Optimising Global Early-phase Oncology Trials: Strategies for Success
Visualisation Approaches Coming to Light: Advances in Optical Imaging
Imaging: Advancing Biomarker Research for Alzheimer's Disease Diagnosis
Facilitating the Adoption of Digitalisation in Medications Adherence Monitoring
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WATCH PAGES
6 Expert Predictions for 2025: Spotlight on Efficiency, AI, Regulatory, Risk and Better Communications
In 2024, the life sciences sector faced cost pressures, driving AI adoption for regulatory processes and efficiency. Companies tackled patent cliffs and data governance challenges while integrating AI into compliance and safety. Sue Tabbitt of Sarum Life Sciences explains how data science, digital transformation, and improved communication strategies is key in 2025.
8 Visualisation Approaches Coming to Light: Advances in Optical Imaging
In response to growing interest in novel optical imaging drugs and devices for surgical procedures, the FDA released draft guidance outlining clinical trial designs for these agents. The guidance emphasises enhancing tumour detection and reducing positive margins using optical imaging, which minimises radiation exposure. Clarivate’s Deborah Komlos adds how notable approvals, highlight significant advancements in surgical precision and patient outcomes.
12 How Technology will Shape Pharma in 2025: Predictions from Veeva
Biopharma companies are leveraging AI and data tools to enhance R&D efficiency and commercial performance in 2025. AI enables personalised HCP engagement, faster clinical trials, and regulatory compliance. Chris Moore at Veeva Europe examines key trends include AI-driven content review, advanced analytics, simultaneous regulatory submissions, and CRO data transparency.
16 Addressing Disparities in Clinical Research Participation in Genetically Based Rare Paediatric and Neurodevelopmental Diseases
Advances in genetics have improved diagnoses of paediatric neurodevelopmental disorders, but treatment remains limited, especially for rare diseases. Clinical trials lack diverse participation due to socioeconomic, geographic, and cultural barriers. Scott J. Hunter of WCG Clinical states that solutions include community engagement, decentralised trials, and ethical research design.
The opinions and views expressed by the authors in this journal are not necessarily those of the Editor or the Publisher. Please note that although care is taken in the preparation of this publication, the Editor and the Publisher are not responsible for opinions, views, and inaccuracies in the articles. Great care is taken concerning artwork supplied, but the Publisher cannot be held responsible for any loss or damage incurred. This publication is protected by copyright.
Volume 17 Issue 1 Spring 2025, Senglobal Ltd.
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20 Imaging: Advancing Biomarker Research for Alzheimer's Disease Diagnosis
The Bio-Hermes study explores blood-based biomarkers, as a costeffective alternative to amyloid PET scans for Alzheimer’s diagnosis. Findings show strong predictive accuracy, reducing the need for invasive tests. IXICO’s Robin Wolz highlights how the study emphasises inclusivity, revealing racial biomarker differences.
24 Expert Insight Q&A: Advancing Oncology Therapies with Highly Potent APIs
Highly potent active pharmaceutical ingredients (HPAPIs) are transforming oncology by enabling targeted, low-dose treatments with minimal toxicity. Their demand is reshaping pharmaceutical manufacturing, requiring advanced containment and scalable production. David O’Connell at PCI Pharma Services adds that future growth depends on investments in safety, technology, and integrated development to meet evolving oncology treatment needs.
26 Facilitating the Adoption of Digitalisation in Medications Adherence Monitoring
Digital adherence monitoring is essential for clinical trials, yet outdated methods persist. New guidance from ICH and WHO stresses adherence monitoring but lacks a standardised method. Dr. Bernard Vrijens of AARDEX Group highlights how with proven benefits, digital adherence monitoring should be universally adopted to enhance trial reliability, reduce costs, and improve patient outcomes.
28 Malaysia: The Hub for Medical Device Trials
Malaysia is emerging as a key hub for medical device clinical trials, supported by a strong regulatory framework, AI-driven innovation, and a growing clinical research ecosystem. Nur Ain binti Amir and others at Clinical Research Malaysia (CRM) explain how the country’s streamlined approval processes, government initiatives, and experienced workforce position it for global competitiveness.
32 Navigating Protocol Development in Early Phase Trials
Early phase clinical trials shape drug development by assessing safety, dosing, and early efficacy. Choosing between healthy volunteers or patients impacts trial outcomes. Adaptive designs, Bayesian methods,
and master protocols enhance efficiency. ICON’s Sandra Eagle states that strategic protocol design, regulatory foresight, and partnerships help biotech firms navigate challenges and accelerate drug approval.
34 Building Effective Partnerships Between Biotechs and CROs to Optimise Outsourcing
Biotechs are driving R&D innovation but face outsourcing challenges in data, expertise, and infrastructure. Partnering with the right CRO is crucial for success. Stephen Corson at Phastar discusses how effective partnerships enhance trial execution, accelerate timelines, and ensure quality outcomes, allowing both biotechs and CROs to thrive in a rapidly evolving market.
36 Master Protocols: Patient Centricity in Randomisation Design and System Implementation
Master Protocols, including Basket, Umbrella, and Platform trials, enhance drug development efficiency and patient outcomes through adaptive designs. They enable flexible randomisation, shared control arms, and real-time modifications. Widely used in oncology research, they expedite treatment identification. Jennifer Ross, Kevin Venner, and Noelle Sassany of Almac Clinical Technologies touch on the importance of robust randomisation systems and early collaboration among key stakeholders for successful implementation.
40 Optimising Global Early-phase Oncology Trials: Strategies for Success
Early-phase oncology trials are evolving due to new FDA guidelines and global regulatory shifts. Success requires dose optimisation, global coordination, adaptable planning, site and KOL engagement, and effective logistics. AI tools and real-world data enhance efficiency and improve outcomes. Keya Watkins and Marcia Milholen at Catalyst Clinical Research stress how strategic planning, local expertise, and flexible operations are essential to navigate complexities and deliver high-quality, timely trial results.
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42 Balancing Innovation and Efficiency in Clinical Trials: Is There a Middle Ground?
To balance scientific progress and trial efficiency, clinical research must simplify user experiences and integrate connected data. Increasing trial complexity hinders efficiency, with technology burdens and disjointed systems affecting sites and patients. Veeva’s Manny Vazquez discusses how streamlining processes and tailoring tech to site needs can enhance clinical trials, ensuring innovation focuses on meaningful advancements rather than excessive data collection.
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Welcome to the Spring edition of JCS! I hope you enjoy the features in this issue as much as I have enjoyed curating them.
Clinical research plays a pivotal role in shaping healthcare policies, developing new treatments, and refining existing practices to meet the evolving needs of diverse populations. Our journal aims to provide a comprehensive platform for the latest developments in the industry while offering valuable insights into the challenges and opportunities facing clinical research today.
As healthcare systems worldwide confront emerging health threats, an aging population, and the increasing demand for personalised medicine, clinical studies provide essential data to guide practice. The articles in this issue reflect the dynamic and evolving nature of clinical research on a global scale. Through these pages, we hope to inspire collaboration, stimulate further inquiry, and equip healthcare professionals and researchers with the tools necessary to continue advancing clinical studies for the benefit of all.
In the market report section, Chris Moore at Veeva Europe presents a forward-looking feature that offers a timely exploration of how clean data, compliant systems, and human-centric AI integration are essential to unlocking true value. From streamlining medical content reviews to enabling simultaneous regulatory submissions and automating safety monitoring, biopharma is redefining speed, precision, and impact. With a strong emphasis on data quality and collaboration, this piece provides essential guidance for companies aiming to thrive in a digital-first, insight-driven future.
Highly potent active pharmaceutical ingredients (HPAPIs) are at the forefront of a transformative era in oncology. Their ability to deliver powerful therapeutic effects at ultra-low doses makes them essential in today’s push toward precision medicine. Under the therapeutics heading. This expert Q&A in the Therapeutics segment, featuring David O’Connell of PCI Pharma Services, examines the crucial role of HPAPIs in modern cancer therapies – from their use in antibody-drug conjugates to the challenges of safe, scalable manufacturing.
Medication adherence is a cornerstone of clinical trial success, yet outdated and unreliable monitoring methods persist. We open the
• Ashok K. Ghone, PhD, VP, Global Services MakroCare, USA
• Bakhyt Sarymsakova – Head of Department of International Cooperation, National Research Center of MCH, Astana, Kazakhstan
• Catherine Lund, Vice Chairman, OnQ Consulting
• Cellia K. Habita, President & CEO, Arianne Corporation
• Chris Tait, Life Science Account Manager, CHUBB Insurance Company of Europe
• Deborah A. Komlos, Principal STEM Content Analyst, Clarivate
• Elizabeth Moench, President and CEO of Bioclinica – Patient Recruitment & Retention
• Francis Crawley, Executive Director of the Good Clinical Practice Alliance – Europe (GCPA) and a World Health Organisation (WHO) Expert in ethics
• Georg Mathis, Founder and Managing Director, Appletree AG
Technology section with an insightful piece by Dr. Bernard Vrijens of AARDEX Group, highlighting how digital adherence monitoring can revolutionise data quality, patient safety, and trial efficiency. With new guidance from ICH and WHO emphasising adherence as essential to trial integrity, the time has come to modernise our approach. This article makes a compelling case: digital adherence monitoring isn’t just beneficial – it’s essential for the future of clinical research.
As biotech companies continue to lead the charge in research and development innovation, the need for specialised outsourcing support has never been greater. In this timely and engaging article from the Clinical Management segment, Phastar’s Stephen Corson explores the evolving challenges biotechs face in navigating the complex clinical trial landscape. With data integrity, talent access, and agility at the forefront, he offers practical strategies for selecting the right contract research organisation (CRO) partner.
As we move forward, these articles serve as a testament to the progress being made within clinical research and the impact it has on the broader healthcare landscape. By addressing key issues such as data quality, patient safety, and the integration of new technologies, we are collectively advancing the capabilities of clinical studies.
We hope this edition gets the conversation going, sparks fresh ideas, and helps shape the future of clinical research. Thanks for reading, and we cannot wait to share more great content in the next editions of JCS!
Kelly Woods, Editorial Manager
• Hermann Schulz, MD, Founder, PresseKontext
• Jeffrey W. Sherman, Chief Medical Officer and Senior Vice President, IDM Pharma.
• Jim James DeSantihas, Chief Executive Officer, PharmaVigilant
• Mark Goldberg, Chief Operating Officer, PAREXEL International Corporation
• Maha Al-Farhan, Chair of the GCC Chapter of the ACRP
• Rick Turner, Senior Scientific Director, Quintiles Cardiac Safety Services & Affiliate Clinical Associate Professor, University of Florida College of Pharmacy
• Robert Reekie, Snr. Executive Vice President Operations, Europe, AsiaPacific at PharmaNet Development Group
• Stanley Tam, General Manager, Eurofins MEDINET (Singapore, Shanghai)
• Stefan Astrom, Founder and CEO of Astrom Research International HB
• Steve Heath, Head of EMEA – Medidata Solutions, Inc
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In 2024 the life sciences sector experienced high levels of cost pressure that drove process transformation in regulatory functions and smarter use of data and human resources. At the same time, the potential of artificial intelligence (AI) began to be widely realised, with new use cases emerging to address the multiple challenges facing the sector. In this article, expert commentators review the developments of the past year and look forward to 2025.
The life sciences sector has seen the beginning of a major transformation in 2024, driven by AI and data science technology that has underpinned process change and efficiencies in areas ranging from regulatory through to communications strategies.
Cost reductions and attempts to leverage AI to save cost or become more efficient were overriding themes for 2024, according to Peter Muller, Director, Americas at Schlafender Hase: “This looks set to continue into 2025. For many organisations looking at the horizon right now there is a lot of uncertainty.”
He adds, “Also a lot of companies are facing patent cliffs. Their portfolios have drugs that have lost or are losing patent protection, and they are not generating, or expected to generate the kind of revenue the company needs. Companies anticipate a period of financial struggle, and so they want to improve their operating profitability.”
“The themes which have dominated the life sciences regulatory and quality landscape in 2024 are the integration of advanced data analytics and AI in regulatory compliance. This shift is driven by the need for more efficient and accurate compliance processes from the molecule to the product,” says Jens Marburg, Principal Consultant at MAIN5. He points out that evidence of this can be seen in the increased adoption of AI-driven tools for data validation, risk assessment, and regulatory reporting, adding: “2025 is likely to be the year of digital transformation and adoption in regulatory compliance.”
ArisGlobal’s CEO Aman Wasan cautions: “As one of the most safety- and risk-conscious industries there is, it's important that pharma/biopharma gets AI right. Where patient safety is concerned, there can be no tolerance for ‘black box’ mystery; nor data security/ privacy breaches. Results must be reliable, robust, explainable, consistent, and trust-inspiring. It’s why the sector is pushing hard not only to harness trailblazing applications but also to test the boundaries of AI explainability, optimised human sampling, and transparency (e.g. through the combination of LLMs and retrievalaugmented generation or RAG), so that regulators can see and assess outcomes for their reliability and consistency.”
Marburg at MAIN5 has seen clients increasingly embracing riskbased approaches to computerised system validation (CSV), moving away from exhaustive validation processes to more targeted, riskfocused strategies that allows for more efficient use of resources and faster project timelines. However, he says, “Companies are still not fully appreciating the importance of data governance and ownership. Many are at risk of non-compliance due to fragmented data management practices. The urgency around this issue is high, as regulatory bodies are increasingly scrutinising data integrity. Companies should establish clear data governance frameworks and assign ownership to ensure compliance and data quality.”
If there has been one overriding theme that has dominated the life sciences regulatory, safety and quality landscape in 2024, it has been the use of technology - especially AI - to support process, according to Sam Tomlinson, Vice President, Global Drug Safety, Arriello: on pharmacovigilance/PV.
She says, “We’ve seen more of an acceptance by industry that AI and technology advancement needs to be embraced. Although trust has always been an issue, Covid brought an awareness that data volumes will only increase so that technology is the only solution. The downturn in funding to support clinical development is a further factor driving the need for technology implementation. The ability of AI to interrogate data and leverage clinical results to ensure study programme success is critical.
Tomlinson believes that companies still do not fully appreciate that technology doesn’t necessarily result in a reduction of human resource; it just changes the need and the skillset: “As PV departments become more technology-driven we will need people who understand the inner workings of the technology as well as the clinical aspects of the data. The concern is this combination is very hard to find.”
“2025 will continue to see the technology theme grow, but the industry is facing an issue with the lack of combined clinical/ technology skill sets. There is an urgent need for training and education to upskill PV teams to support this new technology driven approach.”
A balanced approach that integrates AI tools with human expertise could prevent potential missteps and foster more sustainable advancements, according to Daniel Jamieson, CEO of Biorelate. “The optimism surrounding AI must be tempered with a realistic understanding of the drug discovery process. It's crucial for the industry to recognise that while AI can enhance efficiency, the expertise of drug discovery professionals remains indispensable,” he says.
Enhancing the learning and development function is key to adapting the workforce and having the right skills in place for the
coming year. Alexander Tryba, Managing Partner, at MAIN5, has seen companies increasingly recognising the importance of establishing a culture of sustainable learning, moving away from a mere ‘read and understood’ approach: “While there is a reserved attitude towards allocating resources for state-of-the-art eLearning material, there is a growing openness towards using digital adoption tools and didactically sound learning concepts.”
He cautions that AI technology cannot be relied upon to enable in-house production of learning materials: “While AI tools are indeed helpful in reducing the time required by experienced learning architects and developers for tasks such as creating starting point content, rephrasing, and generating generic images, they still and will remain to require significant input from highly skilled professionals.”
Preeya Beczek, a regulatory affairs and compliance expert and managing director of consulting firm Beczek.COM, comments: “The way in which Regulatory Affairs operates and evolves is imperative in life sciences now, where AI and automation will continue to be an important facilitator in 2025. It no longer makes sense for so much of skilled professionals’ time to be taken up by the basic operational tasks such as writing documents from scratch, performing document checks, reconciling duplicate data entries, finding information. I also see great scope for AI in transforming information searches, sourcing insights from diverse sources.
“My advice concerning new technology adoption would be, don’t wait for everything to align perfectly, or for someone else to go first. As long as the tools are fit for purpose and add value to key business processes, there are considerable benefits to be had by embracing these tools now. So often, the best way to start learning is by doing.”
Disinformation is continuously growing online and health communicators inside and outside of industry need to be more effective and relevant than ever. Michelle Bridenbaker, COO, Unbiased Science, a Vital Statistics Consulting company, says: “In 2024, there has been a big push for AI and particularly Generative AI (GenAI) where the technology promises to free up teams to be more creative in their communications. When drawing on a well-curated large language model (LLM), companies have found they can compile a good starting document that is 60% of the way there, which they can then build on and finesse.
“Freeing up creatives’ time will be important in 2025, as the industry strives to be heard and get across credible, evidence-based scientific insights amid an ocean of public health disinformation.”
Bridenbaker adds, “The other big theme for 2025 from a communications point of view will be the need to overcome organisational silos – for example between clinical, commercialisation, marketing, and medical affairs functions. This is so that the company’s
messaging and interactions with healthcare professionals and patients become more consistent and joined up (e.g. in terms of presentation of the latest disease state information, or treatment guidance), whether at a clinical trials stage or preceding or during real-world treatment. The encouraging development is that companies are more aware now of the need to deliver a well-coordinated multi-channel experience; now they just need to understand how.”
Will Hind, Founder at Alpharmaxim, believes it is time for companies to look again at how to achieve their communications goals: “When it comes to managing spend, the vast majority of educational and promotional spend on communications is done based on old habits and potentially out-dated heuristics. Measuring the behavioural impact through communications that have been developed with a behavioural science focus will ensure that these programmes achieve their goals and we move away from the old maxim of “half of my communications budget is wasted, I just don’t know which half!”.
All too often, life sciences organisations involve research data scientists too late in the development process, according to Jessica Steier, CEO at Unbiased Science. Steier says, “This impedes their ability to communicate the right messages to the market when the time arises – because they haven’t captured the right insights along the way. I expect and hope to see that start to change now.”
Marburg at MAIN5 believes the rise of data science and real-world data in the life sciences sector presents a significant opportunity for new data partnerships. These partnerships will enhance the pool of available data assets, driving innovation in drug discovery and development.
He says: “However, the foundation of these advancements lies in the completeness, correctness, and consistency of data, which is ensured through rigorous CSV. By maintaining high standards in CSV, companies can create a reliable data foundation that supports robust data ecosystems. This, in turn, will enable more accurate predictions, personalised medicine, and faster regulatory approvals through collaborations between pharmaceutical companies, technology providers, and regulatory bodies.”
Sue Tabbitt is a Technology and Business Journalist with 35 years' experience. She has covered digital transformation in healthcare and life sciences for the last two decades, and is a senior writer at Sarum Life Sciences in the UK.
Amidst the “burgeoning” interest in developing novel optical imaging drugs and imaging devices to assist various standard surgical procedures, the US Food and Drug Administration (FDA) recently issued guidance regarding clinical trial design features supporting the development and approval of optical imaging agents.1 According to the agency, a contributing factor propelling the advancement of these drugs is the use of minimally invasive surgical procedures, which are associated with a loss of tactile perception and a more narrowed field of view.
As explained in the FDA’s new draft guidance for industry Developing Drugs for Optical Imaging published in January 2025, optical imaging is the use of light in conjunction with imaging drugs and devices during medical procedures to assist in the detection of tumours or other pathology and delineation of normal anatomical structures.2 Surgeons employ these drug/device pairings to help with the direct visual inspection and palpation of tissue in the surgical field. Because imaging drugs enhance the surgeon’s ability to discern tumours from normal tissue, they can augment the likelihood of safe and complete removal of cancers and minimise the risk of unintended injury to the normal structures.
Optical imaging is advantageous as an imaging modality because it significantly reduces patient exposure to harmful radiation by using non-ionising radiation, which includes visible, ultraviolet, and infrared light, the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), explains.3 Since it is much safer than techniques that require ionising radiation such as X-rays, optical imaging can be used for repeated procedures to monitor disease progression or the results of treatment.
The new guidance focuses on the use of optical imaging for tumour detection (surgery, endoscopic resection of neoplasm), lymph node staging (lymphatic mapping, Sentinel lymph node identification), and the enhanced delineation of normal anatomy to decrease risk of injury (e.g., nerve structures in head and neck surgery). The FDA notes that optical imaging drugs are generally governed by the same regulations as other drugs. Moreover, its recommendations to developers of other medical imaging drugs – published in three separate guidance documents more than 20 years ago4,5,6 – remain applicable to optical imaging drugs.
Strengthening Surgical Success
In 2024, the FDA approved 50 “novel” drugs, either as new molecular entities (NMEs) under new drug applications (NDAs) or as new therapeutic biologics under biologics license applications (BLAs), as summarised in the agency’s 14th annual report, Advancing Health Through Innovation: New Drug Therapy Approvals 2024 issued in January 2025.7 In novel drugs, the active ingredients have not been previously approved in the US. Patrizia Cavazzoni, MD, then director
of the Center for Drug Evaluation and Research (CDER), FDA, stated in the report that it features “notable” drug approvals that CDER considers “likely to have a significant impact on patient care and public health.”
Among the 50 agents, CDER highlighted the optical imaging prodrug pegulicianine as one of the notable first-in-class approvals. Granted marketing authorisation under the trade name Lumisight from Lumicell, Inc, pegulicianine is optically inactive when intact but produces a fluorescent signal after its peptide chain is cleaved by cathepsins and matrix metalloproteases.8 The levels of these enzymes are higher in and around tumour and tumour-associated cells than normal cells. This enzymatic cleavage generates two optically active metabolites that fluoresce and another fragment that contains the fluorescence quencher that maintains the intact molecule optically inactive.
Pegulicianine is administered as an intravenous injection for fluorescence imaging in adults with breast cancer as an adjunct for the intraoperative detection of cancerous tissue within the resection cavity following removal of the primary specimen during lumpectomy surgery. It was approved for use with the Lumicell Direct Visualisation System (DVS) or another fluorescence imaging device that is FDA approved for use with pegulicianine in the indicated population.
The efficacy and safety of pegulicianine were evaluated in a multicenter, intrasubject-controlled clinical trial of patients with breast cancer undergoing lumpectomy surgery.9 Subjects randomised to the pegulicianine group received fluorescence imaging of the lumpectomy cavity following standard-of-care (SOC) surgery and removal of additional tissue if a positive signal was observed. As discussed in the FDA’s optical imaging guidance, an intrasubject control for efficacy trials can be used to test the hypothesis that optical imaging provides supplementary information beyond the tactile and visual perception achieved with SOC practice. This approach is efficient because it internally controls for variability due to individual subject characteristics (e.g., pathology, anatomy), the agency states.
Regarding specific considerations for efficacy, the guidance reviews common types of surgical oncology procedures for which optical imaging drugs might be used to improve current SOC procedures. It notes that in clinical settings in which the presence of positive margins directly affects patient management, the trial should aim to show a reduction of the positive margin rate with use of the optical imaging drug as compared with SOC resection. One such clinical setting is breast-conserving surgery, where positive margins may result in a second surgery for the patient or a boost of radiation therapy. In the case of pegulicianine, the study evaluated the proportion of subjects who had residual breast cancer detected and removed following SOC lumpectomy surgery and the sensitivity and specificity of pegulicianine for residual breast cancer.
The US commercial availability and first commercial use of LumiSystem, which combines Lumisight and the Lumicell DVS, was announced by Lumicell in January 2025.10 Lumicell explained that its system permits real-time visualisation of cancer within the breast, unlike other detection systems that solely assess tissue after it has been removed surgically. Thus, surgeons can identify and promptly resect any suspicious tissue, improving the prospect of achieving a more complete resection and reducing the need for follow-up procedures.
Clinical investigations underway for other types of optical imaging agents to improve cancer visualisation during surgery include the following agents for head and neck cancer:
A fluorophore developed by FluoGuide A/S, FG001 is directed against a cancer-specific target expressed extensively in the majority of solid cancers known as urokinase-type plasminogen activator receptor. Because FG001 has the identical spectral specifications as indocyanine green, which is already marketed as an optical imaging agent (e.g., Spy Agent Green,11 from Novadaq Technologies), it “can be used on current imaging equipment without adaptation,” according to FluoGuide.12 Administered intravenously prior to surgery, FG001 illuminates the cancer to guide the surgeon in removing the malignancy while sparing healthy tissue. FG001 was granted orphan drug designation by the FDA in October 2023 for its use as an optical imaging agent to visualise malignant tissue during surgery for high-grade glioma.12,13
In January 2025, FluoGuide announced the approval of its phase II clinical trial application for FG001 in head and neck cancer, which was based on “strong clinical topline data” from a proof-of-concept phase II clinical trial.14 The firm is preparing to initiate a single-center phase II trial to assess multiple endpoints for measuring surgical completeness with FG001 in 25 to 30 patients with head and neck cancer. Enrollment is to begin within the first quarter of 2025.
Various clinical studies are evaluating the use of panitumumabIRDye800 in optical imaging for cancer indications. As explained by the NIH’s National Cancer Institute, the agent comprises panitumumab, a humanised anti-epidermal growth factor receptor (EGFR) monoclonal antibody, conjugated to the near-infrared fluorescent dye IRDye800.15
After administration of panitumumab-IRDye800, the panitumumab moiety targets and binds to EGFR expressed on tumour cells, which can be detected upon fluorescence imaging of IRDye800.
Investigations underway include a phase II clinical study at the University of Alabama at Birmingham evaluating panitumumabIRDye800 as an optical imaging agent to detect head and neck cancer during surgical procedures. The study description at ClinicalTrials.gov notes that panitumumab-IRDye800CW “has emerged as the frontrunner in optical imaging.”16 The webpage states that the design of the study, which is anticipated to complete by the end of 2027, is the standard for phase II clinical trials in the field of fluorescence-guided surgery.
In the 2025 FDA guidance on optical imaging, the agency mentions in relation to primary solid tumours that complete resection with negative tumour margins “is often necessary for curative treatment.” Incomplete resection with positive margins or close margins – cancer cells within a certain distance from the edge of resected tissue – is associated with poorer clinical outcomes.
With novel agents such as pegulicianine entering the scene, the surgical experience is undergoing a finetuning that promises to, ultimately, improve upon health consequences for patients, including recurrence rates, disease-free survival, and overall survival.
1. Developing Drugs for Optical Imaging; Draft Guidance for Industry; Availability. Federal Register, Vol. 90, No. 5. January 8, 2025: 1504-1505. https://www.govinfo.gov/content/pkg/FR-2025-01-08/pdf/2025-00213.pdf
2. Developing Drugs for Optical Imaging: Guidance for Industry. Food and Drug Administration Website. https://www.fda.gov/media/184943/download
3. Optical Imaging. National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health Website. https://www.nibib. nih.gov/science-education/science-topics/optical-imaging
4. Guidance for Industry: Developing Medical Imaging Drug and Biological Products Part 1: Conducting Safety Assessments. Food and Drug Administration Website. https://www.fda.gov/media/71212/download
5. Guidance for Industry: Developing Medical Imaging Drug and Biological Products Part 2: Clinical Indications. Food and Drug Administration Website. https://www.fda.gov/media/71226/download
6. Guidance for Industry: Developing Medical Imaging Drug and Biological Products Part 3: Design, Analysis, and Interpretation of Clinical Studies. Food and Drug Administration Website. https://www.fda.gov/media/71237/ download
7. Advancing Health Through Innovation: New Drug Therapy Approvals 2024. Food and Drug Administration Website. https://www.fda.gov/ media/184967/download?attachment
8. Drugs@FDA Database. Food and Drug Administration Website. https://www. accessdata.fda.gov/drugsatfda_docs/label/2024/214511s000lbl.pdf
9. NCT03686215: Investigation of Novel Surgical Imaging for Tumor Excision (INSITE). ClinicalTrials.gov Website. https://clinicaltrials.gov/study/ NCT03686215?term=NCT03686215&rank=1
10. Lumicell Announces U.S. Launch and First Commercial Use of LumiSystem™, Pioneering Real-Time Detection of Residual Breast Cancer During Lumpectomy. Lumicell Website. https://lumicell.com/lumicellannounces-u-s-launch-and-first-commercial-use-of-lumisystempioneering-real-time-detection-of-residual-breast-cancer-duringlumpectomy/
11. Drugs@FDA Database. Food and Drug Administration Website. https://www. accessdata.fda.gov/drugsatfda_docs/label/2023/211580Orig1s006lbl.pdf
12. FluoGuide Receives FDA Orphan Drug Designation for FG001 in HighGrade Glioma. FluoGuide Website. https://fluoguide.com/mfn_news/ fluoguide-receives-fda-orphan-drug-designation-for-fg001-in-high-gradeglioma/
13. Search Orphan Drug Designations and Approvals Database. Food and Drug Administration Website. https://www.accessdata.fda.gov/scripts/opdlisting/ oopd/detailedIndex.cfm?cfgridkey=960223
14. FluoGuide A/S Receives Approval for Phase II Trial in Head and Neck Cancer. FluoGuide Website. https://fluoguide.com/mfn_news/fluoguidea-s-receives-approval-for-phase-ii-trial-in-head-and-neck-cancer/
15. Panitumumab-IRDye800. National Cancer Institute Website. https://www. cancer.gov/publications/dictionaries/cancer-drug/def/panitumumabirdye800
16. NCT04511078: Phase II Panitumumab-IRDye800 in Head & Neck Cancer. ClinicalTrials.gov Website. https://clinicaltrials.gov/study/NCT04511078? term=Panitumumab%20IRDye800&rank=4
Deborah Komlos, MS, is a Principal STEM Content Analyst for the Cortellis suite of life science intelligence solutions at Clarivate. In this role, her coverage centres on FDA advisory committee meetings, workshops, and product approvals. Her previous positions have included writing and editing for magazines, newspapers, online venues, and scientific journals, as well as publication layout and graphic design work.
Email: deborah.komlos@clarivate.com
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As we advance through 2025, biopharma companies are increasingly using artificial intelligence (AI) and new data tools to improve commercial performance and make research and development (R&D) more efficient. AI is changing the way the industry engages with healthcare professionals (HCPs), allowing for personalised interactions, better content strategies, and useful insights for field teams. In R&D, new methods for handling data and navigating regulations are speeding up clinical trials, shortening approval times, and improving transparency at research sites.
On the commercial side, making the most of AI and data-driven solutions depends on having clean, organised data and investing in systems that are scalable and compliant. Companies focusing on data quality, following regulations, and using AI-driven insights will be better prepared for long-term success. Key improvements include AIpowered tools for creating and reviewing content, advanced analytics for smarter business decisions, and streamlined operations through integrated quality and regulatory systems.
In R&D, the spotlight is on speeding up approvals through simultaneous submissions, sharing data more openly with contract research organisations (CROs), and automating safety monitoring. Simultaneous submissions can cut approval times significantly, while better data sharing with CROs leads to faster, more informed decisions. Advanced automation, supported by reliable safety data, will simplify pharmacovigilance and reduce operational challenges.
A Focus
Data Will Fuel Compliant AI Innovation in the EU
The recent wave of AI innovation has fallen short of transforming commercial life sciences. In 2025, European biopharma’s that unlock harmonised internal and external data will start to reap commercial rewards.
Biopharma organisations will combine off-the-shelf AI engines with more harmonised, clean data. The integration of these AI solutions with well-structured, high-quality datasets will enable organisations to achieve deeper insights, enhance operational efficiency, and drive evidence-based decision-making. Acquiring data from trusted, internally verified sources will lead to greater confidence in AI-generated outcomes. By ensuring data integrity and consistency across different functions, companies can minimise biases and inaccuracies, ultimately boosting stakeholder trust. This will make it easier to scale pilots from single-market, single-brand solutions across the enterprise. As these scaled solutions prove their value, biopharma organisations will be better positioned to optimise their supply chains, improve patient outcomes, and streamline regulatory reporting.
The EU recently introduced the Artificial Intelligence Act — the first comprehensive AI regulation by any regulator, designed to ensure that AI is developed and used safely. This landmark legislation establishes clear guidelines on risk categorisation, transparency, and accountability, compelling companies to align their AI strategies with ethical and legal standards. Along with existing European data privacy rules, European biopharma’s will have clear principles to support future investment and innovation. These combined regulations will foster an environment where AI-driven projects are not only innovative but also aligned with societal expectations and legal mandates. Commercial success will come to those that clean up their data, secure new sources, and interrogate them within this regulatory framework. In the long run, organisations that proactively embrace compliance while leveraging AI will gain a competitive advantage, positioning themselves as industry leaders in ethical AI adoption.
Growing AI use cases for content creation, hygiene, and quality checks will drive record-high content volume, making it more difficult to get relevant messages to market. This influx of content will challenge teams to maintain consistency and alignment with brand messaging across multiple channels. As a result, content teams and agency partners that focus AI investments on both targeting high-value content creation alongside improving MLR review will be the first to see ROI. Leveraging AI to identify content gaps and optimise messaging strategies will further enhance engagement and impact.
By decreasing review cycles and reducing cycle times, AIempowered MLR teams will accelerate compliant, accurate commercial content, despite the growing volume. Automating routine tasks within the MLR process will allow teams to allocate more time to strategic decision-making and innovation. These efficiencies will help move MLR review from the last stop in the content cycle to a proactive role with greater visibility and input into the content creation engine, further reducing rework. This shift will foster greater collaboration between content creators and reviewers, leading to higher quality outputs and faster go-to-market timelines.
Organisations that eliminate complexity in their content supply chain by classifying content using standard taxonomy, removing bespoke solutions, and harnessing hosted large language models will move faster and more efficiently. Standardisation efforts will also enhance interoperability across global markets, ensuring regulatory compliance while maintaining flexibility. Content and MLR teams that prioritise content quality over content quantity will be early commercial leaders in delivering value from AI. By focusing on meaningful, impactful content, these organisations will build stronger connections with their audiences and achieve sustainable growth.
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As commercial organisations consider AI use cases for advanced analytics, they will also be forced to confront AI’s limits. Despite its potential, AI systems often struggle to adapt to the nuanced realities of commercial operations, where human judgment and experience play a critical role. For example, an AI-generated “next best action” would need to consider a wide range of variables that are not easily accounted for, from a rep's call plan to challenging HCP access to incentive compensation plans. If these contextual factors are overlooked, AI recommendations may fail to align with on-the-ground realities, leading to inefficiencies. If the recommended next best action is one a rep can’t – or won’t – act on, AI is just adding more noise to the system.
As a result, early leaders will focus on investments in people to get the best ROI from carefully selected initiatives. Success will depend on equipping teams with the skills to interpret AI-driven insights and incorporate them into their daily workflows. AI use cases in advanced analytics will be most successful when companies spend time upfront defining problems, structuring data, and training users to act on insights. Early leaders will build a change management culture that addresses knowledge and skills gaps. This approach will help foster greater acceptance of AI and ensure its recommendations are actionable and relevant.
But AI tools that are bolted onto disconnected systems or need complex integrations with business intelligence tools will also slow down insights and discourage user adoption. Seamless integration into existing workflows will be key to driving adoption and maximising efficiency. AI that augments decision-making and is embedded in users' workflows — similar to how navigation apps guide drivers through traffic in real-time — will see wider use and set the stage for long-term ROI. By focusing on AI solutions that complement human expertise rather than replace it, organisations can unlock new levels of productivity and business value.
Prediction 1:
Simultaneous Submissions Will Shave Years off Approvals
Sequential submissions, once standard, are now seen as an outdated obstacle to timely patient access. Regulatory agencies and pharmaceutical companies are increasingly recognising the inefficiencies of this approach and are turning to more agile solutions. Although digitalisation has streamlined some aspects of regulatory submissions, core markets often receive approvals first, delaying access in smaller markets. This staggered approach results in significant gaps in global market penetration.
New methods, such as active dossiers, will allow teams to reuse prior submissions more effectively. By leveraging modular approaches and cloud-based platforms, companies can ensure consistency and efficiency across multiple jurisdictions. As more companies and health authorities embrace simultaneous submissions, timelines that once exceeded five years could be reduced significantly. This acceleration will provide patients with faster access to life-saving treatments and reduce operational burdens. These advancements will improve access for underserved patient populations while alleviating regulator.
Prediction 2:
CRO Data Transparency Will Boost Trial Success
Sponsors are increasingly prioritising contract research organisations (CROs) that provide continuous data transparency. Real-time, centralised data access is becoming a key differentiator in CRO
selection. In 2025, the industry will see a shift toward end-to-end data ownership, fostering more fluid collaboration between sponsors and CROs. This shift will empower sponsors to make faster, more informed decisions throughout the trial process.
Transparent, real-time data sharing will improve decisionmaking in protocol design, site onboarding, rare disease participant identification, and endpoint adjustments. By leveraging AI-driven analytics, sponsors can further refine trial parameters and reduce inefficiencies. Emerging biotechs will benefit from enhanced oversight, enabling more agile operations. Greater data transparency will build trust across the clinical development ecosystem, improving trial outcomes and accelerating access to new medicines.
Safety professionals continue to grapple with an age-old question: how to handle growing data volumes with fewer resources while maintaining high quality. AI holds promise to do more with less, but inconsistent and disconnected data creates risk.
To effectively support AI, safety teams will strengthen their data foundations with standardised, end-to-end pharmacovigilance processes. Cross-functional workflows will eliminate manual data transfers and provide clear data traceability back to the source. By simplifying and standardising their systems landscape, companies will lay the groundwork for accelerating automation and AI innovation.
This end-to-end data flow also opens the door for improved collaboration across organisations. For example, processes like timely reporting of SAEs from clinical EDC systems to safety can be done automatically with more complete data.
Chris Moore is the president of Veeva Europe. He is responsible for growing the business in the region. A 30-plus-year veteran of the life sciences industry, Chris started his career at ICI Pharmaceuticals (now AstraZeneca). Chris then joined a start-up called Kinesis, building a team delivering document management solutions for pharmaceutical companies. Through a series of mergers and acquisitions, Kinesis ultimately became PwC; Chris made a partner with PwC in 2001. Chris went on to run both European and US (West Coast) life sciences businesses for IBM before leading the IBM global life sciences consulting Business Analytics and Optimisation unit. Most recently, Chris was the lead partner for life sciences for Europe, the Middle East, and Africa at EY. Chris holds a Bachelor of Science degree in Information Technology from the University of Salford.
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We are at an important inflection point in our identification and understanding of genetically based diseases, particularly rarer paediatric-onset disorders that impact neurodevelopment. Concurrent with this movement forward in our knowledge and capacity for diagnosis is a demand for increased treatment, ideally at earlier stages of development. While early diagnosis of some conditions (e.g., Autism Spectrum Disorders) has led to enhanced referral, subsequent intervention at younger ages, and better outcomes, many rarer genetically based paediatric diseases remain without such early treatment. This is particularly the case with medications and genetic interventions, which require significant time for development within the laboratory and then multiphase clinical trials to assess efficacy. The ability to engage disparately affected communities has also proven challenging. A shared commitment by both the disease support communities and drug regulatory agencies has encouraged greater efforts for increased diverse participation in clinical trials. This article reviews where we stand regarding the involvement of diverse affected communities and identifying where specific emphases can be placed, to better allow for enhanced recruitment, assessment of change, and improved outcomes.
We are in the midst of a significant revolution in our identification and understanding of genetically based diseases, particularly regarding the range of paediatric-onset disorders that impact neurodevelopment and contribute to significant morbidity and mortality. Concurrent with this substantial leap in our knowledge and resulting capacity for diagnosis is a demand for increased treatment, ideally at an earlier stage of development, to maximise outcomes and opportunities for affected individuals. Early diagnosis in such conditions as Autism Spectrum Disorders has led to enhanced referral and engagement in intervention at a younger age, and hence, better outcomes given this earlier opportunity for significant support and change. However, many of the rarer genetically based paediatric diseases, including several lysosomal storage disorders, remain elusive to early treatment. This is particularly the case regarding medications and genetically based interventions, which require significant time for development within the laboratory, followed by multiphase clinical trials, to assess their validity and efficacy. There have, however, been important improvements in identifying and developing pharmacological interventions over the past decade, specifically for diseases like the mucopolysaccharidoses (MPS), Gaucher, and Rett. And these interventions have opened promising avenues for developmental improvement.
One significant challenge has been the difficulty with more effectively connecting and engaging the diverse communities that are affected by these diseases into clinical trials, particularly at phase II and III levels. This has been identified most recently as a significant need – in recognition that many of these diseases are more
readily present in diverse groups and settings where opportunity for intervention is limited. The emphasis on this particularly important challenge has led to a shared commitment by both the disease support communities, often through patient advocacy efforts, the United States Food and Drug Administration (FDA), and the European Medicines Agency (EMA), to engage greater efforts for such increased participation. With a clear emphasis on expanding treatment development in tandem with enhanced community involvement and breadth of patient engagement, it is hoped that better options for effective interventions and stronger outcomes will alter the current landscape dramatically.
This article reviews where we presently stand with regard to the involvement of the broad diversity of communities, both in the Western countries where much research takes place, but also more directly worldwide. Furthermore, we will identify and discuss where specific emphases can be placed to better allow for enhanced recruitment, assessment of change, and for improved outcomes that are seen as efficacious and valid across the range of communities these diseases affect.
Clinical research participation is critical to the development of safe and effective treatment options for genetically based rare paediatric and neurodevelopmental diseases. However, even as efforts are actively being made to increase broader patient participation (e.g., enhanced communication and recruitment efforts), the current landscape of clinical trials still reveals disparities in the inclusion of necessary diverse populations into studies. This rings true to the degree that those often most in need are the ones not receiving opportunities for treatment and enhanced outcomes. These gaps are particularly evident when considering factors such as race, ethnicity, geographic location, and socioeconomic status.
One of the continually challenging issues in paediatric clinical trials is the underrepresentation of non-white and non-Western minoritised groups; most paediatric clinical trials are predominantly composed of participants from white-majority Western countries, specifically those of middle to upper-class status. This means we are still leaving a significant portion of affected individuals behind regarding treatment options, across both local and global communities that remain historically underrepresented. This underrepresentation is directly problematic because it has been well noted that genetic variability can influence how different populations present with rare diseases and ultimately then respond to treatments. By increasing the participation of globally diverse individuals, we better understand the breadth of potential differences a disease itself may present, and how that may either hamper or encourage response to intervention. Studies have shown that certain paediatric neurodevelopmental disorders, such as Sickle Cell Disorder and Thalassemia, manifest differently across various racial and ethnic groups. Without inclusive representation,
the effectiveness and safety of treatments for patients may remain unknown, perpetuating health disparities in both opportunity and outcome across the early lifespan.
There have been several well identified factors that contribute to the disparities we observe in clinical research participation:
1. Socioeconomic Barriers: Families from lower socioeconomic backgrounds often struggle with the time and financial demands required for participating in clinical trials. These barriers can include parents and caregivers taking time off from work, traveling to study sites, and covering associated costs that come with participation, including stays at clinical sites and meals while away from home.
2. Geographic Barriers: Many clinical trials are conducted in urban centers of Western countries, making it difficult for families living in rural areas or developing countries to participate. This geographic disparity can limit access to potential treatments for children in these regions, leading to disparities in possible developmental progress, as well as potential morbidities.
3. Cultural and Linguistic Differences: Cultural beliefs and language barriers can impact a family’s willingness to participate in clinical trials. Misunderstandings and mistrust in the healthcare system also play a significant role. Communities with a history of poor engagement with medical treatment often shy away from outreach.
4. Lack of Awareness: Many families simply may not be aware of the availability of clinical trials or how to access them. This lack of information can lead to missed opportunities for early intervention and treatment.
5. Historical Mistrust: Historically, there have been unethical practices in medical research, resulting in mistrust from certain communities towards researchers and medical institutions. This mistrust can deter participation in clinical studies and even seeking care when needed.
To address these disparities effectively, it is essential that researchers, CROs, pharmacologic therapy sponsors, and community care activists adopt multi-pronged strategies that aim to enhance participation across diverse populations. There are several important areas to continue focusing on to maximise recruitment and engagement across the lifespan of a clinical trial:
Building Community Trust and Engagement Trust is the recognised cornerstone of effective community engagement. Establishing relationships with community leaders, religious and social centers, and health advocates can help bridge the gap between researchers and potential study participants. Collaborative efforts should specifically focus on:
• Developing Broader Community Partnerships: Working closely with community organisations, religious and social programmes, and even educational settings such as schools and their health outreach teams, can all foster opportunities to engage parents and caregivers of children with rare diseases. This process will ultimately co-develop key elements of research protocols, fostering culturally sensitive approaches to participant recruitment and retention, and supporting better study outcomes.
• Education and Awareness Initiatives: By enhancing greater awareness about clinical trials through community-based campaigns, researchers and study sponsors can emphasise
the safety of the clinical protocols, promote a balanced understanding of potential benefits against the possible risks, and the significance of participation for ensuring better developmental progress.
• Transparency in Communication: Providing clear, transparent information about study goals, processes, and outcomes will build greater confidence and trust among communities. This will further help caregivers experience greater confidence in the meaningfulness of their child’s participation.
Reducing Socioeconomic and
Eliminating socioeconomic and geographic obstacles can significantly improve participation rates. This can be achieved through:
• Financial Support: Offering stipends, travel reimbursements, and childcare services to alleviate some of the financial burdens faced by participating families.
• Flexible Scheduling: Offering flexible appointment times, including evenings and weekends, to accommodate the schedules of working families.
• Telemedicine and Decentralised Trials: Utilising digital health technologies and remote monitoring to bring clinical trials to participants’ homes, reducing travel and geographic barriers.
• Mobile Clinics: Establishing mobile clinics to reach rural communities and underserved areas to ensure wider accessibility to clinical trials.
Culturally competent care and research practices are crucial for inclusive participation. Strategies to improve cultural competence include:
• Training for Researchers and Clinicians: Providing cultural competence training to researchers and healthcare providers ensures respectful and sensitive interactions with diverse populations.
• Linguistic Accessibility: Offering study materials and consent forms in multiple languages and providing interpreters can help overcome language barriers that can limit the understanding of the clinical trial and its possible impact on developmental status.
• Community Review of Protocols: Bringing together caregivers and clinicians from the communities where participants are sought and reviewing the study prior to recruitment allows for better understanding of the study, the risks and benefits, and foster conversations that enhance caregiver and participant willingness to step forward and accept the opportunity being presented to them.
Designing ethically sound and inclusive research protocols is vital for reaching underserved populations. Emphasis should be placed on:
• Equitable Inclusion Criteria: Ensuring that inclusion criteria for clinical trials does not disproportionately exclude certain populations or limit the range of intersectionality important participants from the study (e.g., gender and sexual orientation diversity).
• Ethical Oversight: Establishing robust ethical oversight mechanisms to safeguard participant rights and address historical mistrust.
• Community Advisory Boards: Involving community representatives in advisory boards to ensure study designs address the needs and concerns of diverse communities. These boards have been key in guiding appropriate patients and their families to available studies.
Addressing disparities in clinical research participation requires global efforts and collaboration among various stakeholders, including governments, research institutions, pharmaceutical companies, and non-profit organisations. Key actions include:
• International Collaboration: Engaging in international collaborative research initiatives to ensure representation from diverse global populations.
• Capacity Building in Developing Countries: Investing in research infrastructure and capacity building in developing countries can enable local participation in clinical trials and enhance global health equity.
Addressing disparities in clinical research participation for genetically based rare paediatric and neurodevelopmental diseases is imperative for achieving equitable health outcomes. The current disparities in representation discussed above pose a significant challenge to the development and validation of effective treatments. However, by building community trust, reducing barriers, enhancing cultural competence, designing ethical research protocols, and fostering global collaborations, we can move towards a more inclusive and equitable research landscape and increase the opportunity for enhanced developmental outcomes. Ultimately, inclusive clinical research will lead to the development of treatments that are effective across diverse populations, ensuring that all children, regardless of their background, have access to the best possible care and improved health outcomes. As we continue to advance in our understanding of genetically based diseases, it is our collective responsibility to ensure that no child is left behind in the quest for better health and quality of life.
1. Center for Information & Study on Clinical Research Participation (CISCRP). (2019). Public and Patient Perceptions and Insights Study. https:// www.ciscrp.org/wp-content/uploads/2019/05/CISCRP_Perceptions_ Insights_Report_2019.pdf
2. European Medicines Agency (EMA). (2009). Reflection Paper on the Investigation of Children’s Participation in Clinical Trials. https://www. ema.europa.eu/en/documents/scientific-guideline/reflection-paperinvestigation-childrens-participation-clinical-trials_en.pdf
3. Global Genes. (2021). Understanding Patient Needs and Priorities for IRB (Institutional Review Board) Review of Rare Disease Clinical Trials. https:// globalgenes.org/resources/understanding-patient-needs-and-prioritiesfor-irb-review-of-rare-disease-clinical-trials/
4. National Organization for Rare Disorders (NORD). (2020). Barriers to Rare Disease Diagnosis, Care, and Treatment in the US: A 30-Year Comparative Analysis. https://rarediseases.org/rare-advocacy-skills-partnership-barriersto-rare-disease-diagnosis-care-treatment-in-the-us-study/
5. U.S. Food and Drug Administration (FDA). (2020). Enhancing the Diversity of Clinical Trial Populations — Eligibility Criteria, Enrollment Practices, and Trial Designs Guidance for Industry. https://www.fda.gov/media/127712/ download
6. World Health Organization (WHO). (2018). International Clinical Trials Registry Platform (ICTRP). Guidance for Clinical Trial Registers. https:// www.who.int/ictrp/network/guidelines/en/
7. Baynam, G., Baker, S., Steward, C., Summar, M., Halley, M., & Pariser, A. (2024). Increasing diversity, equity, inclusion, and accessibility in rare disease clinical trials. Pharmaceutical Medicine, 38, 261-276. https://doi. org/10.1007/s40290-024-00529-8
8. Brewster, R.C.L., Steinberg, J.R., Magnani, C.J., et al. (2023). Race and ethnicity reporting and representation in pediatric clinical trials. Pediatrics, 151(4), e2022058552. https://doi.org/10.1542/peds2022-058552
9. Krause, D. S., & Subbiah, V. (2020). Enhanced participation of diverse populations in rare disease trials. Nature Reviews Drug Discovery, 19, 495496. https://doi.org/10.1038/d41573-020-00063-z
10. Symonds, J.D., Elliott, K.S., Shetty, J., et al. (2021). Early childhood epilepsies: epidemiology, classification, aetiology, and socio-economic determinants. Brain, 144, 2879-2891. https://doi.org/10.1093/brain/awab162
11. Wojcik, M.H., Bresnahan, M., del Rosario, M.C., Ojeda, M.M., Kritzer, A., & Fraiman, Y.S. (2023). Rare diseases, common barriers: disparities in pediatric genetic outcomes. Pediatric Research, 93, 110-117. https://doi. org/10.1038/s41390-022-02240-3
Scott J. Hunter is a senior scientific expert in neurodevelopmental disorders and rare paediatric diseases for WCG Clinical. He was on the faculty at the University of Chicago for 22 years, where he was a professor of psychiatry, behavioural neuroscience and paediatrics, and director of neuropsychology for UChicago Medicine. He was chair of the department’s Diversity, Equity, and Inclusion Committee. A fellowship-trained clinical medical ethicist, he was vice chair of the UChicago Medicine and Biological Sciences Institutional Review Board and a faculty member with the MacLean Center for Clinical Medical Ethics. Hunter served as the 2022 chair of the American Psychological Association’s Board for the Advancement of Psychology in the Public Interest and the 2014 chair of the Committee on Professional Practice and Standards with the APA Practice Directorate. A co-editor of four well-regarded textbooks addressing neuropsychological development and practice with Cambridge University Press, and the recent publication “Applied Multiculturalism,” published by APA Press, Hunter has authored numerous research articles in peer-reviewed publications, as well as served on multiple editorial boards and as chief editor for Behavioural Sciences.
A New Perspective on Alzheimer's Research Alzheimer's disease (AD) presents a significant challenge, not only for those diagnosed but also for their families and healthcare providers. As the global population ages, the urgency for effective diagnostic tools and treatment strategies intensifies. The BioHermes study, sponsored by the Global Alzheimer's Platform Foundation (GAP) and supported by IXICO as imaging partner, offers valuable insights into emerging possibilities for efficient and reliant determination of a participant's amyloid status, a key part of diagnostic and trial recruitment criteria.
This article delves into how data from the Bio-Hermes study7 enhances our understanding of blood-based biomarkers (BBMs) for p-tau217,6 as a complement to amyloid positron emission tomography (PET),8 a gold-standard imaging technique for the measurement of amyloid pathology. By examining the interplay between imaging and BBMs, we highlight how BBMs offer a more accessible, cost-effective, and less invasive alternative where appropriate, acknowledging the need for a gold-standard assessment from PET in certain cases.
This study in over 1000 community-based participants from throughout the US compared the results of blood and digital biomarkers with brain amyloid PET scans or cerebrospinal fluid assays. The study revealed a strong correlation between p-tau217 and the presence of amyloid plaques in the brain, a diagnostic hallmark of Alzheimer's disease (AD), with several blood-based biomarkers (BBM's).3 This relationship was demonstrated across the entire study population including the 24% of Bio-Hermes participants from African American, latino and other traditionally underrepresented communities, an unprecedented level of diversity. These findings will enhance the field’s ability to provide a more economical, timely, and accurate diagnosis of Alzheimer’s disease and speed enrolment into clinical trials.
With its rich dataset from a highly diverse population, the Bio-Hermes study offers a valuable source to enhance our understanding of how BBMs may impact future AD clinical trials. In a two-stage screening process9,10 potential trial participants initially undergo a blood-based biomarker test, e.g., measuring plasma amyloid beta (Aβ)40, Aβ42, total tau, phosphorylated tau (p-tau)181, or p-tau217. Those with no clear diagnosis from the BBM test will subsequently undergo an amyloid PET scan. Depending on the screening paradigm, individuals with a positive BBM test outcome may also receive a confirmatory PET measurement. This two-stage screening process reduces screen failure rates associated with invasive and more costly amyloid PET and thereby improves the efficiency of identifying suitable candidates for clinical trials.
A key objective of the Bio-Hermes study is its commitment to inclusivity. Approximately 2,500 participants were prescreened for possible inclusion in the core study, with 956 receiving an Amyloid PET
Scan, and 924 participants had all the measurements taken required to simulate the proposed two-stage screening process. With a focus on traditionally underrepresented populations, of these 924 participants included here, 104 were Black or African American, 803 White and 16 Asian participants. Moreover, 11% of the included participants identified as Hispanic or latino. This diversity is essential for ensuring that the findings are applicable across various demographic groups, reflecting the realities of those affected by Alzheimer's disease.
To characterise a BBM's ability to accurately detect amyloid pathology, it’s sensitivity and specificity in predicting the goldstandard assessment from PET imaging in a reference database can be considered. Based on the application, different levels of prediction confidence are required for BBMs. Brum et al3,10 have proposed to consider levels for sensitivity and specificity of 90%, 95%, and 97.5%. In this study, we have validated in the Bio-Hermes dataset the ability of p-tau217 to predict the reference assessment from amyloid PET at the 95% level. It is important to note that p-tau217 tests are not uniform; different manufacturers and measurement techniques evaluate various aspects of the protein, leading to variations in their reported accuracy.
When deploying the 95% sensitivity/specificity thresholds to defining a cut-off for the BBM in an independent dataset, pTau217 classifies 67% of cases as clearly amyloid-positive or amyloid-negative, resulting in an overall accuracy of 92%. The remaining 33% fall into an ambiguous range, requiring follow-up with amyloid PET. These results show that when deploying a two-stage screening tool with the described characteristics, the BBM can provide a reliable response in approximately two thirds of the cases and the invasive amyloid PET is only required in the remaining third of cases.
The Bio-Hermes study was designed to study biomarker differences between racial and ethnic groups and has revealed differences in p-tau217 levels among different racial and ethnic groups. Comparing p-tau217 in amyloid positive participants (as defined by gold-
standard PET) in the White group to the Black or African American group, reveals statistically significant differences between BBMs or differences in the sensitivity of the the BBMs. These findings extend earlier observations of differences in amyloid positivity rates between these racial groups, despite comparable levels of cognition.2
Better understanding these racial and ethnic group differences is crucial for several reasons. Firstly, it highlights the need for tailored diagnostic approaches that consider the unique biological, socio-economic, and comorbidity factors influencing different populations. For instance, variations in p-tau217 levels may reflect underlying differences in genetic predispositions, environmental exposures, differences in lifestyle or diet, or access to healthcare resources. Moreover, these findings underscore the importance of inclusivity in Alzheimer's disease research. By ensuring that diverse populations are represented in clinical studies, researchers can better understand the nuances of Alzheimer's disease and assoicated
diagnostic tools, and develop interventions that are effective for all individuals. This focus on differences not only enhances the validity of research findings but also promotes health equity in the diagnosis and treatment of Alzheimer's disease.
The findings from this analysis underscore the significance of bloodbased biomarkers, such as p-tau217, in Alzheimer's disease diagnosis and screening. The study demonstrated that elevated levels of p-tau217 can effectively serve as a first-stage screening measure for the identification of amyloid positive participants as measured through gold-standard PET imaging. The observed differences in p-tau217 levels among various racial and ethnic groups raise important questions about the underlying factors contributing to these variations. Understanding these factors is crucial for developing targeted interventions and ensuring that all communities benefit from advancements in Alzheimer's research.
The implications of the Bio-Hermes study and the analysis presented here extend beyond immediate clinical applications. The study paves the way for more accurate and efficient diagnostic processes. The twostage screening process validated in this analysis could lead to earlier detection of Alzheimer's disease, allowing for timely interventions that may slow disease progression and improve patient outcomes. The proposed change to framework emphasises the importance of using imaging as a complementary tool to blood-based biomarkers. By leveraging the strengths of both approaches, healthcare providers can enhance diagnostic accuracy and tailor treatment strategies to individual patients.
As we reflect on the Bio-Hermes study, it is essential to consider the broader context of Alzheimer's disease research. The integration of imaging and blood-based biomarker analysis represents a significant advancement in our understanding of Alzheimer's disease. This shift towards a more holistic approach to diagnosis and treatment aligns with the growing recognition of the need for personalised medicine in neurodegenerative diseases.
Furthermore, the emphasis on inclusivity in the Bio-Hermes study aligns with a broader movement within the scientific community to address health disparities and promote equitable access to research findings. By ensuring that diverse populations are represented in clinical studies, researchers can better understand the nuances of Alzheimer's disease and develop interventions that are effective for all individuals.
The Bio-Hermes study marks a pivotal step towards advancing our understanding of Alzheimer's disease through the lens of imaging and biomarker research. By prioritising the integration of BBMs like p-tau217 with advanced imaging techniques, this study sets a precedent for future investigations aimed at combating this complex disease.
As we move forward, it is crucial to maintain a focus on the human element of Alzheimer's research. Behind every data point is a person – a loved one, a caregiver, a community. The Bio-Hermes study not only advances our scientific understanding but also calls us to action: to continue pushing for inclusivity in research, to advocate for early diagnosis, and to foster hope in the fight against Alzheimer's disease. Together, we can illuminate the path forward.
1. Joules, et al., 2024. A multi-stage approach to screen Amyloid status using plasma p-tau217 prior to confirmatory Imaging applied to the Bio-Hermes Trial. CTAD.
2. Brum, W.S., 2023. Varying thresholds based on patient and care setting. Nature Aging, 3, pp.1079-1090.
3. Mohs, R., et al., 2024. The Bio-Hermes Study: Biomarker database developed to investigate blood-based and digital biomarkers in community-based, diverse populations clinically screened for Alzheimer's disease. Alzheimer's & Dementia, 20(4), pp.2752-2765.
4. Joules, et al., 2024. A multi-stage approach to screen Amyloid status using plasma p-tau217 prior to confirmatory Imaging applied to the Bio-Hermes Trial. CTAD.
5. Joules, et al., 2024. A multi-stage approach to screen Amyloid status using plasma p-tau217 prior to confirmatory Imaging applied to the Bio-Hermes Trial. CTAD.
6. Palmqvist S, Janelidze S, Quiroz YT, Zetterberg H, Lopera F, Stomrud E, Su Y, Chen Y, Serrano GE, Leuzy A, Mattsson-Carlgren N, Strandberg O, Smith R, Villegas A, Sepulveda-Falla D, Chai X, Proctor NK, Beach TG, Blennow K, Dage JL, Reiman EM, Hansson O. Discriminative Accuracy of
Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA. 2020 Aug 25;324(8):772-781. doi: 10.1001/jama.2020.12134. PMID: 32722745; PMCID: PMC7388060.
7. Beauregard, D.W., Mohs, R., Dwyer, J., Hollingshead, S., Smith, K., Bork, J. and Kerwin, D.R., 2022. Bio-Hermes: A Validation Study to Assess a Meaningful Relationship Between Blood and Digital Biomarkers with Aβ PET Scans for Alzheimer’s Disease. Alzheimer's & Dementia, 18, p.e063676.
8. Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, Pontecorvo MJ, Hefti F, Carpenter AP, Flitter ML, Krautkramer MJ, Kung HF, Coleman RE, Doraiswamy PM, Fleisher AS, Sabbagh MN, Sadowsky CH, Reiman EP, Zehntner SP, Skovronsky DM; AV45-A07 Study Group. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011 Jan 19;305(3):275-83. doi: 10.1001/jama.2010.2008. Erratum in: JAMA. 2011 Mar 16;305(11):1096. Reiman, P Eric M [corrected to Reiman, Eric M]. PMID: 21245183; PMCID: PMC7041965.
9. Ashton NJ, Brum WS, Di Molfetta G, Benedet AL, Arslan B, Jonaitis E, Langhough RE, Cody K, Wilson R, Carlsson CM, Vanmechelen E, MontoliuGaya L, Lantero-Rodriguez J, Rahmouni N, Tissot C, Stevenson J, Servaes S, Therriault J, Pascoal T, Lleó A, Alcolea D, Fortea J, Rosa-Neto P, Johnson S, Jeromin A, Blennow K, Zetterberg H. Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology. JAMA Neurol. 2024 Mar 1;81(3):255-263. doi: 10.1001/jamaneurol.2023.5319. PMID: 38252443; PMCID: PMC10804282.
10. Brum WS, Cullen NC, Janelidze S, Ashton NJ, Zimmer ER, Therriault J, Benedet AL, Rahmouni N, Tissot C, Stevenson J, Servaes S, Triana-Baltzer G, Kolb HC, Palmqvist S, Stomrud E, Rosa-Neto P, Blennow K, Hansson O. A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases. Nat Aging. 2023 Sep;3(9):1079-1090. doi: 10.1038/s43587-023-00471-5. Epub 2023 Aug 31. PMID: 37653254; PMCID: PMC10501903.
Robin Wolz is the Chief Scientific Officer at IXICO. He has over 15 years of experience in the development of innovative analytics solutions in healthcare with a focus on imaging technology. Prior to IXICO, Robin held various roles at Philips in the Research and Diagnostic X-Ray divisions. He holds a PhD in medical imaging and computer science from Imperial College London, with a focus on the early detection of Alzheimer's disease. Robin is the co-author of more than 100 publications and holds multiple patents in the field of medical imaging and AI data analytics.
Q1: Why are highly potent active pharmaceutical ingredients (HPAPIs) playing a transformative role in oncology drug development?
A: The rise of precision oncology has elevated HPAPIs to a pivotal role in cancer treatment. These compounds are uniquely effective at delivering therapeutic benefits at extremely low doses, which minimises toxicity compared to traditional cancer therapies like chemotherapy. Because of this, they can be used in a variety of ways including standalone therapies, antibody-drug conjugates (ADCs), and immuno-oncology agents. This versatility, combined with their effectiveness, has consequently led to HPAPIs being included in about 60% of oncology drugs today, underscoring their significance in modern-day cancer care.
HPAPIs also represent a shift towards more targeted therapies, where treatment efficacy is maximised, and adverse effects are reduced. This capability has been particularly beneficial in addressing cancers that were previously deemed too challenging to treat effectively, which broadens the horizons of oncology drug development.
Q2: What makes HPAPIs uniquely suited to oncology, and how is this shaping their manufacturing processes?
A: HPAPIs are particularly well-suited to oncology because of their ability to target diseased cells while sparing healthy tissue. This precision aligns with the growing demand for therapies that balance efficacy and patient safety. As oncology continues to evolve as a therapeutic area, the need for highly targeted treatments has made HPAPIs increasingly valuable.
From a manufacturing perspective, the production of HPAPIs requires strict containment strategies and specialised facilities. Traditional open processing methods are inadequate for these compounds due to the risks of exposure to the CDMO’s operators, which means closed systems and advanced technologies are required to ensure worker and patient safety. For example, the handling of these substances often involves the use of negative pressure environments, isolators, and automated cleaning systems – essentially having
facilities with engineered containment strategies built into their design – to operator exposure and significantly reduce cross contamination risk. Consequently, manufacturing HPAPIs demands both scientific and engineering expertise and tailored operational capabilities to meet these exacting requirements.
Q3: How does the growing demand for HPAPIs impact the pharmaceutical manufacturing landscape?
A: The increasing adoption of HPAPIs is driving significant changes in pharmaceutical manufacturing. For example, some reports suggest that the oral solid dosage (OSD) market, which includes highly potent OSD products, could surpass $72 billion by 2030. Similarly, the ADC market – an emerging and highly targeted therapy which leverages HPAPIs linked to monoclonal antibodies to target cancerous cells –is expected to reach $7 billion by 2035. These trends highlight the need for manufacturing facilities that can accommodate both smallscale clinical production and large-scale commercial batches for a range of dosage forms, reflecting the demands for both innovation and scalability.
This surge in demand also underscores the importance of collaboration between pharmaceutical companies and experienced CDMOs. As the industry grows more complex, companies with specialised containment capabilities and scalable solutions are better positioned to support the lifecycle of HPAPI-based products, from early development to commercialisation.
Q4: What challenges do CDMOs face in ensuring safe and efficient HPAPI drug product production?
A: Handling HPAPIs comes with unique and complex challenges, particularly in ensuring the safety of workers and the environment. One of the most significant hurdles is maintaining robust containment to prevent exposure. This means operating in highly controlled facilities equipped with enclosed processing systems, where direct observation is often replaced by advanced monitoring technology. Additionally, cross-contamination must be rigorously avoided, which calls for validated cleaning protocols and the use of negative-pressure environments to keep contaminants contained.
Another critical challenge is scalability. Many HPAPI drug products are produced in small batches due to their low-dose requirements, but when commercial demand increases, CDMOs must scale up production without compromising safety. Balancing the precise safety measures needed for small-scale operations with the infrastructure and training required for larger-scale production often demands substantial investment and careful planning.
Q5: Could you elaborate on the protocols and technologies that enable safe HPAPI handling in drug product production?
A: Safe handling of HPAPIs relies on the CDMO having robust containment systems that are supported rigorous protocols. For instance, dispensing operations should be conducted within rigid or flexible isolators, using methods like split butterfly valves to minimise exposure during material transfer. Process such as wet and dry granulation, blending, encapsulation and tableting should also employ enclosed systems with isolator units and/or negative pressure, with cleaning processes that utilise automated clean-in-place or wash-inplace systems, both of which mitigate the risk of cross-contamination and operator safety.
Moreover, these protocols extend to waste management, ensuring that compounds do not contaminate the water supply, thereby protecting both workers and the broader environment. The integration of advanced technologies, such as real-time monitoring systems and predictive analytics, further enhances the safety and efficiency of HPAPI manufacturing.
Q6: How do antibody-drug conjugates (ADCs) represent a novel approach to utilising HPAPIs?
A: ADCs exemplify the innovative potential of HPAPIs in oncology. These therapies combine a monoclonal antibody with an antineoplastic payload, allowing for the precise delivery of the payload to the target cells while sparing healthy tissue. The U.S. FDA has already approved around 15 ADCs, reflecting their growing importance in cancer treatment. However, their unique composition requires tailored manufacturing processes, such as negative pressure isolator technology, which is used during formulation to ensure containment; and positive pressure applied during aseptic filling to maintain sterility, followed by negative pressure for vial cleaning to prevent contamination.
The development of ADCs also highlights the importance of collaboration between pharmaceutical companies and CDMOs. By leveraging their expertise in complex biologics and containment technologies, CDMOs play a vital role in advancing ADCs from concept to market, ensuring that these cutting-edge therapies reach patients safely and efficiently.
Q7: What role does a quality management system (QMS) play in the development of HPAPI products?
A: A robust QMS is indispensable for the development of drug products containing HPAPI, as it ensures compliance with regulatory standards and safeguards both product quality and operator safety. Key aspects of a QMS include the establishment of occupational exposure limits (OELs) to define safe handling thresholds, conducting risk assessments to identify potential hazards, and implementing stringent quality assurance (QA) measures to prevent crosscontamination and protect operators.
In addition, quality control (QC) processes are adapted to handle HPAPIs safely. For example, analytical methods like Karl Fischer titration are modified to minimise analyst exposure during testing. By integrating these measures, a QMS supports the efficient and safe
development of HPAPI products while ensuring compliance with Good Manufacturing Practice (GMP) standards.
Q8: How does Design of Experiment (DoE) contribute to optimising high potent development?
A: DoE is a statistical approach that enhances product and process development by systematically evaluating the relationships between input and output variables. In the context of HPAPIs, DoE allows researchers to identify critical factors influencing product quality and establish optimal operating conditions, which helps reduce the number of experimental runs, saving clients and their CDMO partner valuable time and resources.
By adopting DoE early in the development lifecycle, CDMOs can create robust processes that streamline production and ensure consistent product quality. This approach not only improves efficiency but also enhances the scalability and reliability of HPAPI manufacturing, ensuring that therapies reach patients without delay.
Q9: Looking ahead, what does the future hold for HPAPIs in oncology?
A: The future of HPAPIs in oncology looks incredibly promising. Their ability to target cancer cells with precision makes them invaluable across various therapeutic modalities, including oral solids, immunotherapies, and ADCs. But at the demand increases, CDMOs need to work hard to continue meeting that demand. Investing in engineered containment technologies and scalable facilities is vital, and the ability to offer integrated services that span early development to commercialisation is increasingly sought after by clients within the pharma and biopharma space. Greater capabilities allows the CDMO to adapt to emerging trends, such as combination therapies and patient-centric drug delivery methods.
By aligning their capabilities with these needs, CDMOs can accelerate the development of life-changing oncology therapies while ensuring safety and quality at every stage. As the global oncology landscape continues to evolve, the role of HPAPIs will only grow, offering new hope for patients facing challenging diagnoses.
David O’Connell is the Director of Scientific Affairs at PCI Pharma Services. After graduating from Glasgow Caledonian University with a BSc. in Applied Bioscience, David spent seven years as a Supervisory Scientist working for Aptuit in Edinburgh before moving to Penn Pharma as Head of Formulation Development in 2009. Here he played a vital part in the design of the potent Contained Manufacturing Facility (CMF), which won the ISPE Facility of the Year award for Facility Integration (2014). In 2013 David took on the role of Director, Pharmaceutical Development at the PCI site in Tredegar and in 2017 became PCIs Director of Scientific Affairs.
Effective monitoring of medication adherence is vital for the success of any clinical trial. Yet too often archaic, biased methods are used for compliance monitoring. Dr. Bernard Vrijens from AARDEX Group explores new guidance on clinical trial quality and argues digital adherence monitoring should be a ‘no-brainer’.
Failing to effectively monitor adherence in clinical trials can have far-reaching implications – from an inability to provide reliable safety and efficacy data1 to significantly increasing costs2,3 and delaying access to treatment. Yet, despite its obvious importance, there is huge variability in the assessment and reporting of medication adherence in clinical trials.4
New guidance for clinical trial quality makes it clear that medication adherence is a must have5 alongside randomisation, blinding and appropriate statistical analysis.6 However, a policy gap remains because no one is asking for a valid method to measure adherence.7
To ensure medications are effective, and make clinical trials more efficient, we need to move beyond out-of-date methods and adopt digital adherence monitoring.8
The latest version of ICH E6(R3) was published in January 2025. It said strategies should be implemented to avoid, detect, address and prevent the recurrence of serious noncompliance with good clinical practice (GCP), the trial protocol and applicable regulatory requirements. Appendix B. highlights the need to mitigate and eliminate risks to the safety and wellbeing of patients and the reliability of data. This includes strategies to monitor the participant’s adherence to treatment.
In 2024, the World Health Organization (WHO) published new guidance on best practice in clinical trials. The guidance, which advocates risk-based and proportionate approaches, aims to enhance clinical research efficiency and support sustained clinical trials that are always functional for endemic conditions and can pivot in times of emergency.
It highlights the need to facilitate and encourage adherence and ensure data collection, storage, exchange and access enables reliable and consistent analyses. The guidance also highlights the potential risks of low adherence. For example, in a randomised controlled trial (RCT) if participants receiving the active intervention are nonadherent, the ability to assess any difference in outcome between that arm and a placebo arm of the trial is reduced. This increases the risk of a false conclusion being drawn about whether a meaningful difference exists between the interventions.
The WHO guidance also says monitoring should take a riskbased proportionate approach which focuses on the issues that will make a “material difference” to the participants in the trial and the reliability of the results. This includes adherence to the allocated intervention.
However, despite this recognition of the importance of medication adherence, and the need to reliably monitor and analyse data, there remains a clear policy gap – no one is saying how we measure it.
Thirty years ago, a sealed envelope was the primary method of randomisation. Of course, it was flawed because investigators could see through the envelope and now the process has been improved by being fully digitalised.
However, when it comes to medication adherence, many clinical trials are still using archaic, biased methods of monitoring such as pill counting or patient self-report, which lack the sensitivity to detect non-adherence.
There are also flaws in the way medication adherence is commonly computed. Typically, a number is given for the whole monitoring period – from first dose to end of protocol. This provides an aggregate measure of adherence. However, this approach fails to reflect the different behaviours a participant will have throughout the course of a trial and can result in participants with diverse behaviours being given the same adherence number. This limits the ability to identify participants requiring an intervention to maintain adherence.
So, what can we do to improve medications adherence monitoring?
The WHO has said digital technology can improve the relevance and completeness of information collected during a clinical trial and reduce the burden on both trial staff and participants.
Digital adherence monitoring allows us to identify and focus on the metrics and risk indicators that matter the most. It is easy, frictionless and provides reliable and complete data. It enables healthcare teams and researchers to map real-life, rather than estimated, adherence and provides accurate, objective results. This increases understanding of participant habits and detection of issues and allows the recommendation of appropriate interventions before data reliability or trial efficiency is compromised.
There are two key pieces to this puzzle – smart packaging and cutting-edge software.
Unlike traditional methods, smart packaging can accurately record dosing events. Products like smart pill bottles, smart blister
packs, and smart drug delivery devices (e.g. injectables or inhalers) feature an electronic microcircuit that automatically generates a timestamp every time the participant takes or applies their medication. This information is stored in the packaging’s memory before being transferred to a central system for analysis and interpretation.
Smart packaging and device monitoring is 97% accurate compared to 70% accuracy for drug levels and markers, 60% for pill counts, 50% for healthcare professional ratings, and 27% for patient self-reporting, including electronic patient diaries.7
When provided with this more accurate data, algorithms and data visualisations allow healthcare teams and researchers to spot typical behaviour changes, such as drug holidays or different intake times, and offer insight on whether these are within a normal range or cause for concern. This empowers them to work with participants to provide personalised advice and develop appropriate interventions.
Conclusion
The current clinical trial landscape, including an increase in
decentralised clinical trials and the latest regulatory guidance, has removed barriers to digitalisation for the benefit of all stakeholders. The WHO has said automated and digital processes should be encouraged and supported globally to increase clinical trial speed, efficiency and transparency.6
Digital adherence monitoring is a proven, validated approach which should be included in all clinical trials if we want to improve efficacy and safety data, support patients to adhere to interventions and reduce clinical trial costs. It is a no-brainer to make it the norm across all clinical trials.
1. https://academic.oup.com/eurheartj/article/40/25/2070/5050879
2. https://pmc.ncbi.nlm.nih.gov/articles/PMC7098872/#bcp14240bib-0001
3. https://aardexgroup.com/clinical-trial-failures-are-we-counting-thecost-of-poor-adherence/
4. https://www.amjmedsci.org/article/S0002-9629(15)32453-8/abstract
5. https://database.ich.org/sites/default/files/ICH_E6%28R3%29_Step4_ FinalGuideline_2025_0106.pdf
6. https://www.who.int/publications/i/item/9789240097711
7. https://pubmed.ncbi.nlm.nih.gov/24739446/
8. https://aardexgroup.com/is-the-data-from-randomised-clinical-trialsrandom-to-the-point-of-pointless/
Dr. Bernard Vrijens is the Scientific Lead at AARDEX Group and the Invited peodsssed of Biostatistics at Liège University. He holds a PhD from the Department of Applied Mathematics and Informatics at Ghent University, Belgium. He currently leads a research programme investigating (a) the most common errors in dosing using a simple but robust taxonomy, (b) particular dosing errors that can jeopardise the efficacy of a drug, and (c) the optimal measurementguided medication management programme that can enhance adherence to medications and maintain long-term persistence.
Dr. Vrijens is also the co-author of seven book chapters, over 100 peer-reviewed scientific papers, and named as inventor on 6 patents. He is a founding member of the International Society for Medication Adherence (ESPACOMP), and an active member of several EU- and US-funded collaborative projects around the theme of adherence to medications.
Over the years, medical device has undergone significant transformation, with the use of evolving technological tools such as artificial intelligence (AI), machine learning (ML) and Internet of Things. In Malaysia, all medical devices are required to be registered with the Medical Device Authority (MDA), in compliant to the Medical Device Act 2012 (Act 737), before placing in Malaysian market. As for medical device clinical trial, a Medical Device (Exemption) Order 2016 grants exemptions from registration for medical devices designated for clinical investigations or other clearly specified purposes. Through medical device clinical trials, the safety, effectiveness, and performance of the devices could be evaluated before approval for general use and in post-marketing evaluations.1 While pharmaceutical trials take up the largest share of trials conducted in Malaysia, the potential for medical device clinical trials is an opportunity to be tapped into, particularly with the current robust clinical trial ecosystem in the country. Therefore, this article will be discussing on the current landscape and future outlook of medical device trials in Malaysia.
Regulatory Overview
Medical devices that are intended for clinical investigational use (CIU) or clinical research use (CRU) must follow the process of notification of exemption from Registration of Medical Devices. The review timeline for CIU and CRU is approximately 30 and 14 working days respectively, yet it is important to ensure that all assessment data and required documents are completely compiled according to the guidance document for a successful application. All medical device trials follow the same pathway.2
Malaysia's Medical Device Clinical Trials Landscape
Malaysia's diverse healthcare landscape presents extensive opportunities for conducting clinical trials across a wide range of therapeutic areas. Based on Figure 1, a total of 46 interventional medical device trials were conducted in the last 6 years.3 Cardiology/ vascular diseases stands out as the leading area, with 18 medical device trials (Figure 2) contributed by the volume of innovations addressing cardiovascular health which is a critical focus in global healthcare.
These trials were conducted across multiple sites in the public, private and academic centres which are staffed by experienced principal investigators and supportive study team as well as being well-equipped thus ensuring clinical trial are delivered with speed, quality and reliability.
The National Heart Institute has conducted various clinical trials for medical devices, having participated in over 10 trials, including those focused on coronary stents, heart valves, as well as a firstin-man trial.4 Hospitals in the public sector such as Pulau Pinang Hospital, Queen Elizabeth II Hospital and Sultan Idris Shah Hospital have also participated in multiregional clinical trials in which the data generated was used for regulatory submissions.
In this day and age, the incorporation of artificial intelligence (AI) into medical devices has completely transformed the healthcare sector by increasing diagnostic precision especially in early disease detection. As an example, the successful identification of Malaysia's first lung cancer case was identified using AI which saw the public and private partnership between a local organisation with global pharmaceutical company.5 This accomplishment highlights how AI medical device will continue to evolve, presenting various opportunities for patients and healthcare professionals in the country to tap into.
The recently published IQVIA report "Rethinking Clinical Trial Country Prioritization" highlights Malaysia as a promising destination for medical device clinical trials due to its diverse patient population, strong regulatory framework, including streamlined processes for unregistered devices, advanced healthcare infrastructure and experienced clinical workforce. Positioned in the Opportunity Tier alongside other Southeast Asian countries like Thailand, Vietnam, and Indonesia, Malaysia offers significant potential for patientintensive trials with minimal concerns about trial saturation. The report also ranks Malaysia among the Top 30 globally and leading in Southeast Asia for clinical trial readiness. Moreover, Malaysian clinical investigators have also been recognised by the clinical research industry as top and first global/regional recruiters in multiregional clinical trials.6
The establishment of Clinical Research Malaysia (CRM) as a global trusted research management organisation has also significantly shaped the clinical research landscape in the country. CRM as the national one-stop centre and site management organisation for the Ministry of Health has also been recognised in an article by Syneos Health for its contribution in driving the clinical research agenda of the country as well as delivering quality trials from global sponsors.7 Together with a robust regulatory framework, streamlined clinical trial process and well-equipped infrastructure at trial sites, Malaysia is being viewed as the hub for global clinical trials.
Malaysia is emerging as a dynamic hub for medical device trials with forward-thinking regulatory initiatives. MDA has outlined the "Innovative Medical Device Pathway Framework" to foster innovation and streamline the regulatory process for medical devices to be applied soon. This framework, which will be implemented soon, comprises two main initiatives. Initiative 1 is the Innovative Devices Review Service which provides guidance on safety and performance principles for devices at varying development stages. It includes two components: Initiative 1A - Early Development Review and Initiative 1B – Pre-commercial Review. Next, Initiative 2 complements the Initiative 1B – Pre-Commercial Review pathway by offering two key components: a facilitated Registration Exemption Notification for clinical investigations, and a compliance conclusion letter from MDA. This strengthens funding applications to the National Technology & Innovation Sandbox (NTIS) under the Ministry of Science, Technology and Innovation (MOSTI). Malaysia’s national sandbox programme allows for relaxed regulatory framework in which medical device trials can be conducted. MDA can also act
as the sandbox coordinator for applicants to gather essential safety and performance data during clinical trials. In a nutshell, the entire framework is an intervention strategy to assist researchers in novel R&D development of medical devices by offering early regulatory knowledge input and support the growth of Malaysia's medical device sector.8
An opportunity to align with global trends in medical devices specifically in AI/ML, lies in streamlining international standards, enabling faster approvals and facilitating smoother global operations. Medical device clinical trial regulations differ significantly across countries, reflecting varying risk classification systems, approval pathways, and monitoring mechanisms. FDA provides detailed guidance for high-risk devices, including specific frameworks for Software as a Medical Device (SaMD)9 and AI/ML technologies, with premarket review process like Premarket Clearance, De Novo Classification and Premarket Approval (PMA).10 Apart from the current regulatory process of general medical devices, MDA may adopt detailed SaMD and AI/ML specific frameworks and harmonise regulations or the process for assessment and supervision of clinical trials to increase efficiency, foster innovation, and ensure safety in medical device clinical trials. Adopting AI/ML-specific regulatory frameworks alongside general medical device regulations can address unique risks like algorithms error and biases, ensuring patient safety, streamlines updates and builds trust while aligning with global standards to enhance competitiveness and regulatory efficiency. With these benefits and advantages, Malaysia can position alongside other leading nations in embracing and advancing the latest trends in medical devices on the global stage.
Conclusion
Malaysia has immense potential for medical device clinical trials. This potential is driven by its matured clinical trial ecosystem, skilled professionals, robust regulatory framework and supportive government policies. Initiatives like the Innovative Medical Device Pathway Framework and support from Clinical Research Malaysia demonstrate the country's commitment to foster innovation and maintain high trial standards. To realise its full potential, Malaysia may focus on regulatory harmonisation, adoption of advanced frameworks for emerging technologies, and collaboration between researchers and clinicians. By building on these strengths, Malaysia can drive advancements in medical device research, contributing significantly to global healthcare innovation.
Acknowledgement
We would like to express our sincere gratitude to Puan Nur Syafura Binti Ariffin from the Clinical Investigation (CI) section of MDA, Ts. Zubair Faramir Zainul Fadziruddin and Puan Idamazura binti Idris @ Harun, both from the Policy & Strategic Planning Division of MDA, for their inputs during the development of this article.
1. Medical Device Authority. (2017). Medical Device Guidance DocumentNotification Of Exemption From Registration Of Medical Devices For The Purpose Of Clinical Research Or Performance Evaluation. Retrieved from https://portal.mda.gov.my/index.php/documents/guidance-documents/ 807-16-notification-for-clinical-research-or-performance-evaluation/file
2. Clinical Investigation. (2024). Medical Device Trials Regulation. Medical Device Authority (MDA).
3. Clinical Research Malaysia (CRM). (2024). CRM Annual Report 2019 to 2023. Retrieved from https://clinicalresearch.my/annual-report/
4. News Straits Times. (2018). IJN Emerges As First Asia Pacific Hospital To Treat Heart Patients With Smallest Pacemaker. Retrieved from https:// www.nst.com.my/news/nation/2018/01/328093/ijn-emerges-first-asiapacific-hospital-treat-heart-patients-smallest
5. The Star. (2024). Successful diagnosis and treatment of country's first lung cancer detection using AI. Retrieved from https://www.thestar.com. my/news/nation/2024/09/04/successful-diagnosis-and-treatment-ofcountry039s-first-lung-cancer-detection-using-ai
6. IQVIA Institute. (2024). Rethinking Clinical Trial Country Prioritization Enabling Agility Through Global Diversification. Retrieved from https:// www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/ reports/rethinking-clinical-trial-country-prioritization
7. Low, L., Lim, M., Haring, T., Chan, S., Gebbo, D., Andalucia, L. R., Himawan, R., Rooslamiati, I., & Arlinda, D. (2024). Clinical trials in Indonesia: Challenges and opportunities for industry sponsors. Applied Clinical Trials. Retrieved from https://www.appliedclinicaltrialsonline.com/view/ clinical-trials-indonesia-opportunities-industry-sponsors
8. Medical Device Authority (MDA). (2024). Regulatory Pathway for Innovative Medical Device [Power Point]. Medical Device Seminar 2024.
9. U.S. Food and Drug Administration (FDA). (2017). Software as a Medical Device (SaMD): Clinical Evaluation. Retrieved from https://www.fda. gov/regulatory-information/search-fda-guidance-documents/softwaremedical-device-samd-clinical-evaluation
10. U.S. Food and Drug Administration (FDA). (2025). Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device. Retrieved from https://www.fda.gov/medical-devices/software-medical-device-samd/ artificial-intelligence-and-machine-learning-software-medical-device
Liew Eu graduated from Universiti Sultan Zainal Abidin with a Bachelor of Animal Production and Health (Hons). She later went on to obtain her master’s degree in Biomedical Science from Universiti Sains, Malaysia. Liew Eu has experience working in a diagnostic lab before joining Clinical Research Malaysia, where she currently works as a senior Feasibility Specialist.
Email: liew.eu@clinicalresearch.my
Nur Ain binti Amir
Nur Ain binti Amir holds a Bachelor of Science in Cell and Molecular Biology and a Master's degree in Neuroscience from Universiti Putra Malaysia. She is currently a Feasibility Specialist at Clinical Research Malaysia. Her experience includes involvement in Investigator-Initiated Research (IIR) focused on prophylactic migraine treatment, as well as active participation in multiple pre-clinical studies.
Email: nur.ain@clinicalresearch.my
Shu Hui is currently a senior Feasibility Specialist at Clinical Research Malaysia. She has a bachelor’s degree with honours in Medical Biotechnology from both Sunway University, Malaysia and Lancaster University, UK. She has experience in medical diagnostics, as well as in research and development.
Email: shuhui@clinicalresearch.my
Nur Aziemah Ab. Ghani
Nur Aziemah is currently working as a Feasibility Specialist at Clinical Research Malaysia. She has experience working in molecular microbiology wet lab research, before working as a study coordinator in clinical trials. She earned her bachelor's degree with honours in Microbial Biotechnology from The University of Queensland. She has attended conferences on oncology and clinical research during her tenure with Clinical Research Malaysia.
Email: aziemah@clinicalresearch.my
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While less resource-intensive than pivotal trials, early phase trials often involve significant scientific uncertainty. Information on a therapy’s side effects, behaviour in the human body and early efficacy data gathered in phase 1 trials directly influence the design of subsequent studies and set the stage for the entire drug development and approval process.
In an ICON survey of 149 biotech professionals, 45% of survey respondents identified protocol design as one the most significant challenges faced when transitioning from preclinical to clinical testing. Here, we discuss considerations for several key elements of protocol development in early phase trials, including selection of healthy volunteers (HVs) or patients, trial design and endpoint selection.
Selection of HVs or Patients to get to the Next Decision Point
Typically, phase 1 trials establish a drug's short-term safety and dose range and are conducted on very small cohorts of either HV’s or the target patient population. Deciding which population to enrol is one of the biggest decision points in early-stage protocol design, and can have significant impacts on the future development of the investigational therapy.
HVs are the standard choice for early-stage trials and are especially invaluable for therapies that may have applications in multiple indications. HVs are often easier to identify and enrol in clinical trials than target patient populations, reducing start-up time and costs. In addition, enrolling HVs allows for investigation of a drug’s pharmacology in the absence of disease and broadens the generalisability of early-stage safety and dosing data across patient populations.
Meanwhile, patient enrolment in early phases (if following HVs being labelled as phase 1b) is the norm when therapies are intended for life-threatening conditions without other treatment options, such as late-stage cancer, or for therapies liable to have serious adverse effects. Sponsors may also enrol patients in early phase trials if initial data on how a treatment impacts disease ( e.g. , with the help of biomarkers see ‘Endpoint Selection’) can accelerate later stage trials, saving time and money. Early phase patient data may be especially useful for treatments that are expected to have non-linear dose-responses, and for targeted therapies.
Healthy populations should not be enrolled in phase 1 studies if there is undue safety risk without counterbalancing treatment benefits. For many modalities, the FDA provides guidance on whether a treatment modality is too risky to enrol HVs. In the US, the FDA indicates that early-stage gene therapy trials are too risky for healthy populations. Meanwhile, bispecific antibody trials may enrol HVs as
long as the evaluated potential for immunogenicity and toxicity is sufficiently low.
Because there is often invaluable, non-interchangeable earlystage data from healthy or patient populations, it is increasingly common to enrol both groups using an integrated early trial. (Phase 1a/b) These tend to start with HVs and enrol a limited number of patients at an appropriate dose level. However, an integrated early trial does not escape the respective disadvantages of either approach, and so a decision to proceed with this enrolment strategy should also be carefully weighed.
For sponsors considering how to improve efficiency and lower costs in early phase development, innovative trial designs are emerging as a solution. These approaches, including adaptive trials, Bayesian methodologies and master protocols, are increasingly accepted and even encouraged by regulatory bodies.
Adaptive trials are the most established innovative early phase study design and can conserve resources by allowing for data-based decision-making earlier in the clinical trial process. If conclusive data is collected, a trial can be terminated early — curtailing the use of resources on a drug candidate that is likely to fail or accelerating development to the next phase. Adaptive designs can also reduce the time and money spent on early phase trial protocol amendments. To be effective, they must be planned carefully, with predetermined criteria that trigger specific adaptations, and often require statistical expertise and frequent, real-time communication between trial sites.
Bayesian statistical methods can also improve the efficiency of early phase trials and are often used to guide dose escalation studies in combination with an adaptive design. Unlike a frequentist statistical approach, which uses only the data gathered in the clinical trial to determine probabilities, a Bayesian approach maximises the use of data collected from smaller early phase studies by using prior data and assumptions to inform the probability of a hypothesis being true. For example, Bayesian analyses can estimate dose-limiting toxicities using data from preclinical studies and past trials, and can continue updating these estimates as more data is collected to enable real-time dose adjustments.
Although typically used in later-stage studies, master protocols –which offer a unified set of guidelines for multiple study arms - can also be invaluable in early phase trials. For example, master protocols are often useful in exploratory contexts, such as when a therapy has the potential to treat multiple indications or be effective as either a monotherapy or combination treatment. Because these designs use a single, shared control arm across all sub-studies, they require fewer participants than independent studies for each arm, reducing recruitment costs, and screening and development times.
Endpoint selection is another pivotal element of entering early phase clinical trials. Typically, phase I trials select endpoints that inform short-term safety and dose recommendations for the next phase, often through the collection of pharmacological data. If sponsors intend to employ longer-term or potentially more clinically meaningful endpoints early on, additional toxicology data may need to be gathered prior to initiating the study – with exceptions for indications such as oncology, to which different guidelines apply.
Sponsors may also utilise exploratory endpoints, including preliminary efficacy, in early phase trials. Once trials enter phase 2, endpoints focus more on clinical proof of concept, often in enriched populations. While later stages must have regulatory-accepted endpoints that centre on clinical outcomes that are meaningful for patients and clinicians, early phase trials can use endpoints that are geared toward exploring the utility of the treatment, as long as they are safe and ethical. However, sponsors must be prepared to adequately prove the connection between early and late phase endpoints to meet regulatory expectations.
In early phases, biomarkers can be critical to proving the efficacy of a therapy’s mechanism of action and may sometimes be used as endpoints. However, it can be challenging to identify the appropriate sets of biomarker data to collect for the clinical trial at hand, and to limit them to only those relevant. Reflecting the significance of biomarkers, and the complexity involved in their selection, biomarker selection was anticipated to be one of the top challenges of conducting phase I trials, chosen by 35% of
ICON survey respondents, and second only to navigating regulatory compliance. When selecting biomarkers as endpoints, one central question to consider is whether it would be worthwhile to continue in that drug’s development if a particular biomarker was not impacted by the investigational drug. It thus may be worth initiating an additional upfront phase 0 to fully characterise the biomarker(s) of interest.
Developing a strategic protocol design sets the stage for later steps in the development process. By fully understanding how an individual protocol fits into the overall development strategy, planning ahead, and utilising strategic partnerships, a biotech can give investigational products in the early phase the best chance of progressing through clinical trials and approval.
Sandra Eagle is a qualified Pharmacist with a PhD in Biopharmaceutics. She has over 28 years of experience in the pharmaceutical industry gained at GSK (SmithKline Beecham), 10 years as a Clinical Pharmacologist in the neuroscience area and 18 years of pharmaceutical project management. She now works as a Senior Director in Drug Development Solutions at ICON, where she leads Portfolio Analysis and Due Diligence.
Biotechs are increasingly driving innovation in research and development. However, this innovation requires specialist support, and it is important to choose the right outsourcing partner to overcome challenges around data, infrastructure and expertise. In this article, Stephen Corson, Associate Director of Statistics and Technical Solutions and Head of Statistical Consultancy at Phastar, explores the outsourcing challenges facing biotechs, considers opportunities for effective collaboration and shares four tips for partner selection.
A rapidly changing clinical trial landscape is driving a shift in the balance of research and development (R&D). Large pharma companies’ share of the clinical trial pipeline decreased from 40% to 20% from 2011 to 2021.1 Meanwhile, the global biotech market size is expected to reach USD 5.68 trillion by 2033, growing at a compound annual growth (CAGR) of 13.95%.2 Key areas driving growth are likely to include nanobiotechnology, biosimilars and precision medicine.
This evolving landscape is resulting in an increase in outsourcing as biotechs seek to tap into the specialised knowledge, resources and infrastructure offered by clinical contract research organisations (CROs). The CRO market is projected to reach USD 188.54 billion by 2030, growing at a CAGR of 12.6%.3 However, despite its advantages, outsourcing is not without its challenges. This makes it vital for biotechs to choose the right partner for them.
There are several reasons why biotechs choose to outsource to a trusted CRO. The first is to overcome global market and data concerns. A survey of 150 biotech executives found their top concerns were rising interest rates (39%), labour shortages (36%) and difficulty accessing necessary data (33%).2 While some of the challenges appear to be beyond the control of biotechs, they can interconnect. For example, improving data access, storage and analysis can reduce costs and increase efficiencies making biotechs more resilient to global pressures like rising interest rates.
The second reason biotechs choose to outsource is because access to top-tier talent within CROs significantly enhances their capabilities. Biotech companies’ constant innovation is helping to drive a rapid growth in market share. This can identify areas where a lack of in-house expertise can impact the success of R&D activities. For example, specialist biometrics CROs, can bring innovative solutions and expertise in biostatistics and regulatory interactions, resulting in cost-savings and accelerated timelines which can boost trial success.
The third reason biotechs may choose to outsource is to overcome barriers to successful trial execution. For example, over the past decade, clinical trial designs have become more complex, driving massive increases in data volume and complexity. Data deficiencies, either via poor data design, or execution, are among the largest drivers of failure for R&D assets. Data CRO teams are well-versed in the intricacies of handling complex trial designs and clinical data, ensuring that studies are conducted with the highest standards of accuracy and reliability.
While Biotechs and CROs can build strong partnerships, some biotech companies feel underserved by CROs. A survey of CRO customers found lower satisfaction across all service areas among smaller biopharma companies, including biotechs, compared to large pharma companies.5 The 14 service areas measured included study design, clinical monitoring, data management and project management.
Further qualitative interviews with the biotech companies highlighted several key concerns. Biotechs feel CROs are failing to deliver on their need for strategic advice and integration of technology solutions. Despite their exceptional scientific expertise, biotechs typically have fewer clinical resources than large pharma companies and their needs are fundamentally different. There is a perceived lack of focus on biotech C-suites and additional investment is needed to cultivate relationships with biotech leaders. Biotech companies can also feel there is a misalignment in incentives between themselves and CROs. Biotech companies can feel that they are not fully prioritised in terms of CRO team experience and expertise, which can result in delays in trial execution, particularly when dealing with rare disease R&D where patient recruitment is long.
Lower satisfaction levels across all services and the issues highlighted in qualitative interviews reveal the need to improve the outsourcing experience for biotechs. CROs need to focus on helping biotechs access specialised knowledge and building strategic partnerships to guide asset development. The benefits of these partnerships do not just go one way. By acting as an invaluable partner, CROs can gain more credibility in a rapidly emerging market and become a trusted provider for future development projects.
The right CRO can be an invaluable development partner for a biotech. To unlock this potential, it is important to focus on the areas where there is the most value in outsourcing.
For example, data access, management and analytics are key challenges for more than a third of biotechs. It therefore makes sense to outsource to a CRO offering centralised biometrics solutions across the clinical trial spectrum.
The rapidly evolving nature of biotechs means flexibility is also a key consideration. Biotechs need an agile partner that can pivot quickly to meet changing priorities and the need to make timely, datadriven decisions that can affect study life-cycles and R&D pipelines.
Finally, biotechs should consider how outsourcing to a CRO will allow them to build a strategic partnership which can guide asset development from inception through to regulatory approval.
1. Decide who to involve – and what their role will be Different people will be involved in the procurement process depending on the type of clinical trial and partner you are trying to appoint. Each of these individuals is likely to have different priorities and perspectives which can lead to disagreements
among stakeholders. An evidence-based process for partner selection can help to overcome this challenge. Be clear from the outset about who needs to be kept informed, how you will run any consultation and who will have final decision-making authority. This can help to avoid extended timelines and potential conflicts.
2. Get detailed information on timings and costs
Time delays and hidden fees are top challenges biotechs encounter when working with outsourcing partners.5 To avoid these issues, realistic timelines and fee structures, including costs like monthly project management fees, should be clearly set out in the contract. Your chosen CRO should be transparent and explain the contract terms clearly before you commit to any agreement.
3. Carry out every step in the selection process
It is important to go through a full selection process to ensure you get the right CRO first time. This process should include a detailed analysis of your needs and the risks and benefits of appointing potential partners. Stakeholder engagement is crucial during the preparation phase. Distribution of tasks must be clearly documented and agreed, and each party should have access to relevant data and information. While it can be tempting to skip steps in the selection process to speed up timelines, this can lead to higher long-term costs. It also increases the risk of having to switch partner mid trial.
4. Ensure your partner will meet your needs
It is important to be aware that biotechs need their partners to meet their needs. While CROs can offer innovative solutions that can drive down costs and accelerate timelines, this cannot be at the expense of data quality and integrity. Data is the life blood of biotech R&D and so selecting a partner that can deliver cost-effective solutions without sacrificing the data quality and integrity is critical to success. This will help build sustainable partnerships which will benefit both the biotech organisation and their target market.
Conclusion
Biotechs and specialist CROs have an opportunity to build mutually beneficial strategically thinking partnerships. However, to make the most of this opportunity, biotechs need to know how to select the right partner and CROs need to ensure they are offering flexible solutions that align with the goals and objectives of the R&D pipeline.
To make the most of outsourcing opportunities, biotechs should allow time for a comprehensive partner selection process. They should consider CROs with specialist expertise in key areas like biometrics and work with partners, such as Phastar, who have a proven track
record of delivering projects with quality and have transparency throughout every phase of the project.
For their part, CROs must work at a strategic level with C-suite executives at biotech firms and ensure they are prioritising and supporting these companies, particularly when their interests are in niche therapeutic areas.
By taking these steps, both markets can continue to grow and biotechs can ensure they are working with partners who will allow them to continue to innovate and lead R&D for years to come.
1. Pharmaprojects and Citeline databases, Informa, January 2022
2. Biotechnology Market Size, Share & Trend Analysis By Technology (Nanobiotechnology, DNA Sequencing, Cell-based Assays), By Application (Health, Bioinformatics), By Region, And Segment- Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024-2033, Nova One Advisor, April 2024
3. Global CRO Services Market By Type (Preclinical CRO, Clinical Trial CRO), By Application (Pharmaceutical Industry, Biotechnology), By Geographic Scope And Forecast, Verified Market Reports, April 2024
4. Adaptive Clinical Trials: Advantages and Disadvantages of Various Adaptive Design Elements, Korn and Friedlin, June 2017
5. CROs and biotech companies: Fine-tuning the partnership, McKinsey & Company, June 2022
Stephen Corson is an Associate Director of Statistics and Technical Solutions and Head of Statistical Consultancy who has been with Phastar since 2018. During his time at Phastar, Stephen has provided statistical support to trials in all phases in multiple therapeutic areas (oncology, hepatology, respiratory, ophthalmology). He currently supports RWE studies in Oncology and engages with industry teams to deliver in house training and identify the best solutions to the challenges they face. Stephen joined Phastar from Academia where he was a lecturer and director of the Mathematics and Statistics Consultancy and Training unit. Stephen is a member of the Estimands in Oncology Working Group and Vice Chair of the DIA Statistics and Data Science Community. Stephen was the 2023 winner of the DIA Global Inspire Community Engagement Award for his work in promoting and engaging in knowledge sharing across all functions within the industry. He is also the 2023 winner of Phastar's Sally Hollis Memorial Award for his contributions to the company and the wider industry.
Master Protocols are complex innovative designs that offer tangible benefits to both sponsors and patients including increased patient centricity, and efficiency in drug development. Randomisation, with Adaptive Design Features, is an essential study design element of Master Protocols that contributes towards patient centricity. However, for the patient centric benefits to be realised, the Randomisation System must be implemented appropriately with the necessary flexibility. This paper will focus on how the Randomisation design and system implementation of Master Protocols can effectively achieve patient centricity.
Master Protocols (e.g., Basket, Umbrella, and Platform trial designs) are complex innovative designs that offer tangible benefits to both sponsors and patients. The FDA defines a Master Protocol as “a protocol designed with multiple sub-studies, which may have different objectives and involve coordinated efforts to evaluate one or more medical products in one or more diseases or conditions within the overall study structure.” 1 Compared with conducting a traditional stand-alone trial, Master Protocols offer several benefits such as increased flexibility and efficiency in drug development, the ability to share control arms/reduced sample sizes, shared infrastructure, increased data quality and patient centricity.2 Therefore, Master Protocols have the potential to identify treatments that are effective or ineffective quicker than traditional trials.
The growing enthusiasm of Master Protocols is attributed to recent successful trials within various therapeutic areas such as COVID-19, glioblastoma, oncology, and amyotrophic lateral sclerosis.3 Master Protocols are efficacious within both small scale and larger scale patient populations. In relatively small patient populations, Master Protocols are able to efficiently and effectively test multiple targeted agents/therapeutic strategies.4 For instance, Master Protocols have the ability to provide optimal therapy customisation to individuals with specific biomarkers, as demonstrated by successful FDA indication approvals of biomarker-targeted Master Protocol trials.5 Regarding the larger scale, the FDA indicates that the efficiencies within the Master Protocol framework are particularly beneficial during public health emergency settings such as COVID-19 when there is a need to find effective therapies at a rapid pace.2 Across the COVID-19 health emergency, numerous traditional trials yielded no significant findings, whereas several Master Protocols (e.g., NIH’s Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) studies), continue to produce reliable evidence for investigational therapies.3 Further, the FDA expects that Master Protocols will continue to play an important role within COVID-19, as well as any potential future pandemics.2
Whether it be on the larger or smaller scale, the Master Protocol framework is well-suited to deliver efficient patient-centred clinical trials.3 As such, since Master Protocols have the ability to adapt by design, this enables continuous learning and has significant advantages for patients.6 In particular, two major hallmarks of innovation of Master Protocols are study design (i.e., Randomisation with Adaptive Features) and infrastructure (Randomisation
Processes).4 This paper will describe the Adaptive Design Features related to Randomisation and how they are applied to Master Protocols, their implementation within the Randomisation System, and how these components can achieve patient centric benefits.
Study Design: Adaptive Design Features Related to Randomisation Before entering the world of Master Protocols, it is important to understand the basic concepts of Adaptive Design Features related to Randomisation. The FDA defines an adaptive design as “a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial.” 7 Thus, an adaptive design allows for a trial to adapt mid-study, where these adaptations are planned and specified in the study’s protocol.
Common adaptations that impact randomisation include:
• Introducing new treatments
• Dropping existing treatments
• Pausing and restarting treatments
• Adjusting treatment allocation ratios or assignment probabilities
• Sample size readjustment.
Implementing Randomisation in an adaptive trial design differs greatly from that of a traditional study design. In a traditional study design, the included treatments and allocation ratio are the same for the entire study. For instance, patients are randomised to one of two treatments in a 1:1 ratio. In this typical scenario, one randomisation schedule is generated and used for patient randomisation across the study’s duration. Whereas in an adaptive design, the randomisation schedule structure may change depending on the planned adaptations. For example, a study starts with three treatment groups assigned in a 1:1:1 ratio. Then based on the results of an interim analysis, one of those treatments could be dropped or a fourth treatment could be added. Therefore, the randomisation could change to either assigning in a 1:1 ratio or a 1:1:1:1 ratio respectively. Having one randomisation schedule that is fixed with three treatments in a 1:1:1 ratio would not support these possible adaptations. A flexible randomisation scheme with additional randomisation schedules would be needed.
Master Protocols with Adaptive Design Features are like standard adaptive designs, but with additional dimensions of complexity. Standard adaptive designs are one dimension where the same adaptations are applied to the entire study. If treatment were dropped, it would be excluded from the study’s randomisation schedule. Whereas Master Protocols are studies that have multiple dimensions such as different subgroups, sub-protocols, sub-studies, etc. In the Master Protocol framework, treatment may be dropped in one subgroup’s randomisation schedule, and a new treatment may be added in another subgroup. The adaptations need to be managed independently for each dimension’s (subgroup’s) randomisation. Another example is the case of biomarker-targeted treatments, where only patients who are biomarkerpositive are eligible for that biomarker-targeted treatment, and those who do not have the biomarker are ineligible. Here, there is varying eligibility based on the included treatments that may or may not target specific biomarkers. The varying eligibility needs to be managed to
allow or not allow patient assignment based on biomarker-targeted treatments and biomarker presence.
Infrastructure: Randomisation System Implementation
Due to the complexity involved, the implementation and management of randomisation for Master Protocols often require a robust Randomisation System (e.g., web-based randomisation system (WBRS), Interactive Response Technology (IRT), Randomisation Trial Supply Management System (RTSM)). As such, the Randomisation System plays a critical role in the operations of a Master Protocol. Successful implementation of the Randomisation System requires that adaptations are executed with minimal disruptions.
Take the case of a study implementing a single fixed randomisation schedule with the initial study parameters (e.g., included treatments/ ratio) without accounting for any possible adaptations. With this set-up, if a new treatment is introduced, then a new randomisation schedule needs to be created and imported into the Randomisation System. While this approach works, it is disruptive since it incurs time, effort, and study downtime. A more efficient approach involves implementing flexibility in the Randomisation System by being able to execute these adaptations in real-time without having to create a new list.
Every Master Protocol is different, which means that each study’s optimal level of flexibility also differs. Planned adaptations are specified within the study’s protocol. Some protocols may explicitly detail the adaptations (e.g., which treatments could be added, which treatments could be dropped), while others may not. For example, a protocol states that new treatments can be introduced throughout the study’s duration as they are discovered. Since they are yet to be discovered, they cannot be explicitly specified in the initial protocol and will be included in an amendment once identified. If new treatments are expected, but not yet identified, the Randomisation System ideally would be built to allow new treatments to enter the randomisation scheme dynamically through flexible Randomisation System functionality. Likewise, any adaptations specified within the protocol that impact the randomisation schedule (e.g., ratios adjusted,
Study Design: Adaptive Features
Introducing New Treatments
Drop Ineffective Treatments (and Pause/ Restart for Interim Analyses)
Treatment Allocation Ratio Adjustments/ Response Adaptive Methods
Enrichment / Precision Medicine/ Biomarker-Targeted
Change to Control Arm if Standard of Care changes
Shared Control Arm
treatments can be paused/re-opened/dropped) should be accounted for as well.
Adaptive Design Features related to Randomisation can result in patient centric benefits. However, the effectiveness is related to how efficient adaptations can be executed within the Randomisation System. Table 1 summarises Adaptive Features related to the study’s randomisation design, how these features can benefit patient centricity and what is required for the implementation of the Randomisation System for efficiency. The optimal goal is for the Randomisation System to handle these adaptations in real time without having to create new randomisation schedule(s) for each adaptation.
Master Protocols require the involvement of more stakeholders and key operational partners than traditional clinical trials. Due to the inherent complexity associated with Master Protocols and level of expertise needed for successful implementation, the consideration and selection of operational partners is an essential part of the planning process for sponsors. To guide the appropriate implementation of Master Protocols, the Clinical Trials Transformation Initiative (CTTI) (a dedicated group focused on innovation of clinical trials co-founded by Duke University and the FDA)9 developed a robust set of resources.10 CTTI uses the terminology “operational partner” rather than “vendor” due to their critical contributions these organisations make during the development and implementation of Master Protocols.11 Per CTTI’s Operations Partners Assessment tool resource, the Randomisation System “plays a vital role in study success, as it uniquely sits within a clinical study’s EDC and database infrastructure.”11 Thus, to be selected as the Randomisation System Operational Partner, the provider needs to demonstrate that their tools and processes can support master protocols.3 Since the Randomisation System provider is considered a key operational partner, they should encompass experience and expertise in Master Protocols and offer consultancy/ guidance on implementation to be effective.11
Patient Centric Benefits
Opportunity to identify most effective treatment(s) for patients4,6
Improves outcomes of patients enrolled in trials8
Higher possibility to receive more promising arms; improves patient outcomes6,8
Optimal therapy customisation to individual patients5
Flexibility for the study to adapt/not terminate2,8
Potentially less patients assigned to control1,2,6,8
Study Implementation: Randomisation System Requirements
Ability to add new Treatments within Randomisation Schedule(s)
Ability to stop patient assignment for dropped / paused Treatments, and ability to restart within Randomisation Schedule(s)
Ability to adapt to the randomisation schedule’s allocation ratio within Randomisation Schedule(s)
Ability to account for patient-level and subgroup-level treatment eligibility in the Randomisation
Ability to add new Control Arm and drop previous Control Arm from Randomisation
Usually impacts ratio weights of involved active treatments requiring ability to adjust allocation ratio within Randomisation Schedule(s)
Once the Randomisation System Operational Partner is selected, it is recommended to begin discussions on randomisation implementation early in the planning phase. Operational partners are in a “key position to contribute crucial feedback about trial design feasibility when they are engaged early in the planning stages.”3 The extent of required stakeholder coordination, infrastructure requirements, and complex trial design elements can considerably extend the start-up time for a Master Protocol compared to traditional single-purpose trial.4 To ensure successful implementation, sponsors, and operational partners must communicate frequently to identify challenges and establish the optimal level of flexibility.3 Thus, it is essential to make sure that there is substantial time included within the start-up plan for these randomisation implementation discussions. Therefore, Randomisation System provider selection should start early during the planning process with careful considerations to ensure their technology has the capability to achieve the required level of flexibility.
Master Protocols can have several advantages such as efficiency in drug development and patient centric benefits. Regarding the Adaptive Features within a Master Protocol’s Randomisation, realising the patient centricity benefits are contingent upon how efficient the adaptations can occur during the trial. Efficient adaptations can successfully be implemented within the Randomisation System when the appropriate Randomisation Operational partner is selected with the necessary expertise and technical capabilities, and collaborations amongst the key stakeholders occur early within the planning phase. While this paper focuses on the patient centric benefits specific to Randomisation within the study design and implementation, there are many ways that Master Protocols can have patient centric benefits in other areas (e.g., standardised protocol processes, patient advocacy groups, site-management, study governance). To maximise patient centricity overall, it is equally important to also give ample consideration for the selection of other key operational partners (e.g., contract research organisations (CROs), safety management, electronic data capture/management, central labs, central institutional review boards). The selected operational partners, together with the sponsor stakeholders, can effectively collaborate working on the study design and implementation for Master Protocols to effectively achieve optimal patient centricity.
1. FDA. Master Protocols for Drug and Biological Product Development Guidance for Industry. [Online] December 2023. https://www.fda.gov/ media/174976/download.
2. COVID-19: Master Protocols Evaluating Drugs and Biological Products for Treatment or Prevention Guidance for Industry. [Online] May 2021. https:// www.fda.gov/media/148739/download?attachment.
3. Mobilizing the clinical trial ecosystem to drive adoption of master protocols. Bronson, Abby, et al. 2022, Clinical Trials; Vol. 19(6), pp. 690-696.
4. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. Woodcock, Janet and LaVange, Lisa M. 2017, The New England Journal of Medicine 377, pp. 62-70.
5. New clinical trial design in precision medicine: discovery, development and direction. Duan, Xiao-Peng, et al. 2024, Signal Transduction and Targeted Therapy; 9:57, pp. 1-29.
6. Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL. Koenig, Franz, et al. 2024, eClinicalMedicine; 67, 102384, pp. 1-12.
7. FDA. Adaptive Designs for Clinical Trials of Drugs and Biologics Guidance for Industry. [Online] November 2019. https://www.fda.gov/media/78495/ download.
8. CTTI. Master Protocol Value Proposition Guide. [Online] 2021. [Cited: February 12, 2025.] https://ctti-clinicaltrials.org/wp-content/ uploads/2021/06/CTTI_Master_Protocol_Value_Prop_Guide.pdf.
9. One-Pager. [Online] 2021. [Cited: February 12, 2025.] https://ctticlinicaltrials.org/wp-content/uploads/2021/07/CTTI-One-Pager_070821. pdf.
10. Master Protocol Studies. [Online] [Cited: February 12, 2025.] https://ctticlinicaltrials.org/our-work/novel-clinical-trial-designs/master-protocolstudies/.
11. Master Protocols: Operations Partner Assessment. [Online] 2021. [Cited: February 12, 2025.] https://www.ctti-clinicaltrials.org/wp-content/ uploads/2021/06/CTTI_Operations_Partners_Assessment_Tool.pdf.
Jennifer Ross is Director of Biostatistics at Almac Clinical Technologies, where she leads a group comprised of Biostatisticians and Data Managers specialised in randomisation design and system implementation. Jennifer has over 20 years of experience in biostatistics and clinical trials. She holds a MS in Statistics and Research Technology, and a M.Phil.Ed. in Psychometrics from the University of Pennsylvania.
Email: jennifer.ross@almacgroup.com
Venner
Kevin Venner is a Biostatistics Group Leader at Almac Clinical Technologies, where he leads a team of Biostatisticians specialised in randomisation design/implementation, providing services such as randomisation scheme generation, simulations for randomisation parameter selection, statistical consultancy and randomisation monitoring. Kevin Venner has over 14 years of experience in randomisation methodology and IRT implementation. Kevin has a BS in Mathematics from the University of Maryland.
Email: kevin.venner@almacgroup.com
Noelle Sassany is a Senior Biostatistician at Almac Clinical Technologies, specialised in randomisation design, subject randomisation/clinical supply list design, and randomisation system implementation. Noelle has 8 years of experience in biostatistics and clinical trials. She holds a MS in Biostatistics and Bioinformatics from the University at Buffalo.
Email: noelle.sassany@almacgroup.com
SOLVING TODAY’S CHALLENGES, LEADING TO TOMORROW’S ADVANCES
August 18-21, 2025 | Boston, MA
Omni Boston Hotel at the Seaport + Virtual NEW VENUE!
1,500 Attendees
300 Presentations
14 Conference Tracks
Stream #1 UPSTREAM PROCESSING
Stream #2 DOWNSTREAM PROCESSING
Stream #3 New for 2025 AI AND DIGITALIZATION
Stream #4 ANALYTICAL & QUALITY
90 Sponsors/Exhibitors
Stream #5 GENE THERAPY
Stream #6 CELL THERAPY
Stream #7 RNA AND GENETIC MEDICINES
Stream #8 FORMULATION AND STABILITY
The clinical trial landscape is constantly evolving, particularly in oncology early development, influenced by shifts in regulatory guidance such as the US Food and Drug Administration’s Project Optimus initiative or its draft guidance on Multiregional Clinical Trials in Oncology. This new era requires a solid understanding of the requirements and potential challenges, along with a strong command of the solutions needed to mitigate risks and optimise trial outcomes.
Below are suggested strategies to ensure success in operationalising global early-phase oncology trials.
Project Optimus emphasises dose optimisation in early-phase oncology trials should not be based solely on maximum tolerated dose (MTD) but should include pharmacokinetic (PK) and pharmacodynamic (PD) data, resulting in complex study designs that necessitate larger sample sizes and a broader global footprint. Sponsors and contract research organisations (CROs) must balance robust data collection with the practicalities of patient recruitment and retention across diverse regions and regulatory environments.
Operational execution in early-phase trials involves coordinating numerous factors during study startup and trial planning. A strong understanding of regulatory requirements and compliance with local laws is crucial for smooth trial initiation and progression. Knowledge of regional data privacy regulations safeguards patient information and maintains trust.
Enrollment and cohort management can be challenging in global trials with multiple sites located across various regions and time zones. Global study teams must be proficient in protocol knowledge, including managing multiple treatment arms, varying doses, and adaptive trial designs.
Sites must be adequately staffed and equipped to handle cohortspecific needs, requiring coordination of specialised personnel, medical equipment, and materials. Adding to the complexity of early-phase trials is risk-based monitoring, safety review, vendor management, investigational product (IP), and sample management. Effective communication and coordination among the various stakeholders are essential to trial success.
Mapping out the clinical study plan from the beginning is vital, especially for small biotechs. Capitalising on a CRO’s knowledge and experiences can be beneficial. Risk mitigation methods, including planning for unexpected events such as natural disasters and geopolitical climates, are essential in early planning.
Leveraging automated document management tools can reduce administrative burdens and speed study startup. Likewise, master service agreements can fast-track site contract negotiations and have a positive impact on global startup timelines.
Changes are inevitable in early-phase oncology trials, so remaining adaptable is key. Amendments to study protocols are frequent, so having a flexible setup process that allows for adjustments is crucial. For example, a trial may start as a first-in-human trial in one country and expand globally over time. Careful advanced planning helps anticipate amendments and effectively manage transitions and predict trial milestones.
Early-phase trials require a strong command of country- and sitelevel feasibility, ethics, and informed consent processes, along with the ability to navigate cultural differences and local regulatory climates. Global startup durations have been impacted due to the adaptation of European Clinical Trial Regulation (EU CTR) 536 and the transition to the Clinical Trial Information System (CTIS). Increased collaboration with investigators and sites has improved feasibility efforts. Critical outreach efforts, focusing on protocol-specific nuances, aid in selecting the right sites for study participation.
Understanding disease prevalence, analysing the competition, and leveraging local relationships with country-level experts and key opinion leaders (KOLs) help ensure a strong study design and smooth startup process. For example, in Asia-Pacific, knowing the country landscape and having strong experts on the ground is essential. Understanding demographics and diversity of patient populations help determine how well protocols will be adopted. Finding the right sites with the necessary equipment and knowledgeable investigators is crucial for quick activation and patient enrollment.
Predictive data analytics can help expedite informed decisions about country selection; and incidence rates and prescribing history facilitate the identification of suitable sites. Next-generation, novel oncology treatments sometimes face greater scrutiny and longer regulatory reviews, so risk mitigation and strategic planning are necessary.
Recruiting the right sites and patients in early-phase oncology trials is critical. Site selection involves assessing experience and expertise, prior knowledge of the site, and identifying KOLs. Qualification assessments ensure sites have the right capabilities, facilities, and staff to support the trial. Geographic considerations, patient demographics, and healthcare access are also important.
Strong, collaborative site relationships are fundamental for successful trial delivery, so feasibility questionnaires should be streamlined through conversations during qualification visits or calls with site coordinators. This helps reduce the site burden, support site engagement, shorten the startup time, and reduce costs.
Early involvement with KOLs is invaluable for identifying suitable sites and countries for trials. Knowledge gained from KOL and patient engagement can help avoid prolonged back-and-forth with regulatory agencies. Patient advocacy groups support CROs and sponsors in understanding patient perspectives about current treatments and how they differ. Patient outlooks, often found in online groups
and message boards, are valuable for helping make more informed decisions.
While rapid site activation is critical to trial success, it’s important to remain flexible. It is imperative to recruit the most appropriate sites and patients to optimise trial outcomes.
Coordinating Clinical Trial Logistics
Managing clinical trial logistics on early-phase trials has become increasingly multifaceted. Flexibility and planning from the outset are crucial for operational success. While using a central supply is recommended, it is important to assess trial needs and plan accordingly.
Using a central supply for comparator drugs is considered optimal as it de-risks the supply chain. Flexibility is necessary, however, to handle potential shortages. Expanding to additional countries may require local supplies, which can vary from region to region or even within hospitals. Each approach has associated costs, and it’s essential to ensure the system can withstand audits and track every pill to avoid recalls and patient issues. Reimbursement cards can simplify the process and may add a layer of complexity for drug reconciliation. Using real-world data arms can potentially replace the need for purchasing comparators.
Each country and region have specific approvals and packaging requirements, which can be challenging to navigate, so being prepared for these complexities and any potential shortages is essential.
Innovation in early-phase oncology trials often focuses on AIpowered platforms that automate tasks and workflows, reducing site burden and administrative challenges. Document workflow automation and forecasting tools are becoming more prominent, aligning multiple data points for better efficiency.
AI can also assist with streamlining processes, improving data acquisition and analysis, and optimising trial performance. Early indicators and trends can help plan downstream activities. Realworld data arms and AI tools may reduce the number of patients required for trials by leveraging historical data sets. Overall, these innovations aim to enhance clinical trial efficiency, reduce costs, and improve outcomes by leveraging advanced technologies and datadriven approaches.
Successfully operationalising early-phase oncology trials involves a multifaceted and well-planned approach. Familiarity with disease prevalence, knowledge of local regulations and customs, leveraging local relationships, and engaging with KOLs and patients are critical to ensuring operational successes. Proficiency with global enrollment, cohort management, and clinical trial logistic coordination is crucial. Effective planning and preparation, including the use of innovative tools like AI, are essential for making informed decisions and ensuring smooth trial execution. Flexibility in drug supply management and implementing cost-saving measures have never been more important. Innovations such as AI-powered platforms and real-world data arms are transforming trial processes, aiming to reduce site burden, and enhancing efficiency.
By embracing change, leveraging expertise, and utilising cuttingedge technologies, early-phase oncology study teams can produce reliable data that supports regulatory submissions and advances drug development for sponsors and patients.
Keya Watkins, Senior Vice President, Catalyst Oncology, brings 25 years of experience in Oncology drug development with biopharma and clinical service providers. Keya has built and provided oversight of multidisciplinary teams across clinical development execution inclusive of clinical operations, data sciences and trial support services. She also served as an executive in operational and commercial roles and has been involved in complex, global trials in various therapeutic areas and phases.
Marcia Milholen, Vice President, Central Site Services, Catalyst Oncology, is responsible for site ID and selection, site start up and eTMF management, oversight, and maintenance. Marcia has over 24 years of industry experience, focused on CRO services. Her experience is across areas of country and site feasibility, site selection and activation, clinical monitoring, and project management.
To advance science without compromising trial efficiency, we must prioritise simpler user journeys and connected data.
Clinical trials are constantly pushing the boundaries of innovation. Over the past few years, there’s been an explosion of advancements, such as the widespread use of biomarkers in precision medicine, exemplified by initiatives such as Genomics England’s 100,000 Genomes Project, that supports patients with rare diseases and cancer, alongside the increasing integration of real-world and digital device insights.
A large-scale analysis of protocols and other data sources from over 16,000 trials highlighted a trend toward increasing complexity in clinical trials across all the indications evaluated. Complex science often undermines operational efficiency. Over the past seven years, the average number of amendments per protocol increased by 60 per cent. At the same time, the typical time to implement an amendment has almost tripled.
Even making minor changes to a gene therapy trial, such as increasing the number of participants, can significantly impact a study’s success by driving costs up sharply. Overhyped technologies (like decentralised clinical trial solutions) have often fallen short of expectations, leading to lower operational efficiency rather than sought-after improvements.
To avoid a tug-of-war between scientific rigour and operational efficiency, we must focus on the user and data journeys of sites, patients, and sponsors. Simpler everyday experiences with the use of connected data are the foundation to delivering the trials we need rather than what the technology allows.
Sites have been voicing their concerns for years about the growing technology burden. Common pain points include navigating over 15 portals per study, organising password changes every six to eight weeks, and accommodating each sponsor’s unique definitions, standards, and database setups. Not only do disconnected tools take site staff away from patient care and absorb their time in training, but they also undermine data quality by forcing repeated data entry. Viviënne van de Walle, medical director and founder of PT&R, likens the site experience to being stuck “in a really bad escape room”.
Thankfully, we are turning a corner on delivering better site support. An aspiration of fewer systems will reduce the site admin burden with positive knock-on effects on both patient recruitment and engagement. A better patient experience would then widen access to life-enhancing treatments, particularly in rare diseases. Reflecting on her experiences and needs as a rare disease patient,
Helen Shaw, co-founder of the virtual site VCTC, observes: “I see how hard it is to take part in a clinical trial. But patients do want that opportunity to be offered something that they wouldn't get in their standard care, whether additional MRIs or new medicines.”
Simplifying at a time when science is becoming more complex can feel counterintuitive. But when sites and sponsors shed the legacy systems holding them back, they can finally determine what processes they need to run the trials they want.
Sponsors and CROs (Clinical Research Organisations) are increasingly focused on alleviating a site’s concerns when introducing new systems, even when those systems are ultimately designed to simplify processes. This involves being aligned on shared objectives and working together closely.
All sites are unique, bringing varying levels of technological experience. A ‘one-size-fits-all’ style of interaction is one of the most commonly cited challenges that sites face in their partnerships with sponsors. One clinical trial management software leader notes the impact of this mindset on sites: “Every site can have a different starting point or place of comfort when it comes to implementing technology. The ideal is to remove some of the administrative burden, but sites can have mixed feelings about new technology.”
They add: “Simplifying is a big win. It shows that we’re moving to a mindset of fixing problems instead of just adding more functionality.”
With cell and gene therapies accounting for a bigger share of the drug development pipeline, we can expect an evolving research profile: more studies with relatively small patient populations and rolling regulatory approvals, leading (hopefully) to compressed timelines. Yet, paradoxically, even a study of 30–40 patients can still ingest and generate huge volumes of relevant data (e.g. DNA-related, molecular information) because each person is treated as an individual rather than a study average. This data is then used to develop highly personalised and effective treatments.
As we transition into a non-EDC-centric world, we need more flexible data management so sponsors can drive science forward while delivering complex studies efficiently. Rather than a one-sizefits-all approach, systems and technology must be able to support the majority of protocols with enough flexibility for niche trial requirements.
Artificial intelligence (AI) and machine learning (ML) will play an important role in transforming raw data, so it is clean and usable. Andy Cooper (CEO of CluePoints, a risk tracking and analytics
provider) observes that machine learning is already taking the noise out of edit checks, for example.
AI and ML are not yet ready to solve all our data challenges. But in the meantime, automation can generate value at multiple points during clinical data management. Commenting on its impact within their organisation, a senior data science leader at a top global healthcare company says: “The growing number and complexity of trials means we should be working at scale, not just in production and facilities but also in our clinical setup. These functions have to be able to work together, and to scale.”
Once all study data is connected, advances in clinical trial efficiency become feasible. For instance, automation is one of the four main pillars that this healthcare company is optimising for growth. Its data science leader explains the importance of another pillar – a strong data foundation – to their team’s success: “It’s common for data infrastructure setup to be horribly patchworked. You can’t introduce meaningful end-to-end automation on poor data and without seamless processes. We need to get rid of the patchwork.”
Now is the time to challenge outdated practices, including excessive data collecting, cleaning, and querying. Too much time, money, and effort are spent today on the latest technology that generates excess data – all in the name of "innovation". A pragmatic, value-focused approach is needed to innovate sustainably. Regulators are already heading in the right direction by encouraging a risk-based approach to trial design, focusing on the data and processes that ensure trial quality.
Our goal is to unify data with the right processes and skilled employees, enabling clinical teams to concentrate on the science while data management systematically uncovers patterns within and across studies. The benefits of a centralised approach go beyond operational efficiency and, over time, could change the economics of clinical trials. By bringing medical, investigators, and site coordinators
onto a single, connected platform, we can streamline patient recruitment, and address emerging challenges in drug development more effectively.
It’s time to end the conflict between scientific rigour and trial efficiency. By prioritising simpler everyday experiences for sites, clean connected data, and a pragmatic approach to innovation, we will advance science together.
1. Genomics England, 100,000 Genomes Project. https://www. genomicsengland.co.uk/initiatives/100000-genomes-project
2. Diabetes Research Institute, ‘The Expanding Role of Real-World Evidence Trials In Health Care Decision Making’, J. Diabetes Sci. Technol.
3. Boston Consulting Group, ‘Clinical Trials are Becoming More Complex: A Machine Learning Analysis of Data from over 16,000 Trials’, Scientific Reports
4. Tufts University Center for the Study of Drug Development, ‘New Benchmarks on Protocol Amendment Practices, Trends and their Impact on Clinical Trial Performance’, Therapeutic Innovation and Regulatory Science
5. Tufts CSDD, Comprehensive Summary of Site Engagement Literature, https://csdd.tufts.edu/white-papers
6. Pink Sheet, ‘Orphans Account For More Than A Third Of EU New Drug Approvals In 2022’
7. European Medicines Agency, ICH guideline E8 (R1) on general considerations for clinical studies
Manny Vazquez is the Director of Clinical Data Strategy at Veeva. He has nearly 20 years of experience in clinical data management, beginning his career at a small oncology biotech before joining a CRO. In early 2022, Manny joined Veeva to support strategy for clinical data applications.
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