection should no longer be viewed as a routine operational exercise, but as a strategic determinant of whether clinical trials can achieve their scientific, commercial and societal goals. 32 Beyond One-Time RECIST 1.1 Training: Knowledge Acquisition and Retention Consistent use of RECIST 1.1 depends not only on the expertise of investigators and imaging specialists, but also on the knowledge of clinical research professionals who review and manage oncology trial data. Maxim Kosov of PSI discusses how the findings support moving beyond one-time, completion-based training toward a model that measures knowledge acquisition, addresses misunderstandings, evaluates retention and uses evidence to improve the curriculum. Such an approach may strengthen the ability of CRAs to recognise inconsistencies, escalate potential issues and support the quality of oncology clinical trial data. TECHNOLOGY 36 Why Pharmacovigilance Teams Don’t Trust Their Own Data – Even When it’s Telling them the Right Thing AI output being correct doesn’t automatically equate to it being trusted. Some pharma companies that have been drawing on AI-powered
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adverse event processing for a while now are reporting a frustrating disconnect between the demonstrated accuracy scores and the way PV teams are responding to them. Jason Bryant and Samiel Wallis of Aris Global discuss how the outstanding challenge is determining when a validated system can be used with lighter checks. Devising the optimal method for calculating a trust coefficient has yet to happen, but the justification for moving towards such a measure is strong, certainly for companies with substantial portfolios and big ambitions, which may be handling several hundred thousand safety reports a year. 38 AI in Clinical Trials: State of the Art and Next Steps Clinical trials have never been more important or expensive. A single new drug now takes over a decade and billions of dollars to bring to market and trials account for much of that cost and time. They are also growing more complex: more data, more sites, more decentralised elements and rising regulatory expectations. This is precisely why AI matters now. Stefan Åström of Astrom Research discusses how the organisations that take on trust, data and workflow together, rather than waiting for one to be perfect before beginning the next, will move AI from a conference demonstration to real, inspectionready use and, for the patients these trials exist to serve, close the gap between what is possible and what reaches the clinic. LOGISTICS AND SUPPLY CHAIN 42 Net Treatment Benefit as a Practical Approach to Dose Optimisation Since the FDA released their Project Optimus guidance for dose optimisation, with the idea to move away from Maximum Tolerated Dose (MTD) designs, many sponsors have very practical questions about how to demonstrate that one dose provides a better overall balance of efficacy, safety and tolerability for patients. Pascal Piedbois of One2Treat discusses how Net Treatment Benefit provides one practical framework for bringing efficacy, safety and tolerability together into a single analysis, helping sponsors select the right dose with confidence and supporting that decision from Phase II through regulatory review and into clinical practice. 44 Clinical Supply Distribution Strategy: Planning for Flexibility in a Changing Trial Landscape The clinical trial ecosystem is undergoing a structural shift. What was once a relatively linear, site-centric model has evolved into a complex, globally distributed and increasingly patient-driven environment. In this new reality, clinical supply distribution is no longer a downstream operational consideration, it is a strategic enabler of trial success. Edward Groleau of PCI discusses how the clinical supply distribution is about more than moving product. It is about ensuring the right investigational therapy reaches the right patient, at the right time, wherever they are.
Volume 18 Issue 2
Corporate Profile
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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:
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.
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.
Ramus Medical EOOD is a full-service contract research organisation (CRO) in Sofia, Bulgaria.
The Medical Centre Ramus allows the conduct of clinical trials in all phases in many therapeutic areas.
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 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.
The company independently manages clinical research projects in Bulgaria and provides partnerships in multinational clinical projects providing a comprehensive range of clinical research services:
Ramus Medical retains an extensive database of investigators and sites compiled through years of mutually beneficial collaboration.
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
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 Integrated QMS
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 www.journalforclinicalstudies.com
Ramus Medical Ltd
26 Kapitan Dimitar Spisarevski Street, 1592 Sofia, Bulgaria Tel./Fax: +359 2 841 23 69 www.ramusmedical.com, www.ramuslab.com email: office@ramusmedical.com
Dimitar Mihaylov Marketing Director
Journal for Clinical Studies 3
Foreword As the leaves begin to change, we welcome you to our third edition of IAHJ in 2026. We hope you enjoy reading all the hard work and research conducted by the great minds of the animal health industry. In this Autum issue it tackles the growing challenges the veterinary world is facing and how this sector of health generates greater visibility. Vicky Strong of Synapse Animal Health Consultancy explores how miRNAs participate directly in the regulatory networks that drive disease processes. As such, changes in their expression can precede clinical signs, offering the possibility of earlier disease detection. Moreover, because many miRNAs are tissue-specific and exhibit distinct expression patterns in different diseases, they can help pinpoint both the organ affected and the underlying pathological process. Vicky goes further to assert that the approaches outlined in the article could ultimately allow testing to move beyond centralised laboratories and into veterinary practice, contributing to more accessible, earlier disease detection and monitoring and, ultimately, supporting more informed clinical decision-making across veterinary medicine.
inconclusive. However, as veterinary practitioners reported similar observations during the spring of 2025, attention increasingly shifted toward an infectious causative pathogen. Tijs Tobias of Royal GD asserts that increasing evidence points towards a causal relationship between presence of fox parvovirus in pigs and the observed clinical signs, despite the fact that not all postulates of Koch have yet been studied as well as many aspects of virus pathogeneses and routes of transmission. Tijs states that Given the extensive spread in the Netherlands and into neighbouring countries, it can be expected that the outbreak will continue and spread further into other European countries. Laura Payo of Argenta examines how the FDA is not proposing to change what it takes to establish that a substance is Generally Recognised as Safe. The science remains the same. What would change is who needs to see that information and when. The proposal includes several exemptions that could limit its impact on ingredients already recognised through existing regulatory pathways. Even so, companies with self-affirmed GRAS ingredients will benefit from reviewing their portfolios and preparing supporting documentation well before a final rule is issued.
In late 2024, Royal GD was notified of an outbreak of ocular disease in weaned piglets on a farm. Initial diagnostic investigations were
Melissa Cavner, Editor
JCS – Editorial Advisory Board
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Hermann Schulz, MD, Founder, PresseKontext
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Ashok K. Ghone, PhD, VP, Global Services MakroCare, USA
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Jeffrey W. Sherman, Chief Medical Officer and Senior Vice President, IDM Pharma.
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Bakhyt Sarymsakova – Head of Department of International Cooperation, National Research Center of MCH, Astana, Kazakhstan
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Jim James DeSantihas, Chief Executive Officer, PharmaVigilant
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Catherine Lund, Vice Chairman, OnQ Consulting
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Mark Goldberg, Chief Operating Officer, PAREXEL International Corporation
Cellia K. Habita, President & CEO, Arianne Corporation
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Maha Al-Farhan, Chair of the GCC Chapter of the ACRP
Chris Tait, Life Science Account Manager, CHUBB Insurance Company of Europe
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Deborah A. Komlos, Principal STEM Content Analyst, Clarivate
Rick Turner, Senior Scientific Director, Quintiles Cardiac Safety Services & Affiliate Clinical Associate Professor, University of Florida College of Pharmacy
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Elizabeth Moench, President and CEO of Bioclinica – Patient Recruitment & Retention
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Robert Reekie, Snr. Executive Vice President Operations, Europe, AsiaPacific at PharmaNet Development Group
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Francis Crawley, Executive Director of the Good Clinical Practice Alliance – Europe (GCPA) and a World Health Organisation (WHO) Expert in ethics
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Stanley Tam, General Manager, Eurofins MEDINET (Singapore, Shanghai)
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Stefan Astrom, Founder and CEO of Astrom Research International HB
Georg Mathis, Founder and Managing Director, Appletree AG
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Steve Heath, Head of EMEA – Medidata Solutions, Inc
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Volume 18 Issue 2
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Journal for Clinical Studies 5
Market Report
Informed Consent Under the Microscope: Protecting Your Data in Trial A clinical trial is a structured research study involving human participants, designed to evaluate new tests, treatments, or interventions. Its purpose is to observe and assess health outcomes, providing critical evidence on safety and effectiveness.1 Clinical trials are of paramount importance, serving as a crucial stage in translating laboratory findings into practical treatments that can improve patient care. For clinical trials, the matter of informed consent is a foundational prerequisite for handling a participant's personal data. It requires the participants to be fully informed about the trial, including what personal data will be collected, how it will be used, protected and shared and who will have access to it.2 Definition and Core Principle Informed consent is a process which trial participants voluntarily confirm their willingness to enroll in a specific trial, after receiving clear information about all relevant aspects of the trial that may influence their decision to participate. The duty to obtain informed consent lies with the Investigator as laid out by the Malaysian Medical Council (MMC) Guideline on Clinical Trials and Biomedical Research. The guideline is grounded with three ethical principles: respect for persons, beneficence and justice. The principle of respect for persons necessitates the ethical obligation to secure informed consent from trial participants and to uphold the confidentiality of their personal information. The Investigator is ethically and professionally obligated to provide potential trial participants information about the trial’s objectives, procedures, risks, benefits and their rights to refuse or withdraw at any time without a penalty. Informed consent must be voluntary and appropriately documented through a signed, dated and witnessed Informed Consent Form.5,6 In Malaysia, the Informed Consent Form must be approved by Independent Ethics Committee (IEC) of the research facility prior to obtaining consent from the trial participant.7 A good Informed Consent Form should be written in a simple practical language and should be understandable to the trial participant. The IEC is also responsible for ensuring that the Informed Consent Form includes all pertinent information related to the clinical trial, including compensation for participant.8 This article explores the legal dimensions of consent and data governance in clinical trials. Legal Frameworks Governing Personal Data in Clinical Trials As Malaysia's role in global clinical trials grows, aligning domestic frameworks with international standards for informed consent and data protection is essential. This section examines Malaysia's legal ethical framework and its alignment to International Standards: 6 Journal for Clinical Studies
Malaysian Context: Personal Data Protection Act 2010 and the 2024 Amendments Personal Data Protection Act (PDPA) 2010 is the main legislation that governs how ‘data users,’ or ‘data controllers,’ (according to the 2024 amendments) are to handle personal data, including but not limited to information gathered in clinical trials. PDPA’s relevancy for clinical research is growing since clinical trials increasingly rely on digital data systems, cross-border data transfers and developing technologies. In the context of a clinical trial, the PDPA 2010 ensures that data is protected by requiring that: •
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Data is collected only for a lawful purpose, paired with adequate notice given to participants about what data will be collected and how it will be used, similar to European Union’s (EU) General Data Protection Regulation (GDPR). Sensitive personal data (such as health information) be handled delicately, ensuring participants understand what is being collected and how it is processed and disclosed. The data user operates with accountability: maintaining security safeguards, limiting retention of data and ensuring accuracy of the data.9
INTERNATIONAL CONTEXT EU GDPR The GDPR is a comprehensive legislative framework for the protection of personal data in Europe, with its provisions outlining rights of individuals and duties for organisation.10 In the context of clinical trials, health data is classified as ‘special categories,’ of personal data under the GDPR (Article 9). The processing of sensitive health data in clinical research is subject to stricter legal requirements under the GDPR. One of the most notable requirements is the need for explicit consent from participants as per Article 9. Article 4 (11) defined consent as ‘any freely given, specific, informed and unambiguous indication of the data subject’s wishes by a statement or by a clear affirmative action.’11 This implies that participant information and permission forms must explicitly state how their health data will be used for the particular trial and the data controller must be able to prove that the consent was freely provided, explicit, informed and unambiguous. Under the GDPR, clinical trials must follow key data protection principles, including data minimisation and purpose limitation. Data minimisation (Article 5(1)(c)) requires collecting only personal data that is adequate, relevant and necessary for conducting the trial, whereas purpose limitation (Article 5(1)(b)) requires that personal data be collected for specific, explicit and legitimate purposes relevant to the trial. The entity controlling the data (e.g. research sponsor) must be able to demonstrate compliance with all these requirements. Volume 18 Issue 2
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ICH-GCP E6(R3) (2025): ICH-GCP E6(R3) is the global standard for clinical trial conduct and monitoring. It revolutionises clinical research through riskbased methods that enhance efficiency and data integrity. The importance of informed consent and protection of participants’ rights remains a cemented core in this guideline, with Section 2, Principle 2 stating that:12 ‘Informed consent is an integral feature of the ethical conduct of a trial. Clinical trial participation should be voluntary and based on a consent process that ensures participants (or their legally acceptable representatives, where applicable) are well-informed.’ Declaration of Helsinki (2024 Revision): The Declaration of Helsinki provides globally referenced ethical guidelines for human research, mandating voluntary informed consent with clear information and no coercion. The significance of informed consent in clinical research is paramount as it aligns research practices with both ethical standards and legal protections.
SECTION 3. Recent Legal Updates on Personal Data and Informed Consent In 2025, the ICH-GCP E6 (R3) has acknowledged significant advancements in clinical trial methodologies and technologies since the previous E6 (R2) iteration. ICH-GCP E6 (R3) provides guidance on the integration of electronic records, remote monitoring and digital health technologies including digital consenting to adapt and streamlined consent process. Additionally, the revision introduces more stringent requirements for data security, audit trails and traceability, hereby ensuring that electronic and remotely collected trial data adhere to high-quality standards.13,14 In 2024, the Declaration of Helsinki introduces significant updates on broad consent. Broad consent allows participants to give a single informed consent for the future bona fide research.17 Paragraph 32 of the Declaration has been rewritten to address informed consent requirements for collecting, storing and secondary use of data and biological materials. Moreover, the Declaration recognised the integration of digital technologies in clinical trials including electronic informed consent (e-Consent). The validation of e-Consent reflects its evolution from a mere alternative to an increasingly standard practice which support to enhance participant comprehension, engagement and accessibility within the clinical trial process. The National Committee for Clinical Research endorsed the Malaysia Decentralised Clinical Trial Guidance Document in 2023, which outlines the implementation of e-Consent. The e-Consent process should follow the PDPA and relevant regulation. It must convey clear and concise information regarding the trial, including its objectives, potential risks and benefits and measures to ensure participant confidentiality. From a technological standpoint, the e-Consent interface should be designed to be user-friendly, intuitive and easily navigable which allow participants to pose questions and seek clarification prior to providing consent. They also should be given the option to withdraw their e-Consent easily. The entire process must be thoroughly documented and securely stored for the duration of the trial, ensuring that it remains accessible and auditable for regulatory inspections or reviews.18 Section 4. Implementation Insights The PDPA serves as the principal legislative framework governing the protection of personal data within the Malaysian healthcare sector. The PDPA establishes consent as a legally recognised basis for the lawful processing of health-related information. Pursuant to Section 6 of the PDPA, data processing is permitted solely for legitimate www.journalforclinicalstudies.com
purposes that are directly connected to the activities of the data user while Section 40 sets out the conditions under which sensitive personal data, including health information, may be processed. The PDPA takes a flexible approach to consent, allowing its application to be adapted to different contexts. Consequently, institutions may adopt varying methods of obtaining patient consent, with many favouring written consent to support clarity and accountability. In the context of big data, rapid advancements in technologies such as data mining and profiling make it difficult to predict future uses of personal data. For instance, patients may initially consent to their data being used for treatment and research within the company. However, as technology evolves the company may begin to apply advanced profiling techniques to predict health risks and share insights with third party insurers. Sections 30 to 44 of the PDPA require company to safeguard personal data and obtain valid consent for its processing. Such scenarios illustrate the practical challenges of applying consent based protections in a rapidly evolving technological environment, particularly where data reuse and secondary analysis are involved.19 Globally, the EU has implemented the GDPR to enhance data privacy protections. In 2020, the EU issued a policy document entitled Big Data and Artificial Intelligence, which highlights both the significant opportunities and the complex challenges associated with the utilisation of large-scale data in healthcare. However, the practical of GDPR implementation remains a subject of discussion. Lalova-Spinks et al. (2022) reports that ethics committees (ECs) often advise on the legal basis for data processing. While guidance may assist compliance, enforcing such opinions would contradict GDPR’s accountability principle, which assigns full responsibility to the data controller as data controllers are required to ensure and demonstrate compliance with data protection laws, the determination of the appropriate legal basis for data processing should therefore remain exclusively with the data controller.20 In the context of cross-border clinical trials, determining a uniform legal basis for data processing under the GDPR remains a mission. The use of disparate legal bases within cross-border study risks creating inconsistencies in the protection afforded to data subjects, thereby leading to unequal treatment of participants.21 Notably, European Commission’s assessment study reveals that ‘the understanding and application of consent can vary significantly across Member States,’22 highlighting the need for further legal clarification and interpretative consistency regarding the role of consent within the GDPR framework.23 Analysis and Recommendations Informed consent has evolved into a legal and ethical requirement protecting individual control over personal data and participant autonomy in research. Malaysia's PDPA balances commercial interests with privacy, GDPR sets the global data protection benchmark and ICH GCP establishes international ethical standards for clinical research. The PDPA, the EU GDPR, and the ICH GCP guidelines, each address informed consent within the context of their respective objectives, while sharing a common emphasis on individual autonomy. The PDPA focuses on the use of personal data in commercial and organisational settings, allowing consent to be given either explicitly or impliedly through reasonable actions and providing individuals with right to access and correct their data...24 The GDPR reflects a detailed and structured approach to informed consent, setting clear requirements for how consent is obtained and managed, including that it must be Journal for Clinical Studies 7
Market Report specific, informed and unambiguous, with particular attention to sensitive data and straightforward mechanisms for withdrawal.25 The ICH GCP guidelines, though not a legislative instrument, extend the principle of informed consent beyond data protection into the ethical domain of clinical research, mandating written, voluntary and fully informed agreements that safeguard participants’ welfare and allows withdrawal at any stage without penalty.26 The Malaysian judiciary has played a formative role in shaping the doctrine of informed consent. In Foo Fio Na v Dr Soo Fook Mun & Anor [2007] 1 MLJ 593, the Federal Court adopted the ‘reasonable patient,’ standard, holding that a physician’s failure to disclose material risks constituted a breach of duty.27 This landmark case underscores the legal importance of transparency and disclosure in medical decisionmaking, aligning Malaysian jurisprudence with international norms of patient autonomy. Within the European Union, the GDPR has been interpreted and enforced with rigor in the health research domain. The European Data Protection Board (EDPB) clarified in its Guidelines 03/2019 that processing health data for scientific research may be justified under the lawful basis of public interest, provided that appropriate safeguards are implemented.28 Enforcement practice further illustrates the GDPR’s strength in 2021, the Hellenic Data Protection Authority fined a hospital for relying on invalid consent, demonstrating the regulation’s capacity to impose significant penalties for non-compliance.29 Globally, informed consent obligations are reinforced through both ethical and legal mechanisms. In Singapore, the Health Sciences Authority suspended a clinical trial due to non-compliance with GCP documentation standards, highlighting the operational importance of proper consent procedures.30 Therefore, several forward-looking reforms are recommended to further strengthen the coherence and enforceability of informed consent regimes. The Malaysian PDPA can be enhanced with healthspecific provisions, dynamic consent mechanisms and expand data subject rights, thereby advancing protection in research context.31 Embedding ICH GCP ethical standards into statutory law would further enhance enforceability and promote consistent clinical research practice. For example, under ICH GCP E6(R2), and reaffirmed in E6(R3), informed consent must be obtained prospectively and re obtained when new safety information becomes available. Statutory codification of these requirements would transform such obligations from guidance based expectations into legally binding duties, thereby strengthening accountability and aligning Malaysia’s regulatory framework with internationally harmonised standards (ICH E6(R2), 2016; ICH E6(R3), 2025).32 Greater legal oversight, targeted training for Ethics Committees and Investigators on informed consent would help to ensure that informed consent processes meets legal, ethical and data security standards. On a cross-border level, adequacy frameworks and bilateral agreements would serve as practical mechanism to facilitate lawful international data transfers while safeguarding participant rights.33 Finally, investment in secure digital infrastructures with audit trails would provide a transparent and accountable system for documenting consent and managing sensitive health data.34 This includes implementing end-to-end encryption, secure data transmission and storage systems that protect unauthorised access or alteration of consent records. Collectively, these measures would further strengthen the alignment of Malaysia’s data protection and research ethics frameworks together with global best practices, ensuring that informed consent remains both a legal safeguard and an ethical cornerstone in clinical trial settings. 8 Journal for Clinical Studies
REFERENCES 1. 2.
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17. 18. 19.
20.
World Health Organization: WHO. (2020, January 17). Clinical trials. https:// www.who.int/health-topics/clinical-trials/ Spiegel, M. (1979). Lawyering and Client Decisionmaking: Informed Consent and the Legal Profession. University of Pennsylvania Law Review, 128(1), 41–140. https://doi.org/10.2307/3311735 Society of Clinical Research Professionals in Malaysia (2024). A Guide to conducting Clinical Trial in Malaysia. https://drive.google.com/file/d/1sAv V2bd7URUPMaAzZHsoOtbghJJzd4ud/view Malaysian Medical Council (2026). Clinical Trials and Biomedical Research. https://mmc.gov.my/wp-content/uploads/2025/09/Clinical-TrialsBiomedical-Research.pdf International Council for Harmonisation. ICH E6(R3): Guideline for Good Clinical Practice. 2025. https://database.ich.org/sites/default/files/ICH_ E6%28R3%29_Step4_FinalGuideline_2025_0106.pdf Sasongko, Teguh Haryo & Hussin, Zabidi & Othman, Nor & Rostenberghe, Hans. (2015). Informed Consent Template and Guidelines on the Ethical Practice in Human Genetics and Human Genomic Research; Initiatives of the Universiti Sains Malaysia. The Malaysian journal of medical sciences : MJMS. 22. 47-53. https://pmc.ncbi.nlm.nih.gov/articles/PMC5295757/pdf/ mjms-22-6-047.pdf World Health Organization, & World Health Organization. (2013). Information for researchers concerning informed decision making: what is an informed consent form?. Retrieved August, 8, 1013. https://www.who.int/docs/defaultsource/documents/ethics/informed-decision-making.pdf National Pharmaceuticals Regulatory Agency (2018). Malaysian Guideline for Good Clinical Practice, 4th Ed https://www.npra.gov. my/images/Guidelines_Central/Guidelines_on_Clinical_Trial/ MalaysianGuidelineforGoodClinicalPractice.pdf Rahim, S. F., Abd Rahman, M. F., & Arif, M. I. A. (2025). To Share or Not to Share Patient Health Data Without Consent for Public Interest Purposes: A Critical Comparative Analysis of EU GDPR 2018 and Malaysia’s PDPA 2010. Akademika, 95(1), 391-408. https://doi.org/10.17576/akad-2025-9501-22 EUROPEAN COMMISSION DIRECTORATE-GENERAL FOR HEALTH AND FOOD SAFETY Health systems and products Medical products -quality, safety and innovation Question and Answers on the interplay between the Clinical Trials Regulation and the General Data Protection Regulation. (n.d.). Retrieved November 2, 2025, from health.ec.europa.eu/ system/files/2019-04/qa_clinicaltrials_gdpr_en_0.pdf? Kirwan, M., Mee, B., Clarke, N. et al. What GDPR and the Health Research Regulations (HRRs) mean for Ireland: “explicit consent”—a legal analysis. Ir J Med Sci 190, 515–521 (2021). https://doi.org/10.1007/s11845-020-02331-2 Mondal, H., & Mondal, S. (2024). A brief review on good clinical practice and its training methods. Indian Dermatology Online Journal, 15(3), 377–382. https://doi.org/10.4103/idoj.idoj_603_23 (pubmed.ncbi.nlm.nih.gov) Bharati, V., Rajaram, S., Prakash, A., & Medhi, B. (2025). International Council for Harmonisation E6 (R3): The Good Clinical Practice, recent developments, and global perspective. Indian journal of pharmacology, 57(1), 1–3. https://doi. org/10.4103/ijp.ijp_152_25 International Council for Harmonisation. ICH E6(R3): Guideline for Good Clinical Practice. 2025. https://database.ich.org/sites/default/files/ICH_ E6%28R3%29_Step4_FinalGuideline_2025_0106.pdf Parums D. V. (2024). Editorial: The 2024 Revision of the Declaration of Helsinki and its Continued Role as a Code of Ethics to Guide Medical Research. Medical science monitor : international medical journal of experimental and clinical research, 30, e947428. https://doi.org/10.12659/ MSM.947428 Jennifer Lubell (2024). Declaration of Helsinki revised to modernize research ethics standards https://www.ama-assn.org/about/office-internationalrelations/declaration-helsinki-revised-modernize-research-ethics World Medical Association, Declaration of Helsinki, paragraph 32, 2024, https://www.wma.net/policies-post/wma-declaration-of-helsinki/ Clinical Research Malaysia (2023). Malaysia Decentralised Clinical Trial (DCT) Guidance Document https://clinicalresearch.my/wp-content/ uploads/2023/07/REVISED-DCT-Guidance-Document-Cover-Backpage-2.pdf Abdul Manap, N., Ab Rahman, M. R., & Salleh, S. N. F. A. (2024). big data and the deterioration of consent principle to protect health data privacy in Malaysia. Malaysian Journal of Syariah and Law, 12(3), 550-561. https://doi. org/10.33102/mjsl.vol12no3.572 Volume 18 Issue 2
Market Report
21.
22.
23.
24.
25.
26.
27.
28. 29. 30. 31. 32.
Lalova-Spinks, T., Sutter, E. D., Valcke, P., Kindt, E., Lejeune, S., Negrouk, A., Verhenneman, G., Derèze, J., Storme, R., Borry, P., Meszaros, J., & Huys, I. (2022). Challenges related to data protection in clinical research before and during the COVID-19 pandemic: An exploratory study. Frontiers in Medicine, 9, 995689. https://doi.org/10.3389/fmed.2022.995689 Negrouk, A., Albu, R., Shakhnenko, I., Lalova, T., & Jagiello, I. (2020) EORTC contribution to the EMA Discussion Paper for Medicines Developers, Data Providers, Research-Performing and Research-Supporting Infrastructures entitled “The General Data Protection Regulation: Secondary Use of Data for Medicines and Public Health Purposes Discussion Paper for Medicines Developers, Data Providers, Research-Performing and Research-Supporting Infrastructures”. EMA-_Secondary-use-of-health-data_Discussion-Paper_ Stakeholders-consultation.pdf Hansen, J., Wilson, P., Verhoeven, E., Kroneman, M., Kirwan, M., Verheij, R., & van Veen, E. B. (2021). Assessment of the EU Member States' rules on health data in the light of GDPR. https://ec.europa.eu/health/system/ files/2021-02/ms_rules_health-data_en_0.pdf Lalova-Spinks, T., Sutter, E. D., Valcke, P., Kindt, E., Lejeune, S., Negrouk, A., Verhenneman, G., Derèze, J., Storme, R., Borry, P., Meszaros, J., & Huys, I. (2022). Challenges related to data protection in clinical research before and during the COVID-19 pandemic: An exploratory study. Frontiers in Medicine, 9, 995689. https://doi.org/10.3389/fmed.2022.995689 Malaysian Personal Data Protection Act 2010 (Act 709), Laws of Malaysia. See also: Noraziah Mohd Awang et al., “To Share or Not to Share Patient Health Data Without Consent for Public Interest: A Comparative Analysis of PDPA 2010 and GDPR 2018,” Akademika, Vol. 90, No. 3 (2020). Regulation (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation), OJ L 119, 4.5.2016. See also: E. Kosta, Consent in European Data Protection Law (Martinus Nijhoff, 2020) International Council for Harmonisation (ICH), E6(R2) Good Clinical Practice: Consolidated Guideline (2016). See also: “Informed Consent of Trial Subjects,” ICH GCP Publications, available online. Foo Fio Na v Dr Soo Fook Mun & Anor [2007] 1 MLJ 593 (Federal Court, Malaysia). European Data Protection Board, Guidelines 03/2019 on processing of personal data for scientific research (2019). Hellenic Data Protection Authority, Decision No. 4/2021. Health Sciences Authority (Singapore), Case Studies from GCP Inspections (2020). Noraziah Mohd Awang et al., “To Share or Not to Share Patient Health Data Without Consent for Public Interest: A Comparative Analysis of PDPA 2010 and GDPR 2018,” Akademika, Vol. 90, No. 3 (2020).
www.journalforclinicalstudies.com
33.
34.
International Council for Harmonisation (ICH), E6(R2) Good Clinical Practice: Consolidated Guideline (2016); see also ICH, E6(R3) Good Clinical Practice Guideline (2025). Beauchamp, T. L., & Childress, J. F. (2019). Principles of Biomedical Ethics. Oxford University Press.
Siti Nuralis binti Abd Muis Siti Nuralis binti Abd Muis is current working as a Senior Legal Executive at Clinical Research Malaysia. She was admitted as an Advocate and Solicitor of High Court of Malaysia in 2017. She obtained a Bachelor of Law (Hons) from Universiti Teknologi Mara. In her current position, she provides legal advice and manages the review of the clinical trial related agreements for the Malaysian Ministry of Health Institutions.
Nurul Farzana Mohd Amin Nurul Farzana Mohd Amin is currently working as a Legal Executive at Clinical Research Malaysia. She obtained a Bachelor of Laws (Hons) from Universiti Kebangsaan Malaysia. In her current position, she provides legal advice and manages the review of the clinical trial related agreements for the Malaysian Ministry of Health Institutions.
Batrisyia binti Abdul Yazid Batrisyia binti Abdul Yazid is currently working as a Legal Assistant at Clinical Research Malaysia. She obtained a Bachelor of Laws (Hons) from University of London. In her current position, she provides legal advice and manages the review of the clinical trial related agreements for the Malaysian Ministry of Health Institutions.
Journal for Clinical Studies 9
Market Report
The Five Assumptions That Undermine Global Clinical Trials The biggest risks to a global study occur long before the first patient is enrolled. The illusory ideal for every sponsor running a global study is to manage one programme built around a single protocol, data package and regulatory strategy that runs universally across every market and region. The reality is far more complex because a global trial is not simply one study happening simultaneously in several places. It is composed of several regional programmes, each running under their own regulatory systems, site relationships and often their own timelines, that a sponsor likes to perceive as a single study. While a protocol summary may be framed as a single study, the operational execution is quite another matter. For instance, a protocol amendment in one region delays site activation in another. Or a sponsor discovers that a regulatory pathway they assumed was standard has changed since the last time anyone on the team actually ran a study in the region. This is how the strategy of a global trial collides with the reality of local execution and it can easily trip up studies. None of this means that the global strategy itself is wrong. Sponsors pursue multi-regional studies for good reasons. It can give broader patient and market access, lead to much faster enrolment timelines (particularly for rare diseases) and provide the diverse patient populations that regulators increasingly expect in the pivotal data. A global strategy works best when the execution model is designed to recognise that local operational realities will inevitably change as the study progresses. False Assumptions About China Timelines China is the perfect example of where sponsors have misconceptions that can significantly impact the success of a study. The country has recently surfaced as a popular region for conducting studies. But many sponsors still make contradictory assumptions about starting a trial there. Ask how long it takes to start a trial in China and all too often, you will still hear a number that reflects the industry as it existed five or ten years ago rather than as it exists now. A few years back, sponsors reasonably budgeted somewhere between 11 and 14 months of startup time before a China site could begin enrolling, a figure driven largely by the pace of regulatory review under a much less modernised approval process. That number has changed substantially in the last 12 months. We’re now routinely seeing startup timelines that average closer to 6 to 7 months and sometimes as fast as 5. Sponsors still planning around the old figure tend to make one of two mistakes: 1) writing China out of the strategy too early, assuming it will slow the program down, or 2) building in so much schedule padding that they lose the competitive advantage speed was supposed to give them in the first place. Multiple factors account for this new reality. Regulatory reviews in China genuinely got faster, but so did quality at the site and investigator level, along with the basic mechanics of running 10 Journal for Clinical Studies
a trial at the site level, communication with sponsors and the data exchange systems that make a study auditable. Each clinical site in China is government owned rather than the academic and private mix common in the US and EU, and studies are typically supported by commercially motivated site management organisations operating inside those hospitals, handling patient recruitment and day to day site setup as a dedicated business function rather than an afterthought. Add a population large enough that even a comparatively rare disease indication has a substantive pool of eligible patients and it becomes easier to understand why enrolment in China has become fast. The opposite mistake is just as common. Sponsors who have heard that China moves quickly overlook the fact that speed still depends on site selection, investigator relationships and regulatory submissions prepared correctly the first time. In fact, it depends on how well the team running the study understands the system as it exists today, not as it existed five years ago. Thus, a sponsor's assumption about timelines is usually the first place a global study starts to drift from its own plan. Enrolment Does Not Behave the Way Sponsors Expect The second assumption sits inside the enrolment plan. Sponsors frequently model a global study as though every region will contribute patients on a roughly parallel schedule. In practice, regions come online at different points, often for reasons that have nothing to do with how well any individual site is performing. A region added later in a programme is dependent on decisions made earlier in other regions. This includes protocol amendments, endpoint definitions and regulatory submissions finalised before that region's sites were ever activated. This matters because a sponsor's confidence in an enrolment forecast is often based on treating each region as an independent input when the regions are sequential and interdependent. A study designed with every region's regulatory and operational reality considered from the earliest stage of protocol development behaves very differently than one where a European based protocol is finalised first and exported outward, with each new region expected to fit itself around decisions it had no part in shaping. The sponsors who get enrolment forecasts closest to reality tend to be the ones who brought regional expertise into study design at the beginning rather than adding it after the protocol was locked. Sponsors Misunderstand the Risk They Are Managing There is yet another kind of false assumption sitting underneath the operational ones and it concerns what sponsors believe they are risking by working in China at all. For years, hesitation about the region was usually framed as a concern about GCP compliance or data quality. In reality, the real concern was just as frequently about intellectual property. Sponsors wondered whether a molecule's underlying science could be reverse engineered once it left its own regulatory environment and whether patent protection would hold up. That concern is easing, for reasons that have little to do with clinical operations. Regulatory alignment with ICH guidelines Volume 18 Issue 2
Market Report and FDA standards has closed much of the compliance gap that once separated China from Western expectations. At the same time, a growing share of the US biotechs running these studies are founded or led by individuals with direct professional ties to China, who carry a working familiarity that an outside sponsor evaluating the region for the first time has not had time to build. The Assumption that a Bigger Name Means Less Risk The fourth assumption shapes the most consequential decision in the entire process. The choice of which partner actually runs the study has a significant impact on the likelihood of success of the study. Sponsors reasonably want to reduce risk, and a common shortcut is choosing the CRO with the most recognisable brand name and the broadest global footprint. Certainly, the logic feels sound. A larger organisation comes with greater infrastructure and people experienced in managing complexity. However, what that logic misses is how a large, generalist CRO actually allocates its focus in. A biotech sponsor running a midsized study is one account among many, competing internally for time and resources against much larger programmes from much larger clients with larger budgets. Systems built to manage dozens of therapeutic areas are often not ideal for the regulatory or scientific nuance of any single one. And that is the problem with brand recognition. It does not measure an ideal fit. It tells a sponsor how well known an organisation is, but not whether it has the regional relationships, therapeutic depth, or the operating model a particular study needs. Fortunately, there are specific CRO capabilities that actually help sponsors reduce execution risk and it has nothing to do with their size or brand recognition. Instead, it includes having regional operational expertise built over an extended period of time, scientific and therapeutic expertise that is embedded in protocol design and an operating structure where regional and global teams function as one group instead of separate vendors potentially fumbling a study handoff between each other. The reality is that therapeutic-area investigator and regulatory relationships built over a decade or more by the CRO tend to produce faster, more reliable site selection. Data Quality Questions Are Not Considered Early Enough The fifth false assumption tends to surface last, usually during a regulatory submission rather than during the trial itself. Sponsors with limited global experience often assume that if a region's portion of a study meets that region's own regulatory standards, the data will simply slot into a global submission without further scrutiny. But locally compliant and globally contributory are not the same standard and treating them as interchangeable is how sponsors end up with data that satisfies one regulator while unfortunately raising questions with another. Data becomes globally contributory only when endpoints, patient population definitions and data standards are aligned across every region from the point the protocol is written, rather than being reconciled after each region has already collected slightly different datasets. This happens by deliberate design and it depends on the same elements the earlier assumptions depend on, including whether the teams running each region were part of the study's design from the start or are they handed a finished protocol and asked to make it work locally. A sponsor usually discovers which of those two things happened only when a regulator asks a question the data was not built to answer. What is really at stake: getting new cures to patients faster. None of these illusions are abstract, and the cost of getting them wrong is not small. Estimates www.journalforclinicalstudies.com
vary, but a single month gained or lost on the way to market can plausibly represent tens of millions of dollars once commercial timing and competitive positioning are factored in. A sponsor who closes a three-to-six-month enrolment gap in a late phase study is tangibly changing where and when their asset lands relative to a competitor competing in the same indication. For biotech sponsors specifically, the stakes are even more pronounced. Early phase data exists to specifically increase the value of an asset enough to support a transaction, a public offering, or the next funding round. A sponsor racing toward a specific milestone, an interim analysis, a filing, a funding round, does not have the luxury of absorbing a timeline miscalculation quietly. A month lost to an outdated assumption about a region's startup time shows up directly in the next conversation with investors. A sponsor who selects a study partner based on name recognition alone, without matching that decision to what the specific study actually needs, usually does not discover the gap until the study is already running, at which point the cost of changing course is high. The promise behind a global clinical trial is real, though risk is often introduced by poor planning and false assumptions about timelines, about how enrolment arrives and the misunderstanding the value a CRO brings. These issues can be addressed by properly matching a study's regional, therapeutic and operational needs with the right CRO partner who genuinely has the ability to delivery them and from being honest about which assumptions are built on current data and which were simply carried over from the last study.
Dan Gallagher Dan Gallagher is a senior commercial leader on Emerald Clinical Executive Team with 17 years of drug development experience driving business development, operational transformation and strategic growth for global CROs and biotech service providers. Currently serving as Senior Vice President, Head of Business Development and acting Chief Commercial Leader at Emerald Clinical, Dan leads a global commercial organisation spanning business development, contracts & proposals and marketing. Email: dgallagher@emeraldclinical.com
Journal for Clinical Studies 11
Regulatory Affairs
The Balancing Act of a Single Pivotal Trial in Ophthalmology The US (United States) Food and Drug Administration’s (FDA) long-standing mission is to protect and promote public health. In fact, the approval of a drug by the US FDA typically signals to the world that a drug is safe and effective.1 The FDA’s mandate includes both rigorous oversight of regulated products to ensure their safety and effectiveness as well as employing regulatory flexibility to respond to innovation and promote timely access to new treatments. The US regulatory environment has evolved substantially since the federal government first started to regulating food production and drug development. The initial regulatory policy enacted by the US dates to 1906 with the Pure Food and Drug Act, which was spurred on by Upton Sinclair’s book The Jungle. The Jungle created an intense focus on the quality of food after Sinclair’s book highlighted the shortcomings of the meatpacking industry in Chicago, IL, US. While the Pure Food and Drug Act was the first step toward pushing manufactures to create safe and effective products, the Act only required manufactures to accurately list product ingredients.2 The 1938 Food, Drug and Cosmetic Act later required drugs to be nontoxic and the 1962 Kefauver-Harris Drug Amendments required drugs to be efficacious.2 The guidance around producing a safe and efficacious drug has continued to evolve over the years, though the FDA’s default stance for demonstrating safety and efficacy has been two pivotal clinical trials since the 1962 Kefauver-Harris Amendments.2 One could define a pivotal trial as a trial that serves as the basis of approval for a new drug or an existing drug for a new indication.1,3 The requirement for two pivotal trials stems from the policies requirement for ‘an adequate and well-controlled clinical investigation.’3,4 Yet, legislation was passed in 1997 that allowed for drug approval with a single pivotal trial to help bring drugs to market faster, especially for those who are suffering from debilitating disease without treatments.5 The FDA has since (2023) provided draft guidance on how to conduct a single pivotal trial.6 Statistically speaking, with the two-trial requirement, Sponsors are required to demonstrate that a new drug is effective more than once, thus reducing the likelihood of a false positive result, potentially leading to an incorrect conclusion that a drug is efficacious. Certain kinds of products in development, especially those aiming to treat diseases with high unmet need and no available therapies, such as rare diseases and oncology indications in particular, faced significant challenges in conducting two standalone trials. Recruiting participants from very small and typically heterogenous disease populations for two trials is often not feasible. Further, the use of placebo controls in diseases that are rapidly progressing, severely debilitating and/or fatal is not ethical. Thus, fulfillment of a two 12 Journal for Clinical Studies
adequate and well controlled trial mandate was often not possible in these circumstances.2–4 Flexibility in regulatory decision-making advanced with the emergence of innovative and disease targeted treatments ranging from therapeutic biologics (antibodies, enzymes, proteins, etc.) in the 1990s and more recently to individualised gene therapy/gene editing/ T-cell immunology and Chimeric Antigen Receptor T-cell (CART) therapeutic products.2 Much of this flexibility has been facilitated by FDA designations/processes such as Orphan, Fast-Track, Breakthrough Therapy, Accelerated Approval, or Priority Review, which again are aimed at bringing treatments to market faster for those who are in need of rehabilitative or life-saving treatments.7 The FDA’s acceptance of one trial for approval has become more common. This is highlighted by work from Zhang et al. who found that 19.4% of approvals between 1995–1997, 39.7% of approvals between 2005 and 2007 and 47.2% of approvals between 2015 and 2017 were supported by single pivotal trial.3 Zhang et al. found that using an accelerated pathway programme was not required for a single pivotal trial approval.3 The caveat with the single trial approval approach is the expectation for Sponsors to provide confirmatory evidence of a product’s safety and efficacy, from related indications, animal models, class effects and real-world evidence.5 In FDA’s accelerated approval pathway, which allows product approval with some limitations based on evidence from a single arm trial, a confirmatory trial may be required to be underway at the time the accelerated approval is granted. Products approved through the one trial regulatory process have benefited millions of patients globally. This year, senior FDA officials announced in a New England Journal of Medicine article, a shift in policy indicating that the FDA’s new default is a single pivotal trial.5 Confirmatory evidence is still expected, but reviewers are now to focus on the robustness of a single clinical trial namely trial quality, including magnitude of effect, appropriateness of control arms, endpoint selection, statistical power, masking, handling of missing data, biological plausibility (mechanistic or pharmacodynamic data, confirmation of target engagement) and exposure-response on biomarkers and clinical outcomes, among others. Like so many of the FDA’s initiatives, time will tell how the impact of this policy shift will unfold. With all of this in mind, the authors would like to highlight a theoretical framework for the approval of a drug aimed at treating dry eye disease (DED), which is an indication that has typically required two or more pivotal trials to achieve FDA approval. While the proposed framework is just theoretical, it can help Sponsors develop a plan to seek FDA feedback for their own path towards a single pivotal trial in their indication of interest. Volume 18 Issue 2
Regulatory Affairs Example Theoretical Path to a Single Pivotal Trial DED ‘is a multifactorial, symptomatic disease characterised by a loss of homeostasis of the tear film and/or ocular surface, in which tear film instability and hyperosmolarity, ocular surface inflammation and damage and neurosensory abnormalities are etiological factors.’8 With DED, it is one of the most common indications in the ophthalmology sector,9 so there is a plethora of data on this topic and there is even an FDA draft guidance document for bringing a DED drug to market (Table 1). Evaluating these sources can likewise help a Sponsor understand what primary endpoints may be most suitable for developing a drug. While established endpoints are typically most reliable, novel endpoints are of interest and may provide a drug with market differentiation. Novel endpoints should be discussed with the FDA prior to implementation and are typically included as exploratory outcomes in pre-pivotal trials. Endpoint selection should always be aimed at evaluating the underlying etiological factors promoting the condition. With the most recent Tear Film and Ocular Surface (TFOS) report on DED, three primary DED subtypes were identified (tear film deficiencies, eyelid abnormalities and ocular surface abnormalities). Each have different underlying deficiencies that can be evaluated with different, established endpoints.8 Table 2 lists the most common endpoints, which should be selected for a clinical trial based upon the novel drug’s underlying mechanism of action and pre-clinical data.
Figure 2: Example Image of Tear Break Up Time: Note that dark spots over the cornea are regions of tear break up. Image captured by Andrew D. Pucker, OD, PhD.
With DED, the FDA draft guidance indicates a need for assessment of two primary outcomes (one sign and one symptom pertinent to the condition) unless evaluating a specific primary outcome (Table 2). In considering the clinical development of a new DED treatment, it is highly recommended to fully evaluate the clinical trial designs and outcome measures that have been utilised previously. This process could start by evaluating public sources of clinical trial information such as ClinicalTrials.gov and the peer-reviewed literature.9 With our theoretical trial, we will start with a Phase II trial based upon developing a topical, small molecule drug and a well-established pathway for DED. Starting with a Phase II trial to establish initial safety and efficacy of our drug is well supported by the literature. This is highlighted by Pucker et al. who determined that there have been 346 DED clinical trials conducted as of 2024 with only 4 of these trials being pure Phase I trials.9 The authors likewise found that the typical Phase II trial (n = 42) enrolls a median of 207 participants, which can provide a basis for a starting sample size given that first in human studies often do not have a fully powered sample size.9 Our drug has a mechanism of action and pre-clinical data suggesting that it has the potential to improve tear volume and tear stability.
Tear volume is typically evaluated with a Schirmer’s test (Figure 1; Table 2), and tear stability is typically evaluated with tear break up time (TBUT; Figure 2; Table 2). Since the primary goal for the Phase II trial is to establish initial safety and efficacy, safety measures are selected as the primary outcomes and Schirmer’s test and TBUT are selected as secondary outcomes. Including multiple secondary outcomes can allow for a proof of concept to provide guidance for the selection of the primary efficacy outcome in a planned pivotal trial. While not always required, the optimal dose is likely unclear for your novel drug, so the FDA typically requires a dose finding aspect to your Phase II trial. Pre-clinical data suggests that concentrations A, B and C could be safe and effective in patients. DED treatments tend to have a high placebo effect and published data clearly indicate that treatments as simple as topical saline have a positive effect on the signs and symptoms of DED;10 thus, you should consider designing a randomised trial where participants are allocated in a 1:1:1:1 manner to concentrations A, B and C or vehicle. Likewise, with the placebo effect being a major potential confounding factor, all participants in your trial should complete a two-week run-in phase with vehicle to help ensure that your eligible participants still have symptoms while on vehicle and that the enrolled participants can demonstrate reasonable treatment compliance. Controlling for treatment compliance is also a major factor in chronic disease trials like DED, because the longer a participant uses a drug, the less likely they are to take it on schedule and could subsequently diminish the chances of observing a treatment effect.11,12 You also conduct an unpowered trial with a sample size of 200 participants (50/participants group), so you can gain an initial understanding of your drug’s pharmacologic
Figure 1: Example Image of Schirmer Test, Image created with AI by Andrew D. Pucker, OD, PhD.
Figure 3: Example of Dry Eye Visual Analog Scales
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Journal for Clinical Studies 13
Regulatory Affairs Trial Design
Traditional Histology-Based Trial Design Parallel, randomised, double-masked trails should be conducted.
Comparator
If evaluating efficacy, a natural exposure/environmental trials should be at least 2-weeks in duration and if evaluating safety, trials should be least 6 weeks in duration if efficacy trials are shorter.
Trial Population
Drug should demonstrate a clinically meaningful and a significant improvement over vehicle control. Symptomatic dry eye disease participants should be enrolled. Inclusion criteria should include both signs and symptoms of dry eye disease.
Efficacy Considerations
A representative sample of dry eye disease participants should be enrolled. Safety and efficacy should be demonstrated in at least two well-controlled, multi-center trials (Note – now possibly only one very robust trial). A significant between-group difference should be demonstrated for at least one dry eye disease sign and symptom, or A significant between-group difference in complete resolution of corneal staining, or
Safety Considerations
A significant between-group difference in participants having a ≥10 mm increase in Schirmer’s test. At least 400 participants treated with the drug at a concentration at least as high as that of the intended marked use.
Clinical Evaluations
At least 300 participants should have completed at least 6 weeks of follow up and at least 100 participants should have at least 1 year of follow up (International Council for Harmonisation [ICH] requirement). Distance best corrected visual acuity should be conducted at every visit. Ocular comfort should be evaluated before and after drug application at every visit. Ocular health should be adequality evaluated with a slit-lamp. Program should include a trial that evaluates the following safety outcomes over at least 3 months: systemic laboratory evaluation, corneal endothelial cell counts, and dilated fundus examination. Table 1: Summary of Key Food and Drug Administration (FDA) Draft Dry Eye Disease Guidance
activity and have the data needed for sample size planning for your upcoming pivotal trial. Planning a 6-week treatment phase post-run in to collect sufficient initial safety data is optimal because your drug’s mechanism of action suggests that the treatment effect will improve with time. Optimally, after completing your Phase II trial you find that your drug is safe and that there is a clear trend towards improvement in TBUT at 4 and 6 weeks. You also find that concentration C provided the largest numerical improvement compared to vehicle at both 4 and 6 weeks. Therefore, after consulting with the FDA, you select TBUT as the primary sign outcome for your pivotal trial and you select the Eye Dryness VAS as your primary symptom outcome for your pivotal trial because it also showed an improvement during Phase II testing and because the Eye Dryness VAS is the most common symptoms questionnaire used in the DED space (Figure 3).13 Since you are gearing up to complete a single pivotal trial, you elect to enroll 800 participants, so you can ensure that at least 400 participants are treated with the drug at the marketed use concentration (overall count includes participants from Phase II trial to help account for attrition provided there are no substantive formulation changes and the pivotal trial test material is comparable to earlier trial material). The sample size is large to help demonstrate safety and is much more than the projected sample size for finding between group differences for TBUT. This trial randomises participants in a 1:1 manner to concentration C or vehicle after they complete a 2-week run-in phase. Additionally, one year of safety data is needed for at least 100 treated participants. Since it is more challenging to enroll participants for a one-year trial and since you do not need all 800 participants to complete a one-year trial, you randomise the first 300 14 Journal for Clinical Studies
participants in your trial to the one-year segment of your trial. The authors’ experience has found that the US FDA typically requires corneal endothelial cell counts data over one year, so the entire first group of participants complete this outcome at baseline, 12 weeks and 52 weeks. After 300 participants are enrolled, the remaining 500 participants are only asked to complete 12 weeks of follow up to help ensure sufficient follow up to demonstrate durable efficacy. Your pivotal trial likewise includes safety laboratory data on all participants and you include pharmacokinetic (PK) testing at two of your clinical sites, so sufficient PK data is collected from about 20 treated participants to understand systemic drug exposure from a topical ocularly administered drug. You conduct this trial with 40 clinical trial sites that are well-trusted across the US and potentially key European countries that you also hope to gain approval in to help ensure the DED population is representative and that the trial is enrolled within your 8-month target timeline.9,14 If enrolling in nonUS countries, different regulatory standards likely apply and they should also be incorporated into the trial design. After completing your trial, you obtain amazing results and you demonstrate a statistically significant and clinically meaningful improvement in both TBUT and Eye Dryness VAS scores. There are also no quality issues either with trial data integrity or inspections in general as they contribute to the quality robustness expected for a one trial approach. You also demonstrate a good safety profile and ultimately, the US FDA approves your drug. While the above scenario demonstrates that a Sponsor can potentially obtain approval of a DED drug with a single pivotal trial after a well-planned initial safety trial, everything will likely Volume 18 Issue 2
Regulatory Affairs Ocular Surface/Dry eye Disease Symptoms Ocular Surface Disease Index (OSDI)
The OSDI has three question groups (12 total questions) that ask about experiencing ocular symptoms (e.g., blurred vision), having trouble with performing visual tasks (e.g., reading, driving at night), and having trouble with environmental factors (e.g., windy conditions, low humidity environments) over the past week.18
Eye Dryness Visual Analog Scale (VAS)
A VAS consists of a horizontal line (continuous scale without numbers) with an anchor on each end; the left-side anchor typically represents the complete absence of symptoms while the right-side anchor typically represents the most extreme presentation of symptoms.19-21 The Eye Dryness VAS typically probes the named symptom at the visit.
Ocular Discomfort Visual Analog Scale (VAS)
The Ocular Discomfort VAS is like the Eye Dryness VAS, but it probes the named symptom at the visit. .
Drop Tolerability The Drop Tolerability VAS is like the Eye Dryness VAS, but it probes the named symptom typically at drop application Visual Analog Scale in office. It might also measure this symptom for up to 10 mins post-drop use. (VAS) Symptom Assessment in Dry Eye (SANDE)
The SANDE is a standardised and psychometrically tested 2-item VAS used to quantify the frequency (question 1) and severity (question 2) of dry eye and/or ocular irritation symptoms.22
Tear Volume Schirmer’s Test
Schirmer’s Test evaluates tear volume with a 5 mm wide piece of standard filter paper that is 35 mm long. It is performed by placing the strip in the conjunctival sac and letting most of the filter paper hang over the edge of the eye for 5 minutes.23,24 Tear volume is determined by the amount of strip wetting.
Tear Film Osmolarity
Tear osmolarity is a key diagnosis test for dry eye because participants with dry eye tend to have higher tear osmolarities due to a lower tear volume and higher tear solute concentrations.25 It is typically measured with a device called TearLab Osmolarity System.
Ocular Surface Damage/Irritation Corneal Fluorescein Staining
Corneal surface damage is typically evaluated with a vital dye called sodium fluorescein, which highlights breaks/ damage.26 Damage is typically quantified with an integer grading card.
Conjunctival Staining
Conjunctival surface damage is typically evaluated with a vital dye called lissamine green, which highlights breaks/damage.26 Damage is typically quantified with an integer grading card.
Tear Stability TBUT is a measure of tear stability. Sodium fluorescein causes the tear film to be more visible when viewed with a Tear Break Up Time cobalt blue light with and a slit-lamp biomicroscope.27 During the procedure, the participants are asked to blink, (TBUT) and the amount of time after the blink until the first dark spot is seen in the tear film is timed with a stop watch.27 Faster TBUTs are considered to be worse because faster times signify worse tear stability.28 Table 2: Common Endpoints in Dry Eye Disease Trials13
need to go according to plan to accomplish this feat. In the case where the pivotal trial demonstrated a clinically meaningful improvement in symptoms, but the primary sign outcome failed to reach significance, an additional pivotal trial may be needed to confirm efficacy. Interestingly, the DED guidance does indicate that demonstrating sign and symptoms efficacy does not need to occur in the same trial, which can be useful if efficacy is not fully proven upon first attempt.14 Other concerns such as the cost of running and investing in a single pivotal trial with 800 participants or including all of the needed outcomes (laboratory evaluation, corneal endothelial cell counts, PK, adequate participants in the safety database), some of which require one year of follow up, in a single trial may cause Sponsors to opt for the two pivotal trial approach. Therefore, even though the US FDA has indicated that their new default path is a single pivotal trial,5 the scientific community is still sceptical and hesitant to embrace this approach.15–17 It is likewise unclear if a single pivotal trial will be sufficient in non-US markets. As noted earlier, time will tell whether the single trial approach to registering a product for marketing will be acceptable. In any case conducting a well-executed, well-designed trial that is executed robustly will provide the opportunity for the investigational product to become a successful treatment for patients. www.journalforclinicalstudies.com
REFERENCES 1. 2. 3.
4.
5. 6.
7.
8.
Downing NS, Aminawung JA, Shah ND, et al. Clinical trial evidence supporting FDA approval of novel therapeutic agents, 2005-2012. JAMA 2014;311:368-77. Darrow JJ, Avorn J, Kesselheim AS. FDA approval and regulation of pharmaceuticals, 1983-2018. JAMA 2020;323:164-76. Zhang AD, Puthumana J, Downing NS, et al. Assessment of clinical trials supporting us food and drug administration approval of novel therapeutic agents, 1995-2017. JAMA Netw Open 2020;3:e203284. Code of Federal Regulations. §21-314.126 https://www.Ecfr.Gov/current/ title-21/chapter-i/subchapter-d/part-314/subpart-d/section-314.126. March 4, 2002. Accessed July 22, 2026. Prasad V, Makary MA. One pivotal trial, the new default option for FDA approval - ending the two-trial dogma. N Engl J Med 2026;394:815-7. US Food and Drug Administration. Demonstrating substantial evidence of effectiveness with one adequate and well-controlled clinical investigation and confirmatory evide. September 2023. Https://www.Fda.Gov/media/172166/ download. Accessed August 9, 2026. Beaver JA, Howie LJ, Pelosof L, et al. A 25-year experience of us food and drug administration accelerated approval of malignant hematology and oncology drugs and biologics: A review. JAMA Oncol 2018;4:849-56. Perez VL, Chen W, Craig JP, et al. TFOS DEWS III: Executive summary. Am J Ophthalmol 2026;282:135-45. Journal for Clinical Studies 15
Regulatory Affairs
9. 10. 11.
12.
13.
14.
15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.
Pucker AD, Derthick N, Scott L. Running the enrollment numbers on ophthalmic clinical trials in the united states. Optom Vis Sci 2024;101:523-9. Pucker AD, Ng SM, Nichols JJ. Over the counter (OTC) artificial tear drops for dry eye syndrome. Cochrane Database Syst Rev 2016;2:CD009729. Blaschke TF, Osterberg L, Vrijens B, et al. Adherence to medications: Insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu Rev Pharmacol Toxicol 2012;52:275-301. Baumgartner PC, Haynes RB, Hersberger KE, et al. A systematic review of medication adherence thresholds dependent of clinical outcomes. Front Pharmacol 2018;9:1290. Nichols KK, Evans DG, Karpecki PM. A comprehensive review of the clinical trials conducted for dry eye disease and the impact of the vehicle comparators in these trials. Curr Eye Res 2021;46:609-14. US Food and Drug Administration. Guidance for industry: Dry eye: Developing drugs for treatment guidance for industry. December 2020. Https://www.Fda. Gov/regulatory-information/search-fda-guidance-documents/dry-eyedeveloping-drugs-treatment-guidance-industry. Accessed July 22, 2026. Peck CC. One pivotal trial for fda approval - ending the two-trial dogma. N Engl J Med 2026;395:207-8. Singh M, Singh Y. One pivotal trial for fda approval - ending the two-trial dogma. N Engl J Med 2026;395:208. Xu I, Ulrich IP, Lurie P. One pivotal trial for fda approval - ending the two-trial dogma. N Engl J Med 2026;395:207. Schiffman RM, Christianson MD, Jacobsen G, et al. Reliability and validity of the ocular surface disease index. Arch Ophthalmol 2000;118:615-21. Maxwell C. Sensitivity and accuracy of the visual analogue scale: A psychophysical classroom experiment. Br J Clin Pharmacol 1978;6:15-24. Carlsson AM. Assessment of chronic pain. I. Aspects of the reliability and validity of the visual analogue scale. Pain 1983;16:87-101. McMonnies CW. Measurement of symptoms pre- and post-treatment of dry eye syndromes. Optom Vis Sci 2016;93:1431-7. Schaumberg DA, Gulati A, Mathers WD, et al. Development and validation of a short global dry eye symptom index. Ocul Surf 2007;5:50-7. Beetham WP. Filamentary keratitis. Trans Am Ophthalmol Soc 1935;33:413-35. Klaassen-Broekema N, Mackor AJ, van Bijsterveld OP. The diagnostic power of the tests for tear gland related keratoconjunctivitis sicca. Neth J Med 1992;40:113-6. Sullivan BD, Whitmer D, Nichols KK, et al. An objective approach to dry eye
16 Journal for Clinical Studies
26. 27. 28.
disease severity. Invest Ophthalmol Vis Sci 2010;51:6125-30. Bron AJ, Evans VE, Smith JA. Grading of corneal and conjunctival staining in the context of other dry eye tests. Cornea 2003;22:640-50. Norn MS. Desiccation of the precorneal film. I. Corneal wetting-time. Acta Ophthalmol (Copenh) 1969;47:865-80. Lemp MA, Hamill JR, Jr. Factors affecting tear film breakup in normal eyes. Arch Ophthalmol 1973;89:103-5.
Mary Newman Mary Newman is a regulatory strategy expert with more than 37 years of experience guiding therapies from discovery through approval across small molecules, biologics, as well as gene and cell therapies. Mary’s deep development expertise has helped create clinical and regulatory strategies for numerous products in therapeutic areas such as rare disease, oncology and ophthalmology. Mary is also the Founder and CEO of Alchemy Development Partners, a regulatory consultancy advising early- and mid-stage biopharma companies.
Andrew D. Pucker Dr. Andrew Pucker is the Chief Development Office of Mintra Health. He is a worldrenowned clinician-scientist with deep expertise in ophthalmic disease and clinical trial design. He earned his OD, MS and PhD degrees from The Ohio State University and he is an adjunct professor at the University of Alabama at Birmingham. Dr. Pucker’s independent research career has focused on ocular surface disease, contact lenses and myopia development.
Volume 18 Issue 2
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Journal for Clinical Studies 17
Regulatory Affairs
Pharmacovigilance Under Pressure:
Why the Workflows Surrounding Case Management Can No Longer Be Left to Chance Despite significant investment in safety database technology, much of the operational work surrounding pharmacovigilance case management aggregate report scheduling, PSMF governance, PV agreement management, medical dictionary upgrade and query management as well as affiliate oversight remains heavily manual. The pharmaceutical industry has made considerable investments in digital safety infrastructure over the past two decades and the results in individual case management are tangible. Safety databases have transformed how individual case safety reports (ICSRs) are received, processed and submitted bringing rigour, speed and scalability to a function that previously resisted both. Yet for all that progress, a significant proportion of the operational work surrounding those systems has not changed in any meaningful way. Aggregate report scheduling, pharmacovigilance system master file (PSMF) management, PV agreement maintenance, medical term query management and affiliate oversight are still largely managed through spreadsheets, shared drives and email. The consequence, measured in expert time, is more significant than it might appear on paper. Across large pharmaceutical companies, between 40 and 50 per cent of expert PV time is currently consumed by administrative coordination rather than active safety assessment. That proportion represents a structural problem that has built up over years, absorbed into the operating costs of running a PV function without ever being properly addressed. Experienced scientists who trained to identify safety signals are spending a disproportionate share of their time chasing approvals, reconciling
18 Journal for Clinical Studies
document versions and managing schedules through tools that were never intended for these tasks. Most PV teams have been living with this for years. They know exactly where the time goes and why it has been so difficult to claw back. Other priorities have always taken precedence and the assumption has always been that any lasting solution would involve replacing the core safety database, a daunting and unthinkable prospect. Yet doing nothing risks leaves companies exposed, for example in the event of a difficult inspection. The Hours Lost by Science The workflows bearing the brunt of persistent inertia are those that are document-intensive and dependent on contributions from multiple people and that recur on a defined cycle and demand a traceable record (one that can’t be reliably produced via ad-hoc systems). For a large pharma organisation with a broad marketed portfolio, aggregate reporting alone can mean hundreds of Periodic Benefit-Risk Evaluation Report (PBRER)), Periodic Adverse Drug Experience Reports (PADERs), Development Safety Update Reports (DSURs) and annual safety reports due across the year. Coordinating those schedules, assigning contributors, tracking versions and monitoring submissions is a substantial overhead in its own right and one that compounds as portfolios expand without a corresponding growth in headcount. The PSMF adds an ongoing governance challenge. The PV Master File is both a regulatory requirement and a living document, one that must accurately reflect the current state of a company’s PV system at all times. For companies managing multiple PSMFs across EU and local variations, that means structured review cycles, version control
Volume 18 Issue 2
Regulatory Affairs and clear records of who approved what and when. Companies managing this through shared drives typically have no structured review process and no reliable way to establish who made a given change before a document was issued. The practical consequences are far from abstract: a PSMF submitted for inspection containing a duplicated chapter or inconsistent annex data, with no illuminating audit trail, is the inevitable consequence of a process not designed to fulfil that standard. PV agreement management and dictionary upgrade processes present their own versions of the same problem. As affiliate and partner structures expand through licensing arrangements, copromotion agreements and growing distributor networks the volume of agreements requiring authoring, review, approval and exchange grows accordingly. Email-based management produces untracked document versions and fragmented oversight, with compliance exposure that tends to surface only at inspection. Medical and product dictionary management tooling has always been a weak point; available solutions have not kept pace with what modern PV requires and the workarounds most companies have constructed struggle to keep up with bi-annual MedDRA updates, without the data integrity that signal detection work depends on. Where Manual Process Meets Regulatory Scrutiny Regulatory expectations around PV documentation have increased substantially in recent years. EU GVP Module II sets out detailed requirements for PSMF governance; FDA guidance on pharmacovigilance practice and inspection expectations has similarly raised the bar on what organisations must be able to demonstrate not just in what they submit, but in how they can account for the processes that produced it. This shift is important. It means regulators are no longer simply checking outputs. They are asking organisations to reconstruct the governance and decision trail that led to those outputs, and in many cases to do so at short notice. For organisations managing their surrounding PV workflows manually, that reconstruction is a significant undertaking. Piecing together an audit trail is not only time-consuming, it also carries genuine risk of incompleteness. Version histories will not have been reliably maintained, sign-off records are informal, while approval sequences have to be inferred from email timestamps, having not been formally documented. The end result may be a plausible account of what happened, but it is unlikely to be a definitive one, and in an inspection context that distinction matters. Heightened expectations by regulators around responsiveness magnify the risk. Affiliate Networks and the Limits of Reconstructed Oversight For large organisations running PV through local affiliates, a further layer of risk exists, largely outside the central system. Local affiliates handle their own ICSR processing, submissions and agreement management through locally maintained tools and central PV functions piece together their oversight from periodic reports and email exchanges. In many cases, central teams will be working from a picture that is less current and less complete than they assume, because the information reaching them is only as timely as the last scheduled update from a given affiliate. That gap can matter gravely. When an inspection requires a precise and concurrent account of both central and local activity, the limitations of that reconstructed picture typically surface quite quickly. The same is true when a serious adverse event demands rapid escalation across territories: a central function that can’t immediately account for what its affiliates are handling is in a compromised position. Incomplete oversight also affects day-towww.journalforclinicalstudies.com
day decision-making in ways that are harder to quantify if the data informing a central safety review is only as current as the last email update from a local affiliate, the quality of that review reflects it, often without those involved realising it. The Case for Building Around What Already Works Safety databases are good at what they were built to do. But beyond case management, those same platforms offer little: other essential activities generally fall outside their scope. Replacing an embedded safety database is not something most large organisations would seriously contemplate, however. The validation burden, combined with the disruption to live PV operations, makes this an inconceivable proposition in all but exceptional circumstances. The practical path, for most organisations, is to address the surrounding workflows as a separate problem, building capabilities that integrate with the existing safety database rather than seek to replace or overlap with it. This supplementary approach also resolves the business case problem that has historically kept these workflows unaddressed. A full-platform procurement is a significant commitment that can seem hard to justify. Starting with a single, high-impact process changes tends to be more palatable. If an experienced PV professional is spending up to half of their working time coordinating aggregate reports, that cost is directly quantifiable. Add the harder-to-measure cost of inspection preparation, the days spent reconstructing email threads and file versions to demonstrate a governance trail that should have been produced automatically and the case for addressing it specifically becomes straightforward. Demonstrating Compliance Should Not Require Reconstruction For critical PV workflows there is a straightforward test of whether a company’s current approach is adequate and that is to ask: if a regulatory authority requested documentation of how a specific report was prepared, who approved the most recent version of a key PV agreement, or what changes were made to a PSMF before submission, could that information be produced without manually reconstructing it from emails and file histories? In organisations relying on shared drives and email to manage these processes, the answer will probably be no, or not in a form that would satisfy an inspector. Increasingly, there will have to be a shift to more formal, GxPvalidated systems with audit trails built in by design, that log every action, every approval and every change in a way that can be retrieved on demand. This would give rise to a fundamentally different kind of compliance record: one that exists continuously rather than being assembled after the fact, and that can be interrogated rather than reconstructed. As drug portfolios continue to grow in complexity and as the regulatory bar for documentation and traceability is raised further, a more systematic approach to the workflows surrounding case management is an overdue response to pressures that have been accumulating, unaddressed, for years.
Gyöngyi Mezey Gyöngyi Mezey is director of product management at Qinecsa Solutions, where she leads development of the company’s PV workflow automation platform, drawing on nearly two decades of hands-on pharmacovigilance experience spanning drug safety operations, clinical coding and PV consulting at organisations including PAREXEL, Roche and IQVIA. She is based in London, UK.
Journal for Clinical Studies 19
Therapeutics
Renal Care in the USA Set for Change in 2026 and Beyond Customised care options expand on results from pivotal trial and FDA clearance Two significant events in the first half of this year have informed new debate around how the standard of care for hemodialysis patients in North America should develop: •
•
Results from a pivotal, long-term clinical trial (MOTheR)1 that directly compared Hemodiafiltration (HDF) and expanded hemodialysis in more than 500 patients have been presented at the European Renal Association (ERA) Congress.2 The U.S. Food and Drug Administration (FDA) has published 510(k) clearance for a new dialyser that will enable an ‘Expanded Hemodialysis with super high-flux membrane,’3 (HDs) approach to be introduced to the North American market, bringing the States in line with other, already established, markets around the world.
In this article we look at the science behind the two treatment modalities, compare the growing body of clinical evidence for each and contrast the practical implications for adoption in the North American healthcare system. Elevating Patient Care Around the world, individual physicians and healthcare providers strive to bring the highest standard of affordable care to their patient populations. In the context of renal disease in North America, this translates currently into the Centre for Disease Control and Prevention (CDC) estimating that, in 2026, 14% of U.S. adults, or around 37 million people, will have chronic kidney disease.4 With the USRDS (United States Renal Data System) annual report 2025 noting almost 500,000 Americans receiving dialysis,5 there is a large opportunity to make a meaningful impact on the lives of patients through middle molecule focused therapies that are built around individual patient needs. The care patients receive will likely change over time, as new developments come to the fore and new therapies or treatment modalities emerge. Focusing on hemodialysis, for example, initial research in the 1970s6 introduced HDF as a treatment modality that could not only remove the toxins from the blood that ‘standard,’ hemodialysis achieved, but also eliminate some important so-called ‘middle molecules,’ that are associated with increased inflammation, cardiovascular events, reduced quality of life and increased mortality.
effectiveness to prefer one extracorporeal renal replacement therapy modality to the other for end-stage kidney disease because significant differences in clinically important outcomes have not been shown by available published RCTs.’ In addition, it is important to note that even in the areas of the world where HDF has seen the most adoption (namely Europe and Japan), a variety of challenges mean it is only able to be used for a portion of the total patient population.8 More recently, an alternative approach to middle molecule removal that is based on newly designed dialyser membrane technology has been commercialised. Known as ‘Expanded Hemodialysis,’ (expanded HD) the first approved products for clinical use were introduced in Japan in 2008 and now, with the recent FDA 510k authorisation of ELISIO-HX, super high-flux dialyser for expanded solute removal (HDs), physicians have an additional treatment pathway for their patients within the United States. The new expanded HD treatment modality maintains the principle of a single stage process and is therefore compatible with most existing dialysis equipment. It allows improved dialysis performance without the need to adapt day-to-day procedures. As with HDF, markets outside North America have embraced the clinical evidence that supports the roll-out of expanded HD but, until the recent FDA approval of ELISIO-HX and the integration of expanded HD into their programme of dialysis care by one of the largest U.S. dialysis providers (DaVita Inc.),9 adoption of Expanded HD in the North American healthcare system has been limited, leaving patients behind. Science Compared Although conceived decades apart, HDF and expanded HD were both developed to allow nephrologists to more closely mimic the clearance achieved by a healthy kidney. As clinical knowledge grew, a consensus was quickly reached that the removal of ‘middle molecules’ – specifically those in the 12–60 KDa range – was a key factor in improving mortality and quality of life for patients.10,11,12
Since then, the HDF modality has been refined and further developed to the point where it has gained some traction around the world. Interestingly, PubMed returns 4,810 all-time hits for Hemodiafiltration, with annual publication numbers still growing.
Hemodiafiltration (HDF) is an advanced form of dialysis in which a patient’s blood passes through a highly permeable dialyser, removing waste products and excess fluid by diffusion and convection, while sterile replacement fluid is infused into the blood – typically before or after the filter – to maintain volume as larger amounts of plasma water are filtered. In practice, this combination approach is performed in a specifically designed HDF system.
Many of these publications have proposed the idea that each new development in HDF should/would mean it becomes the standard for advanced renal care. However, a systematic review7 of the available evidence in 2005 concluded: ‘It is not possible on the basis of
In contrast, expanded HD relies on a re-engineering of the core dialysis membrane to extend the performance envelope of a standard dialysis set-up (Figure 2). With expanded HD, there is no additional infrastructure, specialised equipment, or changes to treatment
20 Journal for Clinical Studies
Volume 18 Issue 2
Therapeutics
Figure 1. Diagram shows a typical flow for an HDF set-up and an example system
Figure 2. Diagram showing the expanded HD process, from water treatment and dialysate preparation to blood filtration and dialysate disposal.
procedures required as the engine for improvement sits within the technology of the dialyser itself.
A contemporaneous review of the development of this new class of membranes, published in 201713 concluded: ‘We believe that the best application is the new therapy called ‘expanded hemodialysis…’ This new therapy is likely to modify the outcome of end-stage kidney disease patients, thanks to the enhanced removal of molecules traditionally retained by current dialysis techniques.’
A key consideration when designing a medium cut-off dialyser is to assess and choose the core membrane material and structure: the newly FDA approved dialyser (ELISIO-HX) is made from polyethersulfone (PES) and features a super high-flux class membrane, that is precisely engineered with sharp-cut off technology. Research has shown (see Table 1) that this membrane material has improved safety and efficacy profile compared to other materials. In addition, this dialyser’s membrane is not made using Bisphenol A and features a newly designed hollow fibre structure, bigger pore size and a specific geometry allowing higher performance compared to standard high flux membranes. No. 1
Super High-flux
2
PES membrane
3
PES membrane
4
ELISIO-HX (HDs membrane)
5
6
Category
ELISIO-HX (HDs membrane)
ELISIO-HX (HDs membrane)
Global Lessons A significant body of clinical evidence has been built up that defines and proves the performance of HDF and expanded HD and makes relevant comparisons of the two modalities. Studies in the mid-2010s focused on the performance of new polymers and their influence on overall mortality risk before, researchers in the early 2020s published studies that compared expanded HD with conventional HD and with HDF.
Year
Name
Author
Reference
Takeaway
2021
High-performance dialyzers and mortality in maintenance hemodialysis patients
Abe M. et al.
DOI: 10. 1038/ s41598-02191751-w
Type V dialyzers (containing HDs membrane) were associated with significantly lower I-year au-cause mortality versus
2017
High-Performance Membrane Dialyzers and Mortality in Hemodialysis Patients: A 2-Year Cohort Study from the Annual Survey of the Japanese Renal Data Registry
Abe M. et al.
DOI: 10.1159/ 000478032
Type IV dialyzers. Fully adjusted analysis: HR 0.878, corresponding to a 12.2% lower mortality risk vs Type lV. Propensity score-matched analysis: HR 0.862, corresponding to a 13.8% lower mortality risk vs Type 'V.
2016
The Choice of Hemodialysis Membrane Affects Bisphenol A Levels in Blood
BoschPanadero E. et al.
DOI:10.1681/ ASN.201SO 30312
Nationwide cohort study in Japan HD patients, 2-year follow-up)-Mortality rates may differ depending on dialyzer membrane type. PES membranes showed 10% lower mortality risk compared with PS.
DOI: 10.1111/ 1744-9987.18919
Shows broad middle molecule removal (up to —45 kDa, e.g., AFLC) Comparable overall removal performance to Theranova (MCO reference) Significantly lower albumin loss: HDs membrane: 0.77±0.25 g/session Theranova: 2.14±0.45 g/session (—3x higher)
DOI: 10.1111/ a0r.14466
Similar removal performance ot HX to HOF with 21.1 ± 3.9 L/ session Comparable etticacyvs MCO competitor (Theranova) Significantly lower albumin loss across all comparators: HDs membrane: —0.44 g/session HOF: -0.82 g Theranova: —2.25 g/session
DOI: 10.1159/ 000524644
Expanded HD clearly superior to conventional HD Expanded HD comparable efficacy to MCO dialyzers (Theranova) Expanded HD slightly below high-volume HDF (–31 L), but close in performance Albumin loss: HDs dialyzer: – 1.6 g/session Theranova (HDx): – 2.0 g/session HDF (post-dilution, —31 L): – 2.3 g/session
2022
Clinical performance comparison of two medium cut-off dialyzers
2022
Comparison ot2 medium cutoff dialyzers versus on-line hemodiafiltration regarding depurative ability and albumin loss
2022
Efficacy and Safety of the Medium Cut-Ott Elisio HX Dialyzer
Krieter D.H. et aL
MartinezMiguel P. et al.
Maduell F. etal.
Table 1: Selected publications illustrating key milestones in HD development. www.journalforclinicalstudies.com
Journal for Clinical Studies 21
Therapeutics Fast forward to earlier in 2026, when results from the largest long-term clinical trial (MOTheR) directly compared Hemodiafiltration (HDF) and expanded hemodialysis (expanded HD) were presented at the European Renal Association (ERA) Congress.14 MOTheR is a multi-centre, open label, prospective, 1:1 randomised, parallel group trial that recruited 552 incident haemodialysis patients (270 HDx, 282 OL HDF). The researchers sought to determine whether expanded HD is non-inferior to OL-HDF at reducing a composite outcome of all-cause mortality, stroke, acute coronary syndrome, peripheral artery disease and ischemic colitis. Secondary endpoints of safety, mortality and quality of life measures were also set. Follow-up was 23.6 months in the expanded HD group and 19.9 months in the OL-HDF group. During follow-up, the two groups had similar mortality rates (20.2% [n=53/262] vs 21.4% [n=58/271], respectively). The two groups were also similar in incidence rates of the composite end point (expanded HD, 14.6 events per 100 personyears and OL-HDF, 16.9 events per 100 person-years [incidence rate ratio, 0.87]). The non-inferiority test was statistically significant (P=0.011), confirming the non-inferiority of expanded HD to OL-HDF and indicating that the safety profiles with respect to mortality and major cardiovascular events were similar between the two groups. The authors concluded: ‘The results of the MOTheR study demonstrate that expanded HD is non-inferior to OL-HDF for the prespecified composite endpoint.’ Removing the Barriers to Adoption The introduction of any new technology or patient pathway always takes time but as noted throughout this article, based on the clinical evidence that reports significant improvements in removing the middle molecules responsible for sub-optimal outcomes and quality of life impacts compared to standard HD, the adoption of HDF and expanded HD has progressed in several markets around the world, notably Asia and Europe. Clearly, both give physicians options for raising the bar for dialysis patients, however there are patient eligibility requirements, along with regulatory challenges and significant infrastructure and equipment limitations, for example, for HDF. These issues make HDF a suitable treatment option for a portion of the patient population, while expanded HD – performed on standard dialysis equipment – offers an easily scalable opportunity to raise the bar for a broad portion of the hemodialysis population. Against this background, and to add further US market-specific data and experience, a series of trials and studies using the new HDs dialyser are planned to start now that F.D.A. 510(k) clearance has been obtained. The upcoming studies will measure a variety of data points including mortality, hospitalisation, removal performance, albumin retention, medication reduction and overall quality of life metrics. Conclusion Researchers and clinicians seem aligned on the opinion that the new treatment modalities now available mark a significant shift in the quality of care they can provide for their dialysis patients. Both HDF and expanded HD have met the mark in terms of safety and efficacy and are now growing in adoption outside of the U.S. where they are becoming common options for improving care. The recent commitment from major US providers to scale up expanded HD is underpinned by the new clinical evidence from the most recent European clinical trial showing that expanded HD is non-inferior to HDF and the availability 22 Journal for Clinical Studies
of the new HDs dialyser. The simplicity and practical advantages of expanded HD finally look set to drive the adoption of the therapy in North America, transforming US dialysis outcomes. REFERENCES 1. 2. 3. 4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
European Renal Association. 2026 European Renal Association Congress, Abstract 27, 2026. Expanded hemodialysis, also called: HDxTM (Vantive Inc.) / HDs (NIPRO Corp.) ELISOTM-HX, NIPRO Corp.) Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2026. https://www.cdc.gov/ kidney-disease/php/data-research/index.html (accessed 2026-08-10). United States Renal Data System. 2025 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States; National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases: Bethesda, MD, 2025. https://usrds-adr.niddk.nih.gov/2025/introduction. Henderson, L. W.; Colton, C. K.; Ford, C. A. Kinetics of Hemodiafiltration. II. Clinical Characterization of a New Blood Cleansing Modality. J. Lab. Clin. Med. 1975, 85 (3), 372–391. Rabindranath, K. S.; Strippoli, G. F.; Roderick, P.; Wallace, S. A.; MacLeod, A. M.; Daly, C. Comparison of Hemodialysis, Hemofiltration, and Acetate-Free Biofiltration for ESRD: Systematic Review. Am. J. Kidney Dis. 2005, 45 (3), 437–447. Ficociello, L. H.; Busink, E.; Sawin, D.-A.; Winter, A. Global Real-World Data on Hemodiafiltration: An Opportunity to Complement Clinical Trial Evidence. Semin. Dial. 2022, 35 (4), 267–275. https://doi.org/10.1111/sdi.13085 DaVita. DaVita Scales Expanded HD to Target Middle Molecules. DaVita Newsroom, Aug 5, 2026. https://newsroom.davita.com/davita-scalesexpanded-hd-to-target-middle-molecules/ Zickler, D.; Schindler, R.; Willy, K.; Martus, P.; Pawlak, M.; Storr, M.; Hulko, M.; Boehler, T.; Glomb, M. A.; Liehr, K.; et al. Medium Cut-Off (MCO) Membranes Reduce Inflammation in Chronic Dialysis Patients—A Randomized Controlled Clinical Trial. PLoS One 2017, 12 (1), e0169024. https://doi.org/10.1371/journal. pone.0169024. Roumelioti, M. E.; Nolin, T.; Unruh, M. L.; Argyropoulos, C. Revisiting the Middle Molecule Hypothesis of Uremic Toxicity: A Systematic Review of Beta-2-Microglobulin Population Kinetics and Large-Scale Modeling of Hemodialysis Trials In Silico. PLoS One 2016, 11 (4), e0153157. https://doi. org/10.1371/journal.pone.0153157. Hutchison, C. A.; Wolley, M. The Rationale for Expanded Hemodialysis Therapy (HDx). Contrib. Nephrol. 2017, 191, 142–152. https://doi.org/10.1159/ 000479262 Ronco, C.; La Manna, G. Expanded Hemodialysis: A New Therapy for a New Class of Membranes. In Current Perspectives in Kidney Diseases; La Manna, G., Ronco, C., Eds.; Contrib. Nephrol., Vol. 190; Karger: Basel, 2017; pp 124–133. https://doi.org/10.1159/000468959. European Renal Association. 2026 European Renal Association Congress, Abstract 27, 2026.
Kaname Sadahiro Kaname Sadahiro has more than three decades of experience in dialysis and blood purification. Since joining Nipro Corporation in 1988, he has been involved in product planning, development, clinical evaluation, technical support and global commercialisation of blood purification products. His expertise includes dialysers, hemodialysis (HD), hemodiafiltration (HDF), high-performance dialysers and expanded solute removal. He has promoted clinical evidence development and collaboration with physicians and healthcare institutions worldwide. In recent years, he has led global product development, regulatory strategy, clinical evaluation and commercialisation, including initiatives in the United States. Since June 2026, he has served as Senior Executive Officer and Head of CPO Product Strategy Headquarters.
Volume 18 Issue 2
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Journal for Clinical Studies 23
Therapeutics
Advancing Precision Medicine in Oncology:
Companion Diagnostic Devices and IRB Best Practices for Identification and Regulatory Assessment Precision medicine has transformed the landscape of cancer treatment by shifting from histology-based to biomarker-driven therapies and enabling targeted interventions within molecularly defined patient populations. This paradigm shift has incorporated adaptive clinical trial designs and companion diagnostic (CDx) devices for participant selection, treatment assignment and response monitoring. However, CDx devices present distinct regulatory and ethical challenges. Institutional Review Boards (IRBs) are pivotal in systematically identifying CDx utilisation within clinical protocols, evaluating device-associated risks and ensuring adherence to regulatory compliance, ultimately safeguarding the rights and welfare of trial participants. Cancer treatment has traditionally been guided by tumour site, histology, stage and prior therapy. While still important, these factors no longer fully define therapeutic opportunities. Precision medicine has transformed oncology by shifting treatment decisions toward biomarker-driven approaches that use tumour-specific molecular features such as genomic alterations, protein expression, immune signatures and resistance mechanisms to determine eligibility, guide treatment and monitor response. Precision oncology relies on two linked systems: clinical trials that evaluate targeted therapies in molecularly defined populations and diagnostic technologies that identify those populations accurately and reproducibly. This article examines companion diagnostic devices in precision oncology and presents a practical Institutional Review Board (IRB) framework for biomarker-guided oncology trials. The framework will help reviewers identify companion diagnostic use, assess regulatory status, evaluate participant risk, strengthen informed consent and document review determinations. Limitations of Traditional Clinical Trial Designs Traditional oncology trials generally evaluate a single investigational product in a single disease population using fixed eligibility criteria, fixed randomisation, a prespecified sample size and a stable statistical analysis plan. The traditional trial design is familiar, interpretable and relatively straightforward for investigators, participants, regulators and IRBs. However, it is often inefficient in precision oncology. Patients with the same histologic diagnosis may have distinct molecular drivers, whereas patients with different tumour types may share the same actionable alteration. Broad histology-based enrolment can dilute treatment effects, increase the likelihood of negative trials and expose participants to interventions from which they are unlikely to benefit. Traditional designs also provide limited flexibility once a protocol is underway. Adding a promising arm, discontinuing an ineffective arm, modifying biomarker-defined eligibility, or responding to an evolving standard of care often requires substantial amendments or an entirely new trial. These constraints may delay development, increase cost and slow participant access to effective therapies.5 Such limitations have contributed to the growth of biomarker-guided designs that align trial architecture more closely with tumour molecular biology. Biomarker-Guided Adaptive Platform Study Designs for Precision Oncology Biomarker-guided study designs use molecular, immunologic, 24 Journal for Clinical Studies
pathologic, or other biologic characteristics to define eligibility, stratify participants, enrich for likely responders, assign treatment, or guide response assessment. These designs may rely on single-gene assays, immunohistochemistry, fluorescence in situ hybridisation, polymerase chain reaction testing, next-generation sequencing, circulating tumour DNA analysis, or composite algorithms.6 A biomarker-guided trial is only as reliable as the diagnostic strategy that identifies its target population.7,8 Precision oncology has accelerated the use of biomarker-guided adaptive platform designs, including umbrella and basket trials. These designs are often conducted under master protocols, which provide a single overarching infrastructure for evaluating multiple therapies, sub-studies, biomarkers, or disease populations. When appropriately designed and governed, master protocols can reduce redundancy, improve operational efficiency and generate evidence more rapidly than multiple independent trials. However, their success depends heavily on accurate and reliable biomarker testing. Table 1 summarises key differences between traditional histology-based trial designs and adaptive platform trial designs.11,12 Companion Diagnostic Devices (CDx) as the Backbone of Precision Oncology Companion diagnostic devices occupy a central place in this evolving landscape of precision oncology. As per the FDA, a companion diagnostic is a medical device, often an in vitro diagnostic, that provides information essential for the safe and effective use of a corresponding drug or biological product.13 Companion diagnostics may also include certain imaging devices when they are necessary to guide the appropriate use of a therapeutic product. ‘Essential,’ means that use of the diagnostic device is required in the labelling of the drug or biological product to support patient selection, dosing, monitoring, or other clinically important decisions. Clinical Uses of Companion Diagnostic Devices • • •
Patient Identification: Determine which patients are most likely to benefit from a specific therapeutic product. Risk Mitigation: Identify patients at increased risk of serious treatment-related side effects from a specific therapeutic product. Treatment Response Monitoring: Track treatment response to inform adjustments that may improve safety or effectiveness.
In precision oncology, companion diagnostics include immunohistochemistry assays, polymerase chain reaction tests, fluorescence in situ hybridisation assays, next-generation sequencing panels, liquid biopsy platforms and, in selected contexts, imaging tools. The FDA maintains a public list of cleared or approved companion diagnostic devices, including in vitro diagnostics and imaging tools; the list is periodically updated and should be consulted when evaluating the regulatory status of a specific device. As of July 2026, there are 226 FDA-cleared or approved CDx devices, including 219 for oncology indications and seven for non-oncology indications (Figure 1). Oncology represents a substantial portion of this landscape, with companion diagnostics used in non-small cell Volume 18 Issue 2
Therapeutics Feature
Traditional Histology-Based Trial Design
Primary Organising Single tumour type, histology, stage and line of Principle therapy.
Adaptive Platform Trial Design Master protocol organised around disease area, biomarker-defined subgroups, multiple agents, or evolving therapeutic questions.
Study Structure
Usually, one drug or regimen tested against placebo, standard of care, or another comparator in a fixed design.
Multiple arms or sub-studies may operate concurrently; arms may be added, modified, expanded, or closed over time.
Patient Selection
Eligibility is often based on histology and clinical factors, with biomarkers sometimes used as secondary or exploratory variables.
Eligibility and treatment assignment frequently depend on molecular profiling, biomarker testing, or adaptive enrichment of biomarkerdefined populations.
Flexibility
Limited flexibility once the protocol is finalised; major changes usually require substantial amendments or new trials.
High flexibility when adaptations are prospectively planned, statistically justified and implemented through predefined decision rules.
Efficiency
Can be slower and resource-intensive because each question often requires a separate protocol, trial infrastructure and control group.
Can improve efficiency by sharing infrastructure, screening procedures, control arms, data systems and oversight processes across multiple questions.
Advantages
Straightforward design, simpler consent, clearer statistical interpretation, familiar IRB review pathways, and strong suitability for confirmatory trials.
Accelerates evaluation of multiple therapies, supports biomarkerdriven matching, may reduce participant exposure to ineffective arms and can respond to emerging evidence.
Disadvantages
May be inefficient for rare biomarkers, may enroll biologically heterogeneous populations, may miss tissue-agnostic signals, and may delay development when separate trials are needed.
Requires complex statistical planning, operational coordination, amendment management, data monitoring and clear IRB review of evolving biomarker-based eligibility and treatment-assignment criteria.
Table 1. Comparison of traditional histology-based oncology trial designs and adaptive platform trial designs
lung cancer, breast cancer, colorectal cancer, melanoma, prostate cancer, gynecologic malignancies, gastric cancer and hematologic malignancies. Common biomarkers include EGFR, ERBB2/HER2, KRAS, NRAS, BRAF, PD-L1, BRCA1/2, ALK, MSI/MMR, PIK3CA/ AKT/PTEN, RET and IDH1.14 IRB Best Practices for Identifying Companion Diagnostics and Assessing Regulatory Status Identification of companion diagnostic use should begin with systematic protocol review. IRBs should determine whether any diagnostic, biomarker, molecular, genomic, imaging or laboratory test affects eligibility, stratification, treatment assignment, safety monitoring, dose modification, response assessment, or discontinuation. Table 2 provides a practical review framework for oncology protocols that involve companion diagnostic or biomarkerbased decision-making. Regulatory Assessment Companion diagnostic review intersects with FDA device and human-subject-protection regulations, including 21 CFR Part 812 for investigational device exemptions, 21 CFR Part 56 for IRB review, 21 CFR Part 50 for informed consent, and 21 CFR Part 809 for in vitro diagnostic labeling. Significant-risk device (21 CFR 812.3(m)) studies require FDA IDE approval and IRB approval before initiation. Nonsignificant-risk device investigations may proceed under abbreviated IDE requirements (21 CFR 812.2(b)) when the IRB agrees with the sponsor’s determination and the applicable regulatory conditions are satisfied. This distinction is important because the IRB may accept a nonsignificant-risk determination, but FDA retains authority to determine that a study is significant risk and requires an approved IDE. Key review questions include whether investigational test results could cause participants to forgo or delay effective treatment, expose them to risks beyond standard care or control therapy, reduce www.journalforclinicalstudies.com
Figure 1 showing Companion Diagnostic devices (CDx) approved by FDA up to July 2026 by disease type.
the safety or efficacy of treatment if incorrect, or require invasive specimen collection outside of standard care.22,23,24 Procedural risk should be distinguished from decisional risk: a blood draw or archival tissue review may be physically low risk, while the resulting diagnostic decision may carry substantial clinical consequences. Recommendations for Sponsors and Investigators Sponsors and investigators can support efficient IRB review by clearly identifying companion diagnostic devices in the protocol, protocol-support documents and consent materials. The protocol should describe the test, regulatory status, intended use, specimen requirements, result-reporting process and clinical impact. Sponsors should provide a study-specific device-risk determination and justify whether the diagnostic use is legally marketed and on-label, IDE-exempt approved device, non-significant risk, significant risk, or already reviewed by FDA. Consent forms should clearly explain diagnostic uncertainty, including whether the test is approved for the proposed use, whether it affects enrolment or treatment, whether confirmatory testing is required, whether results will be returned and how indeterminate or failed results will be handled. Clear communication reduces therapeutic misconception and supports meaningful informed consent. Journal for Clinical Studies 25
Therapeutics Oncology CDx Review Question
Where to Look in the Protocol
IRB Reviewer Focus
Is a tumour biomarker required for eligibility?
Inclusion/exclusion criteria; diseasespecific eligibility; molecular testing requirements.
Confirm whether enrolment depends on a specific oncology biomarker, such as EGFR, ALK, ROS1, BRAF, HER2, BRCA, IDH1/2, KRAS, NTRK, MSI-H/dMMR, TMB, or PD-L1 expression.
Is the CDx essential for safe and effective use of the investigational or approved oncology therapy?
Study rationale; investigational product section; prescribing information references; treatment algorithm.
Determine whether the test identifies participants most likely to benefit, those at increased risk, or those requiring monitoring or treatment selection based on biomarker status.
Is testing used to assign treatment, cohort, arm, or dose?
Study design; cohort definitions; randomisation; stratification; dose-escalation or expansion cohort criteria.
Assess whether the CDx result determines treatment arm, tumouragnostic cohort placement, molecularly defined subgroup, or eligibility for targeted therapy or immunotherapy.
What specimen type and collection method are required?
Screening procedures; biopsy requirements; laboratory manual; schedule of assessments.
Evaluate whether testing uses archival tumour tissue, fresh biopsy, blood-based circulating tumour DNA, bone marrow, or other samples and whether any invasive biopsy risks are justified and clearly described.
Is the assay analytically and clinically appropriate for the oncology use case?
Device description; assay methodology; biomarker testing plan; central/local laboratory information.
Review whether the assay platform, such as IHC, FISH, PCR, NGS, liquid biopsy, or imaging-based testing, is appropriate for the cancer type, biomarker, specimen type and treatment-selection purpose.
Device section; FDA approval/clearance What is the regulatory status information; IDE documentation; of the CDx? sponsor device-risk rationale.
Identify whether the CDx is FDA-approved/cleared for the specific oncology indication and drug, used under broader oncology group labelling, investigational, or subject to IDE significant risk/ non-significant risk considerations.
Are false-positive, false-negative, and test-failure risks addressed?
Risks section; limitations of testing; retesting plan; treatment-decision algorithm; Informed consent.
Ensure the protocol and informed consent form explains how inaccurate, indeterminate, insufficient-tissue, or failed test results may affect eligibility, treatment access, delay in therapy, or assignment to an inappropriate treatment.
Are participant protections for genetic and tumor-profiling information adequate?
Informed consent; confidentiality section; genomic data-sharing plan; optional future-use biospecimen language
Confirm that participants are informed about the purpose of testing, privacy protections, return or non-return of results, incidental or germline findings when applicable, data sharing, and future use of biospecimens.
Is the consent clear about investigational oncology treatment implications?
Informed consent; eligibility language; alternative treatment section
Verify that the consent explains how biomarker results may affect study eligibility, access to targeted therapy or immunotherapy, potential exclusion from the study, and available alternatives if the biomarker requirement is not met.
Table 2. IRB reviews framework for identifying companion diagnostic use in oncology protocols
Future Directions and Conclusion Precision oncology will continue to advance through genomic profiling, liquid biopsy, artificial intelligence, adaptive trial designs and biomarker-driven strategies that improve patient matching to targeted therapies. Companion diagnostic devices will remain central to this model by supporting patient selection, treatment monitoring, resistance detection and real-time therapeutic decision-making; however, they also introduce regulatory and ethical complexity because diagnostic results may determine study eligibility, access to treatment, exposure to toxicity, or delays in standard care. IRBs should therefore evaluate diagnostic validity, clinical utility, decisional and procedural risk, informed consent, data privacy, equitable access and the regulatory status of the CDx. By maintaining scientifically informed, participant-centered review and clearly documenting regulatory determinations, IRBs can support responsible innovation in precision oncology while preserving participant safety, rights, welfare and scientific integrity. REFERENCES 1.
Spreafico A, Hansen AR, Abdul Razak AR, Bedard PL, Siu LL. The Future of Clinical Trial Design in Oncology. Cancer Discov. 2021 Apr;11(4):822-837.
26 Journal for Clinical Studies
2.
3.
4.
5.
6.
7.
8.
doi: 10.1158/2159-8290.CD-20-1301. PMID: 33811119; PMCID: PMC8099154. Rulten SL, Grose RP, Gatz SA, Jones JL, Cameron AJM. The Future of Precision Oncology. Int J Mol Sci. 2023 Aug 9;24(16):12613. doi: 10.3390/ ijms241612613. PMID: 37628794; PMCID: PMC10454858. Wu Y, Xue R, Luo X, Liao J, Zhang Z, Deng J, Liu T, Li X, Chen ZS, Yin M. Companion Diagnostics in Clinical Therapy: Current Applications and Future Directions. MedComm (2020). 2026 Mar 1;7(3):e70638. doi: 10.1002/ mco2.70638. PMID: 41777248; PMCID: PMC12950518. Krzyszczyk, P.; Acevedo, A.; Davidoff, E.J.; Timmins, L.M.; MarreroBerrios, I.; Patel, M.; White, C.; Lowe, C.; Sherba, J.J.; Hartmanshenn, C.; et al. The Growing Role of Precision and Personalized Medicine for Cancer Treatment. Technology 2018, 6, 79–100. Ajmera Y, Singhal S, Dwivedi SN, Dey AB. The changing perspective of clinical trial designs. Perspect Clin Res. 2021 Apr-Jun;12(2):66-71. doi: 10.4103/picr.PICR_138_20. Epub 2021 Jan 8. PMID: 34012901; PMCID: PMC8112330. Awwad O, Ahram M, Coperchini F, Jalil MA. Editorial: Precision medicine: recent advances, current challenges and future perspectives. Front Pharmacol. 2024 Jun 7;15:1439276. doi: 10.3389/fphar.2024.1439276. PMID: 38910892; PMCID: PMC11190335. The Personalized Medicine Coalition. PERSONALIZED MEDICINE AT FDA: The Scope & Significance of Progress in 2022; The Personalized Medicine Coalition: Washington, DC, USA, 2022. Courtney Flaherty Biomarker Identification Drives Research and Drug Volume 18 Issue 2
Therapeutics
9.
10. 11.
12.
13. 14. 15.
16. 17. 18. 19. 20. 21.
Development in NSCLC and SCLC. Available online: https://www. onclive.com/view/biomarker-identification-drives-research-and-drugdevelopment-in-nsclc-and-sclc (accessed on 11 September 2024). Bitterman DS, Cagney DN, Singer LL, Nguyen PL, Catalano PJ, Mak RH. Master Protocol Trial Design for Efficient and Rational Evaluation of Novel Therapeutic Oncology Devices. J Natl Cancer Inst. 2020 Mar 1;112(3):229-237. doi: 10.1093/jnci/djz167. Erratum in: J Natl Cancer Inst. 2022 Feb 7;114(2):326. doi: 10.1093/jnci/djab110. PMID: 31504680; PMCID: PMC7073911. https://www.fda.gov/regulatory-information/search-fda-guidancedocuments/master-protocols-drug-and-biological-product-development Duan, XP., Qin, BD., Jiao, XD. et al. New clinical trial design in precision medicine: discovery, development and direction. Sig Transduct Target Ther 9, 57 (2024). https://doi.org/10.1038/s41392-024-01760-0 Rongala, G.; Rongala, D.S.; Rongala, A.N. The Future of Precision Medicine: Targeted Therapies, Personalized Medicine and Formulation Strategies. J. Pharm. BioTech Ind. 2025, 2, 19. https://doi.org/10.3390/jpbi2040019 https://www.fda.gov/medical-devices/in-vitro-diagnostics/companiondiagnostics https://www.fda.gov/medical-devices/in-vitro-diagnostics/list-fdaauthorized-companion-diagnostic-devices-in-vitro-and-imaging-tools https://www.centerwatch.com/insights/regulatory-evaluations-ofcompanion-diagnostic-medical-devices-in-clinical-trials-experiencesof-an-independent-irb/ https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-56 https://www.ecfr.gov/current/title-21/chapter-I/subchapter-H/part-812 https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-56 https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-50 https://www.ecfr.gov/current/title-21/chapter-I/subchapter-H/part-809 https://www.fda.gov/files/about%20fda/published/Significant-Risk-and-
www.journalforclinicalstudies.com
22. 23.
24. 25.
Nonsignificant-Risk-Medical-Device-Studies---Information-Sheet.pdf https://cersi.umd.edu/sites/cersi.umd.edu/files/D1S09-Bijwaard-v1.pdf https://www.fda.gov/regulatory-information/search-fda-guidancedocuments/principles-codevelopment-in-vitro-companion-diagnosticdevice-therapeutic-product https://irbo.nih.gov/documents/138/Research_Involving_IVDsSeptember_2024.pdf Qiao, D.; Wang, R.C.; Wang, Z. Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives. Cells 2025, 14, 1804. https://doi.org/10.3390/cells14221804
Sharad Adekar Sharad Adekar, MD, PhD, CIP, is the Director, Medical Chair at WCG. He is a physician scientist with clinical experience in family practice and pediatrics and research experience in immunology, oncology, cardiovascular diseases, infectious diseases, and neurology. Dr. Adekar has experience in human antibodies in terms of discovery, lead optimization, and preclinical development of monoclonal antibody therapeutics. At WCG, Dr. Adekar oversees IRB board meetings, ensures compliance with federal and state regulations, and provides scientific, ethical, and regulatory guidance to board members and clients. He offers in-depth knowledge in human subject research, scientific, and regulatory research.
Journal for Clinical Studies 27
Clinical Trial Management
Beyond Familiar Sites: Rethinking Site Selection to Bridge Clinical Research and Routine Care Despite the rise of decentralised trial models, clinical research delivery remains fundamentally dependent on sites. Yet the current U.S. site network is overburdened, under-resourced and increasingly strained by growing demand. To address unmet health needs and keep pace with advances in discovery science, the U.S. must expand and modernise its site ecosystem to increase capacity, accelerate patient recruitment and ensure patient representation better reflects real-world U.S. populations, in line with FDA FDORA requirements.1 Thought leaders have long argued that achieving these goals will require broader participation from community-based care facilities (‘non-traditional’ sites) that can extend research into a wider geographic and demographic cross-section of the population.2–4 The authors of a landmark paper in JAMA expressed a broader recognition that clinical research and routine healthcare currently function as ‘a house divided,’ limiting both the generation of evidence and its application to patient care.5 Yet, while many sponsors recognise the need for change, pressing timelines, delivery risk and operational status quo continue to push site selection toward familiar sites.2 The result is a self-reinforcing cycle in which prospective new sites struggle to gain entry. Listening to the Ecosystem To gain a deeper understanding of this predicament, the Association of Clinical Research Professionals (ACRP), with expert input from Altura and support from Genentech, Duke Clinical Research Institute and the National Medical Fellowships, conducted a qualitative research initiative involving in-depth interviews with five sponsors and ten research sites, spanning the spectrum of ‘research-centric,’ to ‘carecentric,’ (Figure 1). A full report is available on the ACRP website.6
Figure 1. Overarching Classification of Site Types, Where ‘care-centric,’ Denotes Community-based Care (primary and preventive) for Large, Local Patient Populations
Although not intended to be representative of the entire industry, the interviews revealed consistent themes. Most notably, sponsors described a lack of practical mechanisms for broadening site networks beyond familiar sites without taking on what they perceived to be disproportionate operational risk. At the same time, aspiring 28 Journal for Clinical Studies
sites, particularly those with non-traditional care-centric models, described a formidable path into research, with a high bar to entry. Together, these insights point to a system that’s unwelcoming to new entrants and fails to give sponsors the necessary level of confidence in engaging them. Sponsors’ Perspectives Two factors weigh particularly heavily in sponsors’ risk calculations: the experience of the Principal Investigator (PI) – both overall research pedigree and therapeutic area expertise – and the familiarity of the site’s model. Lack of experience is often a non-negotiable barrier, while community-based sites, where research is not the dominant activity, are generally perceived as operationally riskier. Yet many sponsors also recognise that, for common chronic conditions, these settings may offer the greatest opportunity: access to large and diverse patient populations, often supported by pre-existing trusted relationships between patients and healthcare providers. Three barriers to change emerged clearly: a lack of practical frameworks for evaluating new and unfamiliar sites; operational pressures (including start-up and enrolment deadlines) that encourage site selection teams to default to familiar options; and reluctance to shoulder the burden of ‘de-risking,’ aspiring new sites alone. Sites’ Perspectives Across site interviews, experience was confirmed as the dominant currency of credibility, imposing a ‘Catch-22’ for aspiring sites. Interviewees observed that sponsors often place unduly heavy emphasis on the credentials of the PI and on highly specific research and therapeutic area experience. They countered that site capability depends on more than a single CV: experienced PIs can help anchor newer sites; conversely, less experienced PIs can be ably supported by a strong sub-I and competent study staff. Similarly, expertise acquired in adjacent therapeutic areas may often be transferable. A broader and more far-sighted assessment of competence and capability could expand the site base without materially increasing risk. With regard to the feasibility process itself, interviewees were unanimous that this remains geared primarily toward traditional academic research environments and added that no useful feedback is ever given to unsuccessful applicants. Matching Site Models to Research Needs Site selection needs a strategic framework that aligns therapy area and protocol requirements with fit-for-purpose site model(s). Research-centric sites remain essential for complex, specialist studies – particularly in areas such as oncology and rare disease – where concentrated expertise, infrastructure and experience are critical. On the other hand, care-centric, community-embedded sites offer distinct advantages for common chronic conditions, namely direct access to rich reserves of patients. Some care-centric organisations already possess mature governance structures, experienced clinicians and proven experience with industry-sponsored interventional clinical trials. Figure 2 shows a prototype decision framework. Volume 18 Issue 2
Clinical Trial Management
Figure 2. Decision Framework for Matching Site Models to Study Types
From Intent to Action: Practical Pathways for Sponsors While many sponsors recognise the need to broaden their site base, translating intent into action requires a more deliberate approach to site development and engagement (Figure 3). Different site models face different barriers and therefore require different strategies. Some emerging research-centric sites may simply need opportunities to demonstrate their capability. Certain care-centric organisations may already be viable research sites but overlooked because of their unfamiliar model. Others will require longer-term cultivation and partnership before they can participate effectively in research. In essence, sponsors need to become partners in building the site ecosystem, rather than simply consumers of site performance. The following workstreams represent practical steps that could begin now while broader changes in strategy, operating models and collaboration start to take shape: •
Evolve the Feasibility Process: Introduce greater flexibility within site assessment, create opportunities for direct dialogue with prospective sites and provide constructive feedback to unsuccessful applicants.
•
Aim to Pilot Non-traditional Sites in Appropriate Studies: For studies in common chronic conditions, consider including a small proportion of appropriately vetted care-centric sites, supported by additional oversight and modest initial enrolment targets.
www.journalforclinicalstudies.com
•
Strengthen Institutional Memory: High staff turnover within sponsors and CROs can cause non-traditional sites to slip off the radar even if they have been previously engaged successfully. Robust, consistently utilised tracking systems are needed to avoid this.
•
Align Incentives with Strategy: Operational teams will continue to default to familiar sites unless incentives and accountability mechanisms (which may involve a combination of federal government and industry stakeholders) explicitly support broader site engagement.
Conclusion The findings of this qualitative inquiry largely confirmed the premise that prompted it: that current site selection practices are limiting the industry's ability to expand capacity, accelerate recruitment and improve participant representation. Our interviews illuminated several of the practical barriers that sustain this pattern. There is an evident supply/ demand imbalance in clinical research: the industry's demand for research capacity continues to grow, while the mechanisms through which new capacity enters the system remain weak. Engaging with and investing in aspiring and emerging sites of all types brings much needed capacity expansion. Leveraging non-traditional, care-centric sites brings access to untapped reserves of prospective participants in the numbers and demographic diversity that research really needs. Ultimately, site selection should no longer be viewed as a routine operational exercise, but as a strategic Journal for Clinical Studies 29
Clinical Trial Management
Figure 3. Strategies for Engaging with Less Experienced and Less Traditional Sites
determinant of whether clinical trials can achieve their scientific, commercial and societal goals.
4.
REFERENCES 1.
2.
3.
FDA. Food and Drug Omnibus Reform Act (FDORA) of 2022. December 5, 2025. https://www.fda.gov/regulatory-information/selected-amendmentsfdc-act/food-and-drug-omnibus-reform-act-fdora-2022 Woodcock J, Araojo R, Thompson T, Puckrein GA. Integrating Research into Community Practice – Toward Increased Diversity in Clinical Trials. N Engl J Med. 2021;385(15):1351-1353. Brown KE, Fohner AE, Woodahl EL. Beyond the Individual: Community-
Pete Fronte
Pete Fronte, MBA is the Founder and CEO of Altura. Altura focuses on mobility as prevention for older adults and expanding access to clinical studies of all types through practical peoplecentred engagement and technology.
Ruma Bhagat
Ruma Bhagat, MD, MPH, is a physician and public health leader with over 20 years of pharmaceutical industry experience. As Executive Director and Head of Strategy in Genentech’s Population Science team, she advances health equity, inclusive research and representation in clinical trials.
Laveina Dash
Laveina Dash is Director of the Doctors in Clinical Trials Research (DCTR) Program at National Medical Fellowships. With more than 28 years of experience in clinical research, she specialises in Investigator development, site readiness and expanding community-based clinical research capacity.
Jenny Jackman
Jenny Jackman, PhD, MBA, has more than 15 years of experience in clinical research spanning site, academic and industry settings. Her work focuses on optimising clinical trial design to meet regulatory and evidence generation needs across therapeutic areas.
30 Journal for Clinical Studies
5.
6.
Centric Approaches to Increase Diversity in Biomedical Research. Clin Pharmacol Ther. 2023;113(3):509-517. Bensken WP, Cottrell EK, Templeton AR et al. The ADVANCE Clinical Research Network Past, Present, and Future: Accelerating Partnerships for Patient-Centered Research in Community-based Primary Care Settings. Med Care. 2026 Feb 1;64(2S Suppl 3):S205-S212. Angus DC, Huang AJ, Lewis RJ et al. The Integration of Clinical Trials with the Practice of Medicine: Repairing a House Divided. JAMA 2024; 332(2): 153–162. https://acrpnet.org/insights/white-papers/perspectives-onmodernizing-the-u-s-clinical-research-site-network-and-reachingnew-patient-communities
Kanecia Zimmerman
Kanecia Zimmerman, MD, PhD, MPH is a paediatrician, epidemiologist, and clinical trialist with particular experience conducting clinical trials and studies in new and understudied conditions and populations to improve patient outcomes. She is also passionate about enhancing participant and workforce representation in this space.
Karen Lipworth
Karen Lipworth is a communications consultant and writer with more than 20 years of experience in clinical research across the United States and Europe. She has served as a patient engagement advocate in multiple cross-sector collaborations.
Kara Bastarache
Kara Bastarache, CAE, is Senior Manager of Workforce Development at the Association of Clinical Research Professionals (ACRP). She leads workforce development and scholarship initiatives and works with industry, academic and government partners to advance the clinical research profession.
Susan P. Landis
Susan P. Landis was Executive Director for the Association of Clinical Research Professionals from 2021-26. She previously led Research Engagement & Marketing Communications at Duke Clinical Research Institute.
Volume 18 Issue 2
Where pharma comes together 6-8 OCTOBER 2026. MILAN, ITALY.
Discover new ideas Meet new partners Create new opportunities
www.cphi.com/europe www.journalforclinicalstudies.com
Journal for Clinical Studies 31
Clinical Trial Management
Beyond One-Time RECIST 1.1 Training: Knowledge Acquisition and Retention Response Evaluation Criteria in Solid Tumours version 1.1 (RECIST 1.1) provides standardised criteria for evaluating changes in tumour burden in solid-tumour clinical trials. Since their publication in 2009, the criteria have been incorporated into the objectives and endpoints of numerous oncology clinical trials.1,2 Subsequent clarifications from the RECIST Committee have addressed recurring questions involving lesion selection, lymph node assessment, non-target disease, new lesions and the determination of progressive disease.3 Standardisation does not, however, eliminate the possibility of incorrect interpretation and mistakes. A recent comparison of routine clinical interpretations with formal RECIST 1.1 assessments found substantial disagreement in progressive disease classification, most commonly related to target lesion measurements and progression of non-target lesions.4 Such findings reinforce the need for appropriate training across the clinical trial team. Clinical research associates (CRAs) do not replace investigators, radiologists or central readers and they do not independently perform a medical interpretation of imaging. Nevertheless, they regularly interact with RECIST-derived source data. Their responsibilities include source data review, verification of data entered into the electronic data capture (EDC) system, identification of inconsistencies between imaging reports and case report forms, follow-up of missing assessments and escalation of potential discrepancies to medical or imaging experts.5 These activities require not simply familiarity with RECIST terminology – they require a practical understanding of how the criteria are applied, or in other words – practical skills rather than theoretical knowledge.
Several educational resources have been developed to improve the application of RECIST 1.1 across clinical trial roles.6–8 These include training modules for site personnel, role-specific instruction for clinical data managers and institutional toolkits recommending ongoing training with knowledge assessment. Published evaluations remain relatively limited. Andoh and colleagues evaluated educational and feedback interventions intended to improve radiologists’ application of RECIST and found that the combination of distributed educational materials and structured feedback reduced the frequency of errors in radiology reports.9 However, there remains little published evidence on longitudinal, role-specific RECIST training for CRAs that combines baseline assessment, immediate evaluation, facilitated remediation and subsequent assessment of knowledge retention. Conventional training is often delivered as a single event and measured only by completion. Completion confirms that the material was accessed, but not that it was understood or retained. To address this limitation, we developed a structured RECIST 1.1 training programme combining an interactive tutorial, baseline and post-training knowledge assessments, targeted remediation and a follow-up assessment approximately three months later. This article describes the programme and summarises objective and participantreported outcomes collected between March 2021 and June 2026. Preliminary findings from an earlier analysis of this training programme were presented at the 2025 ASCO Annual Meeting.10 The present article reports an expanded dataset through 24 June 2026 and includes additional longitudinal, year-specific and participantexperience analyses. Designing Training Around the CRA Role The programme, RECIST 1.1 Practice Essentials, was designed for CRAs and other clinical research professionals involved in reviewing oncology trial data. It was not intended to qualify participants to
Figure 1. Structure of RECIST 1.1 training programme 32 Journal for Clinical Studies
Volume 18 Issue 2
Clinical Trial Management perform radiological reads or independently determine tumour response. The content focuses on practical concepts that CRAs are likely to encounter: selection and measurement of target lesions, evaluation of pathological lymph nodes, assessment of non-target lesions, calculation of changes in the sum of diameters, use of baseline and nadir measurements, identification of new lesions and determination of response to treatment and progressive disease. The tutorial content and assessment questions were reviewed by several internal and external medical oncologists with experience in applying RECIST 1.1 in clinical trials. Their review focused on clinical accuracy, consistency with RECIST 1.1 principles, relevance to situations encountered by CRAs and clarity of the explanations and response options. Feedback from these reviews was incorporated into the training materials before implementation and during subsequent updates to the programme. The programme has five components (Figure 1). First, participants are offered a pre-tutorial quiz without a passing score. This ungraded assessment establishes a baseline against which learning progress can be evaluated by comparing the results with those of the final post-tutorial quiz. Second, an interactive tutorial explains core RECIST 1.1 concepts through illustrative examples derived from clinical trial practice. The training repeatedly reinforces the boundary between recognising a potential inconsistency and making a medical or radiological determination. Third, participants complete a multiple-choice post-tutorial quiz. An 80% passing threshold is applied. The assessment measures immediate knowledge acquisition and allows recurring misunderstandings to be identified across trainee cohorts. Fourth, participants who do not initially achieve the passing score attend a facilitated question-and-answer session with a medical expert. The relevant questions and the rationale for the correct answers are discussed before reassessment. An unsuccessful first attempt is therefore treated as an opportunity for remediation rather than simply recorded as a training deficiency.
revisions to wording and presentation were introduced over time, but the core RECIST 1.1 concepts assessed remained consistent throughout the evaluation period. The primary analysis compared paired pre-tutorial and initial post-tutorial scores. A longitudinal complete-record analysis included records with scores available at all three time points: pretutorial, post-tutorial, and follow-up. Mean changes were evaluated using paired tests and are presented with 95% confidence intervals. Because quiz scores were discrete and bounded between 0% and 100%, two-sided Wilcoxon signed-rank tests were performed as sensitivity analyses for all paired comparisons. Year-specific results were summarised descriptively because cohort composition, previous oncology experience and follow-up availability varied over time. Participant-experience data were collected using a five-item survey with response options ranging from strongly disagree to strongly agree. ‘Agree’ and ‘strongly agree,’ responses were combined as favourable responses, while ‘disagree’ and ‘strongly disagree,’ responses were combined as unfavourable responses. Free-text comments were reviewed to identify recurring themes. Immediate Knowledge Acquisition A total of 382 paired pre- and post-tutorial assessment records were available for analysis. The mean pre-tutorial score was 55.3%, compared with 76.3% after the tutorial completion. The mean improvement was 21.0 percentage points (95% confidence interval, 18.6 to 23.4; p<0.001) (Figure 2). The result was confirmed in the Wilcoxon signed-rank sensitivity analysis (p<0.001). Performance improved in 76.7% of paired records, remained unchanged in 11.3% and decreased in 12.0%. The median change was 20 percentage points. All participants who initially scored below the 80% passing threshold completed facilitated remediation and reassessment, after which each achieved the required score. The magnitude of improvement is operationally meaningful. At baseline, participants answered only slightly more than half of the assessed questions correctly on average. Following the tutorial, average performance increased by approximately two correct answers on a ten-question assessment. The baseline quiz is therefore more than an introductory exercise: it provides the comparator needed to demonstrate the learning attributable to the programme.
Finally, participants are invited to complete a follow-up quiz approximately three months after the tutorial. This assessment evaluates the extent to which the knowledge demonstrated immediately after training is retained after participants return to routine work. Methods Training records available as of 24 June 2026 were reviewed. Quiz scores were expressed as percentages. Analyses were restricted to records containing interpretable numeric scores ranging from 0% to 100%. Missing, non-interpretable and clearly invalid entries were excluded. The pre-tutorial, post-tutorial and follow-up assessments each comprised ten equally weighted multiple-choice questions covering the core concepts addressed in the tutorial. Partially overlapping question sets were used at the three assessment time points. Participants completed the assessments independently. Correct answers and explanations were reviewed during remediation when required; however, participants did not otherwise have access to the answer key before completing the follow-up assessment. Minor www.journalforclinicalstudies.com
Figure 2. Mean assessment scores before and after the tutorial.
Knowledge Retention A total of 116 assessment records contained results for all three time points. In this cohort, mean scores were 56.2% before the tutorial, 78.2% post-tutorial and 77.8% at follow-up (Figure 3). The improvement from baseline to the post-tutorial assessment was statistically significant (p<0.001), as was the difference between baseline and follow-up (p<0.001). These findings were confirmed Journal for Clinical Studies 33
Clinical Trial Management in Wilcoxon signed-rank sensitivity analyses (both p<0.001). In contrast, there was no meaningful decline between the posttutorial and follow-up assessments: the mean difference was −0.4 percentage points (95% confidence interval, −5.0 to 4.2; p=0.853). The corresponding Wilcoxon signed-rank test was also non-significant (p=0.82). In 61.2% of complete records, the follow-up score was the same as or higher than the immediate post-training score. These results suggest that the knowledge gain was generally maintained approximately three months after training. This is particularly relevant because participants may not immediately encounter every RECIST scenario in their assigned trials. A delayed assessment provides a more realistic indication of whether knowledge remains available for later application than an immediate posttraining quiz alone. Figure 3. Mean assessment scores at baseline, post-tutorial and follow-up.
Results Across Training Years Improvement from baseline to post-tutorial assessment was observed in every annual cohort from 2021 through 2026 (Figure 4). Baseline scores were lowest during the programme’s initial year and generally higher in later cohorts, while mean post-tutorial scores became comparatively stable. These findings may reflect both improvements to the course and differences in composition of annual trainee groups. Year-to-year comparisons should therefore remain descriptive. They do not establish that changes were caused solely by revisions to the training. Nevertheless, the recurring pre-to-post improvement across successive cohorts indicates that the overall result was not limited to a single group or implementation period. Using Assessment Errors to Improve the Programme Assessment data can identify not only whether learning occurred, but also which concepts require greater emphasis. An earlier analysis of baseline errors found that 44.2% involved defining or measuring target lesions, 32.2% involved determining progressive disease, 21.2% involved non-target lesions and 2.4% involved methods of assessment. These findings were used to restructure the tutorial and expand explanations and examples in the most difficult areas. The pattern is consistent with the practical complexity of RECIST 1.1. Target-lesion selection and measurement require several linked rules, while progressive-disease determination may require consideration of nadir, absolute and relative change, non-target disease and new lesions.4,11,12 This illustrates an important advantage of a multi-stage training model: aggregated assessment findings create a feedback Statement
Responses, n
Favourable
Uncertain
Unfavourable
Well engaged during the session
313
88.2%
9.3%
2.6%
Appropriately challenged by the presentation and quiz
312
86.5%
8.3%
5.1%
Comfortable with pace and duration
313
85.9%
7.7%
6.4%
Tutorial was easy to understand
313
79.9%
11.8%
8.3%
Able to apply the learning immediately
312
76.6%
16.7%
6.7%
Table 1. Participant survey results 34 Journal for Clinical Studies
Figure 4. Pre- and post-tutorial assessment scores by training year, 2021–2026.
loop. The course can evolve in response to documented learning needs rather than relying only on faculty impressions. Participant Experience and Perceived Practical Value A total of 313 survey responses were collected between 19 March 2021 and 24 June 2026, representing 296 individuals; 17 individuals submitted responses following two training occurrences. Results are summarised in Table 1. Engagement received the highest rating: 88.2% of responses agreed or strongly agreed that participants were well engaged during the session. A further 86.5% reported that the presentation and quiz questions were appropriately challenging, and 85.9% were comfortable with the pace and duration. The tutorial was considered easy to understand in 79.9% of responses, while 76.6% indicated that the participant expected to apply the acquired knowledge immediately. The comparatively lower rating for immediate applicability is understandable in a heterogeneous population. Some participants may not have been assigned to an oncology trial at the time of training, and the opportunity to use individual RECIST concepts varies by role and project. Seventy-nine respondents provided free-text comments. Positive comments commonly described the training as useful, clear and relevant to clinical trial work. Suggestions included adding more practical examples, providing reference materials or a written script after the training, allowing more time to discuss difficult questions and dividing complex material into shorter learning segments. Some respondents noted that the course was more challenging for participants without a medical background or for those completing it in a non-native language. These comments generated practical improvement opportunities. They supported greater use of applied scenarios, clearer explanations of quiz answers and additional reinforcement of the distinction between identifying a potential inconsistency and independently making a response assessment. Volume 18 Issue 2
Clinical Trial Management The findings support moving beyond one-time, completionbased training toward a model that measures knowledge acquisition, addresses misunderstandings, evaluates retention and uses evidence to improve the curriculum. Such an approach may strengthen the ability of CRAs to recognise inconsistencies, escalate potential issues and support the quality of oncology clinical trial data. REFERENCES Practical Lessons for CROs Several principles from this programme may be transferable to other areas of clinical trial training. Training should be role specific. CRAs need enough knowledge to review RECIST-derived data critically and escalate discrepancies, but they do not require the same curriculum as investigators, radiologists or central readers. Defining this boundary improves both relevance and compliance with assigned responsibilities. Training completion should not be treated as evidence of competence. A completion record confirms access to the material, whereas a knowledge assessment provides evidence of understanding. A baseline assessment also allows the magnitude of learning to be measured rather than inferred. Remediation should be built into the training pathway. When participants do not meet the passing threshold, facilitated discussion can distinguish between a terminology problem, a calculation error, misunderstanding of a clinical scenario or confusion between similar concepts. This is more educationally useful than repeated testing without explanation. Knowledge retention should be evaluated. Immediate post-training performance reflects short-term recall. A delayed assessment helps determine whether learning remains available for later use and can identify the need for refresher training. Finally, further course development should be based on the training data and results. Recurring errors and participant feedback can be translated into revised examples, clearer explanations and targeted supplementary materials. This creates a continuously improving programme rather than a static annual requirement. We adapted the same structure to other oncology response criteria, including iRECIST, Lugano and RANO, while retaining the core elements of baseline assessment, applied learning, remediation and follow-up evaluation. Limitations This evaluation was observational and did not include an untrained control group. Quiz scores are indirect measures of professional performance and were not linked to trial-level quality indicators such as query rates, protocol deviations, data corrections or concordance with central imaging review. Follow-up results were available for fewer records than pre- and post-tutorial results, creating the possibility of selection bias. Future analyses could link training participation with operational quality indicators, compare outcomes by professional role and previous oncology experience and assess the effect of targeted refresher modules. Conclusion Consistent use of RECIST 1.1 depends not only on the expertise of investigators and imaging specialists, but also on the knowledge of clinical research professionals who review and manage oncology trial data. A structured training model combining a practical tutorial, baseline and post-training assessments, facilitated remediation and a three-month follow-up produced a substantial improvement in RECIST 1.1 knowledge. Among records with complete longitudinal data, the improvement was maintained at follow-up. Participant feedback also showed high engagement, appropriate challenge and favourable perceptions of the course’s clarity and relevance. www.journalforclinicalstudies.com
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2. 3.
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Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). European Journal of Cancer. 2009;45(2):228–247. doi:10.1016/j.ejca.2008.10.026. US Food and Drug Administration. Clinical Trial Imaging Endpoint Process Standards: Guidance for Industry. April 2018. Schwartz LH, Litière S, de Vries E, et al. RECIST 1.1—Update and clarification: from the RECIST Committee. European Journal of Cancer. 2016;62:132–137. doi:10.1016/j.ejca.2016.03.081. Siegel MJ, Ippolito JE, Wahl RL, Siegel BA. Discrepant assessments of progressive disease in clinical trials between routine clinical reads and formal RECIST 1.1 interpretations. Radiology: Imaging Cancer. 2023;5(5):e230001. doi:10.1148/rycan.230001. Gouel P, Callonnec F, Levêque É, et al. Evaluation of the capability and reproducibility of RECIST 1.1 measurements by technologists in breast cancer follow-up: a pilot study. Scientific Reports. 2023;13:9148. doi:10.1038/s41598-023-36315-w NRG Oncology. RECIST 1.1 Criteria Toolkit: A Resource for Success in Clinical Trials with Solid Tumor Evaluation Requirements. Available at: https://www.nrgoncology.org/Home/News/Post/recist-11-criteriatoolkit-a-resource-for-success-in-clinical-trials-with-solid-tumorevaluation-requirements/ Accessed 14 July 2026 Society for Clinical Research Sites. RECIST Training Modules: Oncology Training Programs for Site Staff Conducting Clinical Research [Internet]. Training modules developed by the Clinical Imaging Team at Merck Research Laboratories. Available from: https://myscrs.org/learningcampus/irecist-modules/. Accessed 14 July 2026 Stephenson B. RECIST—A Practical Guide for Data Managers [Internet]. Association for Clinical Data Management; 14 May 2019. Available from: https://acdmglobal.org/courses/recist-a-practical-guide-for-datamanagers/. Accessed 14 July 2026 Andoh H, McNulty NJ, Lewis PJ. Improving accuracy in reporting CT scans of oncology patients: assessing the effect of education and feedback interventions on the application of the Response Evaluation Criteria in Solid Tumors criteria. Academic Radiology. 2013;20(3):351–357. doi:10.1016/j.acra.2012.12.002 Kosov M, Bowman N, Maximovich A, Field KM, Murphy F, Semeniuk N. Importance of RECIST 1.1 training program for improving performance of contract research organization staff in solid tumor clinical trials. J Clin Oncol. 2025;43(16_suppl):e21018. doi:10.1200/JCO.2025.43.16_suppl.e21018. Morse B, Jeong D, Ihnat G, Silva AC. Pearls and pitfalls of response evaluation criteria in solid tumors (RECIST) v1.1 non-target lesion assessment. Abdom Radiol (NY). 2019 Feb;44(2):766-774. doi: 10.1007/ s00261-018-1752-4. PMID: 30196362; PMCID: PMC7771310. Teslenko I, Belotserkovskiy M, Kumar A. Common pitfalls of RECIST 1.1 application in clinical trials. Journal for Clinical Studies. 2015;7 (3):26-32.
Maxim Kosov Maxim Kosov, MD, PhD, is Senior Medical Advisor at PSI CRO AG (USA). Trained in paediatrics, anesthesiology and intensive care, he has more than 30 years of combined medical and research experience, including over 20 years in the clinical trials industry. His expertise spans a broad range of therapeutic areas, and he has authored or coauthored more than 60 scientific publications. Email: maxim.kosov@psi-cro.com
Journal for Clinical Studies 35
Technology
Trust, Not Accuracy: Why Pharmacovigilance Teams Still Double-Check AI Output They Know to be Valid Despite strong and improving accuracy scores, AI-powered adverse event processing is often still being routinely double-checked by reviewers, well beyond the expectations of quality control. Could the answer involve a structured way of inferring appropriate reliance from operational evidence, such as through a trust coefficient reflecting the specific context of AI use? AI output being correct doesn’t automatically equate to it being trusted. Some pharma companies that have been drawing on AIpowered adverse event processing for a while now are reporting a frustrating disconnect between the demonstrated accuracy scores and the way PV teams are responding to them. Too often, reviewers are found to be reopening cases already signed off by the system, when there is no logical reason to do so. Audits confirm that this is happening, even though there has been nothing formal from a quality control perspective to require it. So, what’s going on and what can be done about it - to ensure companies derive the expected benefits from the technology’s efficiency? Accuracy isn’t the whole story. To a large extent, reviewer behaviour hasn’t caught up with the growing operational evidence around AI use in PV: although platforms are shown to be reliable, user behaviour suggests they can’t quite make the leap to leaning on it. There is a perception issue linked to the new technology’s approach, too. Anyone accustomed to using rules-based systems will have come to expect the same input to consistently yield the same output. AI challenges that expectation by design. Yet, consistency has been set as the form and a model is seen to only get things ‘right,’ 19 times out of 20, it looks unreliable. Bolting on more rules to fix this just makes matters worse. This is because each new constraint shrinks the pool of cases the system can handle safely on its own. There needs to be a shift in perception, then, to align with what AI does and how it works. In other words, trust needs to be redefined or reframed, to ensure appropriate user confidence in AI-driven output. Consider what often happens with AI-enabled case narrative writing, for example. Here, most of what reviewers go on to change (when they decide to check and edit AI’s output) comes down to house style rather than clinical substance or factual accuracy. If each stylistic tweak is recorded as a correction, the numbers make it look like the model is underperforming. This distorts the real performance picture and feeds people’s instinct to over-check everything. Building a Structured Measure of Reliance Retaining indiscriminate quality control over an AI-driven process defeats the point of process transformation, by restricting the realised benefits – such as freeing up the precious time of safety scientists. That underlying problem is one of trust, manifested through continued old behaviour. However confident teams claim to be in 36 Journal for Clinical Studies
AI-assisted systems, this counts for little if they keep checking every single thing a system produces when there is demonstrably no need to. A formal trust coefficient is one way to gauge this impartially. Instead of scoring AI performance in isolation, a trust coefficient could combine several operational inputs into one structured view: model performance, reviewer behaviour, the maturity of validation and governance, task risk and how ready the organisation is to lean on AI output more broadly. The idea isn’t to produce a fixed calculation, but rather a repeatable answer around how much independent checking a validated system genuinely still needs in the given context. As the evidence for reliable performance grows, the measures would allow oversight to ease off, without any compromise to governance or risk. The measure would stand apart from the operational KPIs already in use (which track things like performance, efficiency or compliance), quantifying how much reliance is justified in a validated AI system under a defined set of conditions. Design conversations already treat trust as a core principle for AI platforms, but nobody has yet turned it into an operational number that regulators, vendors and pharmaceutical companies could apply consistently. That, more than raw accuracy, is where the real work in AI operationalisation needs to happen over the next couple of years. Greater Autonomy for AI, No Change in Accountability Agentic AI has a further bearing on any trust coefficient. Where earlier AI tools took care of single tasks in isolation, an agentic system can now run through a whole chain of preparatory work on its own, gathering data, checking fields, pulling together evidence, all before a human reviewer ever opens the case. This paves the way for specialists’ attention to be concentrated on interpretation, judgement and the cases that are genuinely ambiguous enough to need a scientist’s expertise. The important point to highlight, amid that shift, is the difference between a reviewer checking every single step taken by a system and a reviewer stepping in only when risk or clinical significance actually warrants it. This doesn’t alter ownership and accountability, however. Some organisations already have one AI model reviewing another’s work before either output reaches a human being and none of that changes who is ultimately accountable. Agentic AI may change where the groundwork is carried out, but accountability for patient safety is not handed off with it; that remains the responsibility of qualified PV professionals. Redesigning the Workflow Before Automating It There is a wider point to take on board when building evidence-based reliance, for any organisation bringing AI into pharmacovigilance. And that is to redesign the process first and only automate once what’s left is worth automating. This is about ensuring that advanced automation solutions build on a set-up that has already been simplified, rather than try to retrofit Volume 18 Issue 2
Technology principles for AI use across the medicines lifecycle, spanning earlystage evidence generation, clinical development and post-marketing safety monitoring.1 The emphasis throughout is on validating systems in line with how they will actually be used, keeping data governance robust, watching performance on an ongoing basis and being open about where a system falls short. CIOMS took a comparable line in its own report on AI in pharmacovigilance that December, favouring governance built to flex as AI capability develops rather than rules pinned to today’s specific technologies.2 As medicines become more complex and adverse event volumes continue to climb, the case for actively transforming ways of working in PV grows even stronger. VigiBase, the WHO’s global repository of individual case safety reports, had logged upwards of 40 million entries by the close of 2024, roughly 70% of them added within the ten years prior.3 That aside, volume itself is not the obstacle now. The outstanding challenge is determining when a validated system can be used with lighter checks. Devising the optimal method for calculating a trust coefficient has yet to happen, but the justification for moving towards such a measure is strong, certainly for companies with substantial portfolios and big ambitions, which may be handling several hundred thousand safety reports a year. REFERENCES 1.
2.
3.
European Medicines Agency and U.S. Food and Drug Administration. EMA and FDA set common principles for AI in medicine development. https:// www.ema.europa.eu/en/news/ema-fda-set-common-principles-aimedicine-development-0, visited on 21 Jul 2026. Council for International Organizations of Medical Sciences (CIOMS). Artificial Intelligence in Pharmacovigilance, CIOMS Working Group XIV report, Geneva. https://cioms.ch/working_groups/working-group-xivartificial-intelligence-in-pharmacovigilance/, visited on 21 Jul 2026. Brand, J.S., Gauffin, O., Sartori, D., Fusaroli, M., Sköld, H., Bergvall, T., Sandberg, L., Wallberg, M., Hjelmström, P., Norén, G.N. VigiBase: Resource Profile Update with a Summary of Global Patterns and Trends in Adverse Event Reports for Medicines and Vaccines. Drug Safety, 49(6), 613–629 (2026).
Jason Bryant
AI-powered capabilities to a process built for manual work. The ideal is to streamline hand-offs and touchpoints, but also leave room for future adaptation on the basis that AI capability itself is still evolving. Regulatory Thinking Reflects the Need for Risk-Appropriate Intervention Regulatory guidance is starting to reflect the same logic around AI risk and governance. Several regulators and international expert bodies have published new associated guidance this year, all pointing the same way: AI oversight needs to scale with risk and needs to be designed into a system from the start rather than bolted on once it is already running. In January 2026 the European Medicines Agency and the US Food and Drug Administration published a joint set of 10 guiding www.journalforclinicalstudies.com
Jason Bryant is General Manager of AI Platforms at ArisGlobal, bringing an AIdriven, human-centric approach to product design across pharmacovigilance. Trained originally as a data science actuary, his career has moved between fintech and health-tech, including a spell running a digital incubator at AstraZeneca before he turned his focus to agentic AI in drug safety. He is based in London, UK. Email: jbryant@arisglobal.com.
Samuel Wallis Samuel Wallis is Head of Case Processing at Bristol Myers Squibb, overseeing pharmacovigilance operations across one of the largest ICSR volumes in the industry. His transformation work at BMS has included moving the company’s global safety database onto the cloud and rolling out AI-enabled case processing across the function. He is based in London, UK.
Journal for Clinical Studies 37
Technology
AI in Clinical Trials: From Pilot to Production Clinical trials have never been more important or expensive. A single new drug now takes over a decade and billions of dollars to bring to market and trials account for much of that cost and time. They are also growing more complex: more data, more sites, more decentralised elements and rising regulatory expectations. This is precisely why AI matters now. Applied well, it can compress the slowest, most repetitive parts of the process, screening records, cleaning data, interpreting protocols, drafting documentation and return skilled people to the work that needs them. The difficulty lies in moving from a promising pilot to something that works safely, inside a regulated environment. AI has already reshaped early discovery, protein structure prediction, target identification, molecule design, virtual screening but the dominant cost and uncertainty lie downstream, in demonstrating that a candidate works in humans.11 Clinical research associates (CRAs) do not replace investigators, radiologists or central readers and they do not independently perform a medical interpretation of imaging. Nevertheless, they regularly interact with RECIST-derived source data. Their responsibilities include source data review, verification of data entered into the electronic data capture (EDC) system, identification of inconsistencies between imaging reports and case report forms, follow-up of missing assessments and escalation of potential discrepancies to medical or imaging experts.5 These activities require not simply familiarity with RECIST terminology – they require a practical understanding of how the criteria are applied, or in other words – practical skills rather than theoretical knowledge. Two recent industry reports examine the same shift from different angles.1,2 One asks what a modern, AI-enabled clinical trial requires in terms of regulation and system design. The other asks a more pointed question: why do so many AI projects succeed in a pilot, then stall before reaching everyday use? Read together, they reach the same conclusion. The obstacle is rarely the AI itself. It is the infrastructure around it, the rules, the data and the way people work which is not yet built to carry AI into regulated, inspection-ready use. Clinical research is a very data-disciplined field and it has been slow to give its investigators and coordinators true support again, because of the infrastructure the technology must operate within,
Figure 1. Three structural shifts reshaping clinical trials 38 Journal for Clinical Studies
Figure 2. Compliance by design: the architectural building blocks
not the technology itself. This paper sets out what that infrastructure needs to be, why partial fixes fail and what sponsors, technology providers and sites should each do differently. It pays particular attention to the site, the principal investigators (PIs) and study coordinators (SCs) because that is where the gap between what AI can do and what teams can actually use is widest. The Regulatory Direction Is Already Set Trials have grown more complex while the pressure to run them faster and more transparently keeps rising. Remote visits, wearables and electronic consent have widened access since the pandemic; the Apple Heart Study enrolled more than 400,000 people entirely remotely.3 AI now supports protocol design, patient matching and monitoring and interoperable data networks have made very large trials possible, the ADAPTABLE trial randomised over 15,000 patients through a network linked to electronic health records.4 Yet most trials still run on separate, disconnected systems, one each for trial management, data capture, outcomes, consent and safety. The same information is entered several times, audit trails fail to align, safety signals surface late and inspection risk rises. The regulations demand accountability and traceability; the systems quietly work against both. Regulators have been explicit about where this is heading. The FDA expects remote data tools to be fit for purpose and to transmit data securely.7 The EMA has stated plainly that ‘black-box,’ models, those whose reasoning cannot be explained, are unlikely to be acceptable for high-stakes decisions without human oversight [8]. ICH E6(R3) keeps the sponsor accountable for vendor-run systems, including their validation and audit trails, even when the work is outsourced,9 and the risk-based quality principles of ICH E8(R1) and Q9(R1) push oversight toward the issues that matter most. Digital endpoints are welcome, but only with rigorous, pre-planned validation: the EMA has, for example, accepted a wearable-based walking measure as a primary endpoint in a muscular dystrophy trial.5 Europe now adds a further layer. The EU AI Act, the first broad AI regulation, operates alongside GDPR, with its obligations phasing in through 2026 and 2027. AI that acts as part of a medical device, or that materially shapes decisions about a person's health, is classed as ‘high-risk,’ and must meet requirements for risk management, transparency, human oversight and ongoing monitoring. The Act also obliges organisations to ensure staff have adequate AI literacy, Volume 18 Issue 2
Technology turning a desirable skill into a legal duty. Notably, its risk-based logic mirrors a distinction this paper returns to below: tools that handle patient data carry a far heavier burden than tools that merely help coordinate the work. The conclusion is straightforward. Compliance must be designed into a trial from the outset, access controls, complete audit trails, clear governance for AI and standard data connections rather than retrofitted once a platform is already in use.
generating new ideas, but giving people documented grounds to act on judgments they already trusted. It is worth asking, too, whether the manual workflow AI would replace deserves the trust it already receives. In one multisite cancer study, copying data from the hospital record to the trial database by hand produced errors in 5.8 percent of fields, which an AI-assisted tool with human oversight cut to 1.2 percent,2 yet a 2025 survey found 72 percent of sites still transfer data by hand.
Why Good System Design Is Not Enough on Its Own A second body of research interviews with fifteen leaders across clinical development, operations, quality and IT security, together with findings from a 2026 industry conference2 asked why AI stalls even when the system design is sound. The answer is that three obstacles must be addressed at once. Solve one and neglect the others and the project stops for reasons no one anticipated. Trust means two different things. To operations staff, trust means audit trails and provenance, being able to state exactly who changed what, when and why. To scientific and medical leads, it means something quite different: a model validated for their disease, whose strengths and limits a clinician can judge without needing an AI specialist. A system built only for audit trails will not satisfy the scientists and one built only for predictive accuracy will not survive an inspection. Both forms of trust must be earned simultaneously. Scattered data defeats AI before governance even arises. Trial data typically lives in dozens of systems, each with its own labels and permissions. One large company consolidated more than 60 terabytes from over 50 sources to build an AI assistant. It reached roughly 95 percent accuracy on plain-language questions,2 but only after the team collapsed hundreds of fragmented tables into a small, consistent set with agreed definitions. Before that clean-up, even a capable model produced confident wrong answers, not because the AI was faulty but because the data beneath it was ambiguous. AI scales only if it fits how people already work. Small teams often one technical lead paired with one clinical operations lead moved faster than large, centralised AI programmes. Broad ‘chat with everything,’ tools disappointed, while narrow, high-value uses succeeded. Adoption spread from the bottom up: a tool became standard only after early users demonstrated its value and brought colleagues along. Mandates issued from above, before the tool had proven itself, tended to stall. One finding stands out. In a tool that reviewed draft protocols for design risks, more than half its suggestions told teams nothing they did not already suspect. Their value lay elsewhere: they turned a shared hunch into a written, source-linked rationale that could withstand an inspection. AI's most useful role here was not
Figure 3. Operational versus scientific trust www.journalforclinicalstudies.com
Figure 4. Data-entry error rates: manual versus AI-assisted transfer
Two Kinds of AI Tool for Site Staff One practical rule runs through both reports: the AI projects that succeed layer on top of existing systems rather than replacing them, since sponsors will not and realistically cannot tear out a global trial infrastructure that works well enough. Against that backdrop, two kinds of tool are emerging: those that operate inside the regulated world of patient data and those that stay above it, helping people run and coordinate the trial. Knowing which is which matters, both for planning and for how a site evaluates a new tool. The first kind handles patient data directly. Some tools help a PI review outcome data, assembling summaries, flagging what needs attention and signaling when data is ready for sign-off. Others manage admin and compliance: patient communication, visit scheduling and consent management, including monitoring for updated consent forms. Some even ease staff turnover by guiding new coordinators through a study's steps in real time. Because all of these touch patient data, they operate fully inside the regulated environment ICH E6(R3), 21 CFR Part 11, audit trails, so the validation burden is high and they must be formally qualified within the site's quality system. The second kind never touches patient data. What it operates on is the trial's operational structure, the tasks, responsibilities, timelines and dependencies of running a study across many roles and sites, against a protocol that keeps changing. Today much of that lives in email, spreadsheets and people's heads, with no shared picture anyone can fully trust; a coordination tool makes that structure explicit and current. Because these tools stay above the patient-data line, they can be layered onto existing systems without entering the regulated data world. That also explains why they are adopted so readily: they fit how teams already work rather than asking them to replace validated systems and, echoing the bottom-up pattern in the SCOPE interviews,2 spread as users see value rather than by mandate. The boundary is not always perfectly clean. Coordinators inevitably ask questions connected to patient information even when the tool never operates on patient records. The distinction holds because the coordination layer acts on the trial's operational structure, who does what, by when and in what order, not on the Journal for Clinical Studies 39
Technology source data: it is the operational plan that is shared and kept current, not the clinical record. The two kinds are complementary rather than competing: a site might run a patient-data tool for PI oversight and a coordination tool for daily work at once, with no conflict. The distinction clarifies how to judge a tool patient-data tools on validation, audit trails and quality-system fit; coordination tools on workflow fit, adoption speed and friction removed. The risk lies in conflating the two – subjecting a coordination tool to the scrutiny meant for patient data, or assuming it needs none at all. Knowing what a tool does and which side of the patient-data line it falls on, is the starting point for any realistic plan.
Figure 5. Two kinds of AI tool at the clinical site
AI in Patient Recruitment: Where the Bottleneck Meets the Tool Recruitment is the single most common reason trials fall behind, and most of the weight falls on site staff, coordinators reading through dozens of records to find one eligible patient, checking dense criteria against scattered notes under time pressure. The realistic prize is not that AI recruits patients. It is that AI clears away the least clinical, most time-consuming part of the task, returning coordinator hours to the work that genuinely needs a person: talking with patients, building trust and taking consent. High-volume, rule-based searching is exactly what AI does well; the harder question is how a site gets there from where it stands today. From unregulated to regulated: a staged path. Moving from the unregulated side into the regulated one is demanding validation, audit trails, quality-system integration and lawful data access all take real effort but for anyone serious about AI in recruitment it is the journey that has to be made and it need not be a single leap. Much of recruitment today already operates in the unregulated engagement layer, where specialists work closely with sites to coach them past their recruitment barriers. AI fits naturally into that way of working, spotting patterns across sites, flagging when one falls behind its screening targets, preparing local-language materials faster supporting the people rather than replacing them. Introduced here first, it proves its value and builds the governance habits regulated use will later require. The unregulated side is not the destination; it is the on-ramp. Where a given application falls on that journey depends on the data it touches and the three main uses divide as follows. Finding eligible patients – regulated. The most mature use is AIassisted eligibility screening. The tool reads inclusion and exclusion criteria and matches them against health records, pathology reports and notes, cutting a chart review that took weeks down to a fraction 40 Journal for Clinical Studies
of the time. Searching records this way has also been shown to enroll more patients from under-represented groups than reliance on the usual referral patterns. But because these tools read identifiable patient data and feed directly into an eligibility decision, they belong firmly in the regulated world: GDPR and national health-data law govern the access, GCP governs the decision. The AI does not decide who is eligible it shortens the list so a clinician can judge the ones that matter, with the physician's review and the investigator's sign-off still required. Full validation and audit trails apply. Testing a protocol before it is locked – conditionally unregulated. A different use comes earlier, before any site opens. A model compares the draft eligibility criteria against de-identified records to estimate how many patients a given rule would exclude and how heavy the visit schedule would be, so a sponsor can correct a problem before the protocol is finalised. The condition attached to this category carries real weight and belongs up front: only genuinely anonymised data falls outside GDPR. Pseudonymised records where a key still exists that could re-identify individuals remain personal data and an application built on them stays inside the regulated environment however aggregate its outputs appear. Where that threshold is genuinely met, the analysis shapes design rather than any individual's care and falls outside the regulated world. Given that a substantial amendment costs $141,000 in Phase 2 and $535,000 in Phase 3,6 catching the problem early is well worth the effort. Recent work shows how far this can be pushed. EmulatRx, a multi-agent framework published in 2026, coordinates five specialised agents that refine protocols against real-world electronic health record data; in one emulated cohort it identified 33 additional eligible patients from clinical notes that structured data alone would have missed.12 Its authors are explicit that broader validation is required before deployment, capability demonstrated, deployment gated by validation. Reading identifiable notes to surface named candidates, it also falls inside the regulated environment. Communicating with patients - regulated and consent-sensitive. Automated outreach, chat-style pre-screening, visit reminders, follow-up on patient-reported outcomes and predicting who might drop out all extend the same idea to keeping patients in the trial, often a greater challenge than enrolling them. Because these interact directly with participants and their data, they occupy the most demanding part of the regulated world: consent, GDPR and where they influence a person's health decisions, potentially the high-risk provisions of the EU AI Act.10 One caution applies throughout: the evidence is strongest that AI makes screening faster, while whether it improves final enrolment or retention is less well established and sites should keep the two claims apart when evaluating a tool. Conclusion Neither the regulations nor the technology is what holds AI back in clinical trials. What is missing is the quieter work of building trust, standardising the data and reshaping how people work so that AI becomes a governed member of the team rather than a bolt-on experiment. The teams making real progress share one habit: they treat design, data and workflow as a single problem, not three to be solved in sequence. That places a different demand on each group. Sponsors should fold AI oversight into the quality system they already operate rather than run it as a separate track; technology providers should design compliance in from the start and favour explainable models over opaque ones; sites should treat digital literacy as a core competency; and regulators must keep advancing shared guidance while using pilots and sandboxes to test new tools under controlled conditions. The same thinking extends to areas set aside here, from Volume 18 Issue 2
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safety monitoring to medical writing. The organisations that take on trust, data and workflow together, rather than waiting for one to be perfect before beginning the next, will move AI from a conference demonstration to real, inspection-ready use and, for the patients these trials exist to serve, close the gap between what is possible and what reaches the clinic. This paper synthesizes two review articles published in DIA Global Forum, drawing on the primary sources they cite. Where a figure is reported by those reviews rather than independently verified against the primary study, the DIA source is indicated.
REFERENCES 1. 2.
3.
4. 5.
6.
Rajasimha HK, Rodriguez-Chavez IR. Building AI-Ready Clinical Trials Through Unified, Compliant Systems. DIA Global Forum, 2026. Pirhaji L. Operationalizing GenAI in Clinical Development: The Interlocking Barriers to Regulated AI Deployment. DIA Global Forum, 2026. (Source of the EHR-to-EDC error-rate comparison, the 2025 eSource survey, and the SCOPE 2026 enterprise deployment data cited here.) Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381(20):1909– 1917. Jones WS, Mulder H, Wruck LM, et al. Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease. N Engl J Med. 2021;384(21):1981–1990. European Medicines Agency. Qualification Opinion for Stride Velocity 95th Centile (SV95C) in Ambulatory Duchenne Muscular Dystrophy Studies. EMA, 2023. Smith Z, DiMasi JA, Getz KA. The Impact of Protocol Amendments on
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7. 8. 9.
10. 11. 12.
Clinical Trial Performance and Cost. Ther Innov Regul Sci. 2016;50(4). US Food and Drug Administration. Digital Health Technologies for Remote Data Acquisition in Clinical Investigations. Guidance for Industry, 2023. European Medicines Agency. Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle. EMA, 2024. International Council for Harmonisation. ICH E6(R3) Good Clinical Practice, 2025; ICH E8(R1) General Considerations for Clinical Studies; ICH Q9(R1) Quality Risk Management. Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union, 2024. Can AI Make Better Drugs? Not on Wall Street's Timeline. The Wall Street Journal, 12 July 2026. Li H, Pan W, Zang C, Rajendran S, Wang F. Empowering clinical trial design with agentic intelligence and real-world data. Nat Commun. 2026. doi:10.1038/s41467-026-74501-2.
Stefan Åström Stefan Åström holds a PhD in molecular biology and began his career at Pfizer and AstraZeneca. A serial CRO entrepreneur, he founded Hylae Clinical Research, Astrom Research International and AdDoc (an early EDC company). He is now an independent scientific advisor to biotech and CRO companies and serves on the Editorial Advisory Board of the Journal for Clinical Studies.
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Logistics and Supply Chain
The Net Treatment Benefit and Project Optimus Since the FDA released their Project Optimus guidance for dose optimisation, with the idea to move away from Maximum Tolerated Dose (MTD) designs, many sponsors have very practical questions about how to demonstrate that one dose provides a better overall balance of efficacy, safety and tolerability for patients. Randomised dose optimisation studies are emerging as an important approach. Net Treatment Benefit (NTB), derived from Generalised Pairwise Comparisons, provides a way to bring these different outcomes together into a single, clinically meaningful overall assessment of benefit and risk. What is FDA’s Project Optimus? The FDA’s Project Optimus is a key step toward improving dose optimisation in oncology. Originally announced in 2021, the guidance aims to move away from traditional dose escalation and maximum tolerated dose (MTD) designs toward approaches that identify the dose that best balances efficacy, safety and tolerability.1 The traditional approach was developed largely around cytotoxic chemotherapy, where increasing the dose often meant increasing both efficacy and toxicity. The dose was therefore increased until safety became a concern, with the MTD then carried forward into later development. This approach is no longer appropriate for many modern oncology treatments. Higher exposure does not necessarily mean greater efficacy, being able to tolerate a treatment for longer may be more important than a slight boost to efficacy. Therefore, a dose selected at the limit of efficacy and tolerability may expose patients to additional toxicity without providing meaningful additional benefit.1,2 Ultimately, the goal is for patients to benefit from treatment doses that maintain strong efficacy while avoiding toxicity that is disproportionate or unacceptable, rather than simply pushing the limit of tolerability.
Instead of analysing efficacy, toxicity and other outcomes separately, all patients in one treatment group are compared with all patients in the other treatment arm across a predefined hierarchy of outcomes. For example, efficacy might be given the highest priority, followed by serious toxicity, then other clinically relevant measures of tolerability. For each pair of patients, the analysis compares outcomes sequentially according to the hierarchy and determines for each outcome which patient has the more favorable result at. Across all patient pairs, these pairwise classifications are then summarised as the Net Treatment Benefit: the probability that a randomly selected patient receiving one treatment has a better outcome according to the hierarchy than a randomly selected patient receiving the comparator, minus the probability of the opposite situation.4,5 This creates a single measure of benefit-risk while retaining the information contained in the individual outcomes.
What Approaches Exist to Answer Project Optimus? Project Optimus does not prescribe one universal method for identifying the optimal dose. Their guidance recommends randomised, parallel dose-response trials as an approach for comparing multiple dosages when appropriate.3 However, randomised dose optimisation is still not a standard approach for every oncology development programme. Sponsors must decide which doses to evaluate, how many patients are needed, which outcomes should drive the decision and how the resulting efficacy and safety data should be analysed. This is where Net Treatment Benefit can provide a practical approach.
How Can Net Treatment Benefit Support Dose Optimisation for FDA’s Project Optimus? Imagine a randomised dose optimisation study comparing 2 doses of an oncology treatment. The highest dose may produce a slightly higher response rate, but also more grade 3 or 4 toxicities and more treatment discontinuations. The lower dose may produce a similar response rate, while substantially improving tolerability. Looking at each outcome independently can make the final decision difficult. Net Treatment Benefit allows efficacy, toxicity and tolerability outcomes to be considered together. A possible hierarchy could prioritise Overall Survival or another efficacy outcome first, followed by clinically important toxicities and then treatment discontinuation or patient-reported tolerability. The hierarchy should be defined prospectively and should reflect the clinical objectives of the trial. NTB does not need to replace individual analyses on each outcome. Instead, it can provide an additional integrated analysis that helps to interpret them together, and to make a decision. It can also make the result easier to communicate. Rather than presenting a series of efficacy and safety tables and asking decision-makers to determine how they should be balanced, NTB provides a quantitative estimate of the overall difference between doses. For example, an NTB of 10% means that the probability that a randomly selected patient receiving one dose has a more favorable outcome than a randomly selected patient receiving the comparator exceeds the probability of the reverse outcome by 10 percentage points, according to the prespecified outcome hierarchy. In most cases this does not make a dose selection decision automatic and clinical judgement remains essential, but NTB provides a statistically rigorous framework for making the benefit-risk trade-off more explicit.
What is Net Treatment Benefit? Net Treatment Benefit is a measure of treatment effect derived from the Generalised Pairwise Comparisons statistical methodology. Generalised Pairwise Comparisons allow several outcomes to be considered together, while also allowing those outcomes to be prioritised according to their clinical importance.4 The principle is relatively straightforward.
What Happens After the Dose Has Been Selected? If the treatment is ultimately approved, the same evidence used to select the best dose continues to have value. It can provide a transparent scientific rationale for why the selected dose was taken forward into Phase III and ultimately to patients. A robust benefit and risk analysis can also help to communicate the reason behind
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dose selection to regulators, HTA bodies, and clinicians, who want to better understand the overall benefit-risk profile of a treatment.7 Since Project Optimus was first launched, the positive trajectory is clear. Still, there is work to be done to ensure that approved doses are the ones that provide the greatest overall benefit to patients. Randomised dose optimisation studies provide the evidence needed to make that decision. Net Treatment Benefit provides one practical framework for bringing efficacy, safety and tolerability together into a single analysis, helping sponsors select the right dose with confidence and supporting that decision from Phase II through regulatory review and into clinical practice. REFERENCES 1.
2.
3.
4. 5.
U.S. Food and Drug Administration. Project Optimus: Reforming the dose optimization and dose selection paradigm in oncology. FDA Oncology Center of Excellence. Available at: www.fda.gov/about-fda/oncology-centerexcellence/project-optimus, visited on 13 Aug 2026. Shah, M., Rahman, A., Theoret, M. R. & Pazdur, R. The Drug-Dosing Conundrum in Oncology: When Less Is More. N Engl J Med 385, 1445–1447 (2021). U.S. Food and Drug Administration. Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases: Guidance for Industry. August 2024. Buyse, M. Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. Stat Med 29, 3245–3257 (2010). Giai, J. et al. Net benefit in the presence of correlated prioritized outcomes using generalized pairwise comparisons: A simulation study. Stat Med 40, 553–565
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6. 7.
(2021). Peron, J. et al. Restricted Net Treatment Benefit in oncology. J Clin Epidemiol 168,111340 (2024). U.S. Food and Drug Administration. Benefit-Risk Assessment for New Drug and Biological Products: Guidance for Industry. FDA, 2023.
Pascal Piedbois Pascal Piedbois, professor of medical oncology, offers considerable experience from both the academic and industry sectors. In academia, he served as the Head of Medical Oncology at Paris University and was the General Director of the Comprehensive Cancer Centre of the Alsace Region, based in Strasbourg, until 2015. In the industry, Pascal has held several prominent leadership roles, including Vice President, Head of Global Oncology and Infectious Diseases at AstraZeneca, Senior Vice President, Head of Medical Europe, Canada, and Japan, and Senior Vice President, Head of Medical Go to Market Strategy for Bristol Myers Squibb. Additionally, he has served as the General Manager of Boehringer-Ingelheim Belgium. Pascal’s passion and commitment to combining digital innovation with medical relevance is set to enable the incorporation of patient preferences and needs into the development and market access of new treatments.
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Logistics and Supply Chain
Clinical Supply Distribution Strategy:
Planning for Flexibility in a Changing Trial Landscape The clinical trial ecosystem is undergoing a structural shift. What was once a relatively linear, site-centric model has evolved into a complex, globally distributed and increasingly patient-driven environment. In this new reality, clinical supply distribution is no longer a downstream operational consideration, it is a strategic enabler of trial success. Emerging as the defining characteristic of effective supply strategies is flexibility. Often misunderstood as a reactive capability, flexibility should be a deliberately engineered feature of supply chain management, with the most resilient supply chains designed from the outset to accommodate uncertainty, absorb disruption and adapt in real time. The objective is not simply to have more inventory or more distribution points, it is to create options: options around where inventory is held, how quickly it can be deployed, where it can ultimately be used and how the network can respond when clinical circumstances change. From Predictability to Variability Traditional clinical supply models were built on a foundation of assumed predictability. Enrolment curves, site activation timelines and geographic footprints were expected to follow predefined patterns. While deviations occurred, they were typically manageable within fixed distribution frameworks. Today, variability is the norm. Adaptive trial designs introduce protocol changes mid-study which shift enrolment patterns and
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associated demand across regions. Decentralised models expand the points of delivery beyond clinical sites to include patients’ homes and local care settings. This has fundamentally altered the role of distribution strategy as it must now account not only for what is known, but for what is likely to change. For supply chain leaders, this means challenging assumptions earlier. Where will demand arise? How quickly could it move? Which inventory should be committed to a particular country and which should remain flexible? Where should stock be positioned to provide the greatest resilience without creating unnecessary inventory or cost? Flexibility as a Design Principle Flexibility in clinical supply is built through intentional design, moving away from static planning toward dynamic, scenario-based approaches. At its core, a flexible supply chain strategy integrates three capabilities: • • •
Responsiveness: the ability to adjust supply flows quickly in response to real-time data. Resilience: the capacity to maintain continuity despite disruptions. Optionality: the presence of viable alternative pathways for distribution, sourcing and fulfilment.
Consider a multi-regional trial where enrolment accelerates unexpectedly in Asia while slowing in North America. A rigid supply model may struggle to rebalance inventory without delays or waste. A flexible model managed by an experienced supply chain leader, by contrast, anticipates such divergence and enables rapid reallocation
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Logistics and Supply Chain through regional stock pools, adaptable depot configurations and responsive resupply triggers. Rethinking Network Design Distribution network design is central to enabling flexibility. Historically, centralised depot models were favoured for their simplicity and cost efficiency. However, as trials become more global and timesensitive, centralised approaches can introduce latency and risk. Decentralised or hybrid depot networks offer greater agility, allowing inventory to be positioned closer to demand, reducing lead times and enabling faster response to enrolment shifts. However, increased network complexity requires more sophisticated coordination, visibility and governance. An optimal distribution model is rarely fixed. It should be adaptable and capable of evolving as the trial progresses or conditions change. This means considering not just where inventory is distributed, but where it is strategically held before the final destination is known. A regional hub can act as a buffer between global manufacturing and individual markets, allowing inventory to be deployed as demand becomes clearer rather than prematurely committing stock to a specific country. The Role of Data and Visibility Data are at the heart of a flexible supply chain. Without timely and accurate visibility into inventory levels, site consumption and shipment status, even the most well-designed strategies can falter. Advanced analytics are increasingly being used to move beyond reactive monitoring toward predictive insight. By identifying patterns in enrolment, dosing and resupply, supply teams can anticipate imbalances before they materialise. For example, predictive models can flag potential stockouts weeks in advance, enabling proactive redistribution or expedited shipments. This shift from hindsight to foresight is critical in an environment where delays can directly impact patient outcomes. However, visibility needs to extend beyond physical inventory. Supply teams also need visibility of the regulatory, financial and logistical status of that inventory. Understanding whether stock can be rapidly redirected, exported, or released into a particular market can be just as important as knowing how many units are physically available. Late-Stage Customisation and Inventory Efficiency One of the most effective levers for flexibility lies in postponement strategies, particularly in packaging and labelling. By deferring country-specific or trial-specific customisations until closer to the point of distribution, organisations can maintain a more flexible inventory pool and reduce the risk of obsolescence resulting from protocol amendments or regulatory changes. It also enables more efficient use of manufactured product, which is particularly important for high-value or rare disease therapies. For example, rather than producing fully country-specific inventory at the outset a sponsor may maintain a pool of partially kitted inventory then complete the packaging, apply country-specific labelling or add variable text closer to distribution. This can reduce the amount of stock tied to a particular market and provide greater flexibility if enrolment or country participation changes. However, postponement requires alignment across manufacturing, packaging and regulatory functions, as well as investment in capabilities that support rapid turnaround. www.journalforclinicalstudies.com
Direct-to-Patient and the Expansion of Distribution Models The rise of decentralised and hybrid trials has expanded the scope of distribution beyond traditional site delivery. Direct-to-patient (DTP) models are becoming an integral component of modern trial design, offering improved convenience and accessibility for participants. While DTP introduces clear benefits, it also challenges conventional supply frameworks. Temperature control, chain of custody, patient scheduling and regulatory compliance must all be managed with greater precision. From a strategic perspective, DTP underscores the need for distribution models that are both flexible and patient-centric. Supply chains must now accommodate individualised delivery pathways while maintaining consistency, quality and compliance. The growth of DTP also reinforces the importance of strategically located inventory. When supply needs to reach individual patients rather than simply a defined network of clinical sites, the ability to reposition stock quickly can become a significant determinant of service levels. Managing Global Complexity Global clinical trials increase regulatory, logistical and financial complexity. Country-specific import requirements, lead times and distribution constraints should be considered from the outset with contingencies built in for regulatory changes and delays. Integrating a customs strategy with supply chain, regulatory and trade compliance planning can be beneficial with the utilisation of Customs Warehouses and Foreign Trade Zones (FTZs) providing an additional layer of flexibility. Beyond deferring customs costs and supporting working-capital management, they can help sponsors maintain inventory optionality and avoid prematurely committing stock to a specific market. In the UK and EU, eligible imported goods can be held in a Customs Warehouse under customs control, with applicable duty and import VAT suspended until release into free circulation. In the US, FTZs similarly allow eligible goods to be held under customs control, with duties generally deferred until foreign merchandise enters the domestic market and eligible exports potentially leaving without US duty. For clinical trials, this is particularly valuable when demand, regulatory approvals or country participation remain uncertain. Instead of committing inventory immediately to individual markets, sponsors can position stock at a strategic regional hub and determine its final destination as the trial evolves. Risk as a Constant, Not an Exception Risk is often treated as an external factor to be mitigated but in reality it is an inherent aspect of clinical supply. Transportation disruptions, geopolitical events, tariffs, regulatory changes and environmental factors are not anomalies, they are ongoing variables that must be managed continuously. A flexible distribution strategy embeds risk management into its design. This includes: • • • •
Diversified transportation routes and carriers. Strategic safety stock aligned with risk profiles. Predefined contingency plans that can be activated without delay. Alternative depot and distribution pathways. Journal for Clinical Studies 45
Logistics and Supply Chain •
Appropriate customs and trade strategies that preserve inventory optionality.
Critically, these plans must be operationalised, not just documented. Simulation exercises and scenario testing can help ensure that teams are prepared to respond effectively under pressure. Cross-Functional Integration Early cross-functional engagement is essential for a successful supply strategy. Decisions made during protocol development such as visit schedules, dosing regimens and site locations have direct implications for distribution. Integrating supply expertise at this stage can prevent downstream challenges and enable more efficient trial execution. The same principle applies to customs and trade strategy. Decisions about manufacturing location, packaging configuration, inventory ownership and regional distribution can have significant implications for duties, taxes, import requirements and the ability to redirect inventory. Bringing these disciplines together early allows sponsors to design the supply chain around the clinical programme rather than attempting to retrofit flexibility after the network has already been established. Equally important is ongoing alignment. As trials evolve, continuous communication across functions ensures that supply strategies remain synchronised with operational realities. Looking Forward: Designing for the Unknown Clinical research is becoming more complex, personalised and decentralised, placing greater demands on clinical supply chains. In this environment, flexibility is no longer an advantage, it is a strategic requirement. The most resilient supply strategies are built around options, not fixed pathways. Data-driven forecasting, regional inventory networks, postponement and strategic use of Customs Warehouses and Foreign Trade Zones can help sponsors anticipate change, protect inventory and respond quickly as trial requirements evolve. The supply chains best positioned for the future will combine strategic foresight, real-time visibility and operational agility creating the flexibility to move, hold, redirect and deploy inventory as clinical programmes evolve. Ultimately, clinical supply distribution is about more than moving product. It is about ensuring the right investigational therapy reaches the right patient, at the right time, wherever they are.
Edward Groleau Ed Groleau has over 30 years of experience in pharmaceutical drug development on both the pharma and vendor sides. The first half of his career was spent in various laboratories from analytical method development to solid state characterisation and polymorph screening, to stress degradation and chemical characterisation. He left the labs and moved into clinical supplies in 2003 providing all aspects of CSM support to maintain clinical programmes. He joined PCI in 2018 and became Sr. Director of PCI’s Supply Management And Readiness Team (SMART).
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