With populations aging, chronic diseases becoming more prevalent, and global health experts warning of potential pandemics, the need for rapid therapeutic development has reached unprecedented levels.
And yet, as we approach 2025, the clinical trials community stands at a crossroads, driven by cutting-edge technologies and innovative methodologies, yet hindered by a resistance to change. This article explores four key predictions for the future of clinical trials, examining how these changes will revolutionize the speed, efficiency, and reliability of bringing life-saving treatments to market.
Clinical trials will tick upwards again
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In the Covid-19 world of lockdowns and isolation, the number of clinical trials plummeted. Research by Medidata found that enrollment plummeted 74% YOY in May 2020, and Penn State Health reports that between April and October 2020, study completion rates fell 13%-23% globally.
The impact of the pandemic lingered for a while, but 2024 finally saw the beginnings of a real recovery, and I predict that it will really take off in 2025. One report forecasts that the global clinical trials market will grow from $48.2 billion in 2023 to $73.2 billion by 2028. The past year saw increased investment in biotech companies which tends to translate to more clinical trials, since investors typically choose to invest in companies that are just completing their preclinical stage. As a result, 2025 could be a bumper year for the biopharma and clinical trial industries.
Data capture will surge in popularity
In an era when AI-driven data tools reign supreme, clinical trials are an outlier. Inefficient, error-prone manual data management processes lead to trials that exceed timelines and budget, and sometimes fail to deliver conclusive results. It’s estimated that 20% of the total costs of a study, or as much as $24 million, is spent on copying and verifying data.
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The amount of data collected by medical centers is increasing and as a result, the amount of data collected in each trial is also increasing. If the data is sitting at the medical centers, clinical trial sponsors want access to it. At the same time, regulatory bodies are pushing for more data to be collected in each trial. The most efficient way to handle the growing data burden in clinical trials is to make use of new technologies. Tech solutions can transfer data in minutes instead of days, and with far more accuracy. New solutions make it possible to utilize real world data (RWD) and help remove data silos throughout the trial. Additionally, technologies that pull data from EHRs can impact recruitment challenges by preventing potential patients from falling through the cracks of manual enrollment processes.
Gaps between enrollment plans and enrollment will narrow
Difficulties with patient enrollment have long been an obstacle for clinical trials. It’s estimated that 86% of all trials never meet their recruitment goals, almost all of them take longer than expected for recruitment, and up to 90% are delayed due to low enrollment rates. This pushes up costs, causes trials to be delayed, and often leads to canceled trials.
Companies that use new AI and LLM technologies to predict the study success will help deal with these challenges, and I expect these tools will be used more frequently to improve enrollment in 2025. The global market for AI in clinical trials is predicted to grow from $1.42 billion in 2023 to $8.5 billion by 2035.
AI can quickly identify the best matches for a particular study; design and test protocols to ensure that they are effective; and enable studies to go ahead with fewer control patients. AI modeling can select the best sites for a trial, and generative AI can automate the production of vital trial documents like informed consent forms, while ensuring they are easy to read, easing the enrollment process. These tools ultimately save time and money by making clinical trials faster and more reliable.
Clinical trials will become more patient-centric
As demand rises for more trials and more diverse trial participation, we’ll see increased patient-centricity in 2025. The patient-centric approach may well evolve to patient-mediated research, where patient data and feedback is used to guide drug development and trial planning.
The concentration of clinical trials in urban medical hubs poses a significant challenge to achieving diverse participant pools. Rural residents, in particular, face disproportionate obstacles in accessing these studies due to extensive travel requirements and associated expenses. Additionally, children, the elderly, pregnant women, and people with terminal and/or rare diseases are frequently excluded from participation.
In 2025, companies will continue to find innovative ways to make clinical trials more inclusive and robust. While standalone decentralized clinical trials (DCTs) didn’t solve many of the clinical trial challenges, using DCT elements in the framework of a hybrid model allows for trial interactions in community settings like local hospitals and smaller medical centers. Research by ABCSG (the Austrian Breast & Colorectal Cancer Study Group) has shown that running trials in smaller, rural clinics raised enrollment, shortened setup times, and improved compliance levels.
Progress is gradual, but definite
The world of clinical trials tends to be traditional and slow to change, so we can’t expect a revolution in the next 12 months. What we can expect is incremental progress, with baby steps, so it’s vital to chart the trends and watch as they gradually gain momentum. When companies provide real solutions to serious problems, it’s evident that change is sustainable, bringing real improvements to the clinical trial space.
Photo: rudall30, Getty Images
Iddo Peleg is the CEO and co-founder of Yonalink. Iddo previously spent two decades in clinical testing roles at prominent pharmaceutical firms and CROs and brings a wealth of insight into the challenges plaguing the clinical trial industry, specifically the pressing need to rectify inefficiencies inherent in today’s manual data transfer process, which often lead to inaccuracies, extended trial times, and unnecessary demands on study staff.
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