Predictions and perspectives on the future of DCTs

"The message was clear from industry leaders this year whether on social media or conferences: today’s clinical trials need decentralised solutions", says Temitope Keyes, Executive Director Business Development at encapsia, commenting on the decentralised clinical trial (DCT) event horizon. Clinical trials are now more complex than ever, and sites and patients can (and will) be anywhere. Data is cascading in from a variety of sources and, when coupled with expanded trial data volume, has a continuous growing impact on study timelines and inefficiencies. A quick glance at today’s clinical trial operations makes it clear that, in reality, technology has not kept pace with the advance of today’s decentralised, digital world. There has been a crucial shift towards the implementation of digitalisation across the continuum of trial conduct, especially in the adoption of new patient- and site-centric technologies, and tools that deliver data immediately along with improved processes that provide for continuing patient safety. However, in many of these cases, the solutions are just stitched onto already underperforming legacy systems. It is not news to anyone in the industry that there’s a history of bolting together disparate systems to gain new functionalities in order to meet trial needs. This intricate patchwork of systems has kept growing but was never designed as a functional and cohesive whole. As such, trial data and information are, unsurprisingly, not always properly integrated so may be unavailable holistically. Add on top of this cascading data from high data-load sources such as real-world evidence (RWE) and wearables, imaging, biomarker labs, and electronic patient-reported outcomes (ePROs)/electronic clinical outcome assessments (eCOAs), and it falls to clinical operations and data management staff to connect the dots. Consequently, clinical teams compensate by creating ‘workarounds’, the most common of these being pulling extracts into Excel spreadsheets. In an era of increased data volume and complexity, this hinders data availability and ultimately delays critical trial decisions. Sponsor teams don’t get the benefit of a ‘single source of truth’, thus limiting true patient centricity, and eroding investments in upstream analysis tools (e.g., SAS) by limiting the speed of reporting and visualisation. In addition, we need to consider that a lot of the individual systems are aging and no longer fit for purpose. Predicting the future of DCTs Existing point and unified solutions cannot fully meet the challenges the industry faces today, nor can they adapt to this dynamic world of decentralised clinical trials (DCTs), digital transformation, and data science. There is persistent risk and frustration in not efficiently aggregating and integrating disparate data and DCTs have brought that to the forefront. Although this is not new, the problem has become more acute. An example is in the fact that a lack of speed and inability to visualise the data in anything other than flat presentations or tabular listings has become accepted. Whether virtually or in-person, sponsors emphasised that a holistic clinical technology platform is a must to enable DCTs today. More complexity in trials means intensive data capture, and continuous flows are essential, while vital strategic decisions rely on real-time data analysis and intelligence. However, depending on the organisation, clinical portfolio, and outsourcing model, DCTs will look like many different things across the clinical trials landscape. What we heard over the course of 2021 from senior leaders in clinical operations, data management, biostatistics, IT, and even regulatory, is consolidated below followed by our thoughts on the best path forward to realise the many benefits of DCTs: The role of data managers will evolve through the availability of innovative technology solutions to speed trial data flow and analyses As data managers evolve to become data scientists, there will be people who are experienced in the world of traditional clinical data management, but without a broader base of technological skills in their arsenal, they will be left behind as the rapid pace of clinical development continues, requiring real-time analytics. A crucial node in the process, their responsibility is to stitch all the data together – a responsibility made more complex by the variety of data sources. They need a platform that supports the range of technical skills in the user base, allowing them to leverage workflows and see various insights without the need for any additional programming skills or having to operate through a variety of different tools and systems. With greater data oversight and supported by sophisticated analytical software, data managers will be empowered to create a new paradigm in trial efficiency and effectiveness. There should be a single source of truth The industry has a long history of bolting on bits and pieces, trying to do something new with old tools that are not up to the task, with data remaining separate or adding time and risk to map and translate between systems. A more central holistic platform that can capture the range of different data types that are needed, whether from traditional clinical trial data or more innovative data points such as wearables is required. There will be one single source of truth that allows integration with existing third parties – for example Python or R – to analyse, process and visualise data, in real-time. With the right tools, teams will be able to run DCTs seamlessly and ensure clean, accurate data they can trust. Artificial intelligence(AI) will lead us to the future Adoption of AI is happening throughout many industries, and within pharmaceutical and clinical trials, it is just now being proven as a valuable tool. The incorporation of AI algorithms to interpret trial data instantaneously is very promising and has great potential to revolutionise data analysis and drive the even faster identification and development of new therapies. A notable example of this is identification of cancerous tumors from CT scans, displaying the image and a probability to the location of nodules. The results of that analysis can then be assessed to decide whether results should be analysed manually, by human experts, or automatically, by AI, depending on the risk profile of the client, all in one cohesive platform. There is an opportunity for technology like this that allows the algorithms to be explored but also surfaced as insights and that can be used by anyone regardless of technical ability. Risk-based monitoring(RBM) takes center stage The safety and quality of clinical trials is a high priority – by identifying, assessing, monitoring, and mitigating the risks that affect the quality or safety of a study, you can improve patient safety whilst lowering the attendant risk profile. RBM is the only efficient way of monitoring DCTs due to the disparate nature of patients and associated data in these trials. To achieve the best remote RBM strategies, technology is needed that offers up to the minute visualisations, alerts, and notifications. The trials of today need a platform that can efficiently detect errors, identify risks to patient safety and confirm data validity, and trigger actions so that their impact can be mitigated, with issues detailed and resolved quickly. Disparate data is the norm With traditional, in-person studies, in established indications like diabetes, the range of data sources has been manageable and easily controlled through current processes. However, with DCTs, there are more data types, more volume, and most, if not all, would be eSource data e.g., direct data capture, ePRO, eCOA, central labs – both structured and unstructured. So yes, even in diabetes, patient-wearables, apps, and “implantables” are streaming data. DCT technologies need to be collected and standardised across data sources which, ideally, could be delivered by a single tool that instantly automates that standardisation and easily accommodates the various sources, allowing for smoother, centralised data management and greater adaptability for the inevitable, like protocol amendments. As with all modern trials, ensuring that disparate data can be easily integrated and is immediately available to users for review to aid fast decision making is critical for the clinical program compliance, oversight, reporting, and safety of patients. With the right technology in place, sponsors can realise their goals of running flexible, dynamic DCTs. Enabling DCTs through technology Centralise data collection and analysis DCTs require a single technology platform that allows for improvement of operational efficiency and cost-effectiveness, through reducing the time and cost of database build e.g., data standardisation and training in data transformation processes. Such a clinical trial platform should offer multi-modal and centralised data collection (eSource, EDC, Home Visit) to help standardise quality across sites and enable full traceability of that data for auditing purposes. Disparate data should be easily integrated and immediately available for review and decision-making. Convert data into knowledge Powerful, actionable insights in 3D within the clinical trial system will allow sponsors to act more quickly and make optimal decisions for the clinical program, compliance, and the safety of patients. Get more from your biostatistical team skills using R, SAS, Python, and Julia. Support sites to conduct DCT/virtual trials Sites need the opportunity to update their procedures and adopt more effective/efficient systems that allow new options such as trial virtualisation and central data monitoring. eSource is a straightforward switch which can save a huge amount of time and effort at site. Instant data visualisations, available 24/7, would increase clinical awareness and improve patient safety and oversight. Engage all stakeholders including sites, regulators, and patients Internally, this includes teams such as clinical operations, data management, biostatistics, and medical monitoring. Sites are currently burdened with having to learn and use multiple systems across studies, so it’s prudent to consider what will work best for them. And, because DCTs involve collecting data directly from patients, it’s important to ensure that the technology is easy for patients to use. Additionally, it’s important to secure support from senior executives to ensure time and resource is made available to all teams. Do data differently With the right technology in place, sponsors can realise their goals of running flexible, dynamic DCTs. This includes the power of data science and concepts such as Big Data, AI, machine learning (ML), and natural language processing (NLP). A radical shift of organisations from staid clinical development through to a digital transformation has long been a vision for clinical trial leaders. To truly enable DCTs, nimble and agile technology is crucial to be able to manage, explore, visualise, and analyse clinical data effectively, so we can move trials forward in the new reality. It is not about loosely linked point solutions or so-called ‘unified’ solutions. It is now vital to have a data strategy built around a single technology platform that can support the current and future health innovation of sponsors no matter where sites or patients might be. Tools to fit trials DCTs come in so many different variations, it’s important for sponsors to shift from a place where they’re trying to mold their trials to fit the tools they have, to one where they actively choose the right tools that fit their trials from the get-go. The technology used before the pandemic is no longer good enough and the industry must embrace fit-for-purpose technologies built to enable today’s trials. To do data differently, change must be embraced, using partners who can provide that revolutionary technology as well as technical consultancy, expertise to support that shared vision. About the author Temitope (Tope) Keyes has 22 years of clinical R&D experience, which began on the sponsor side, with the majority of that primarily in the eClinical solutions space. She has a passion for technology and its ability to advance vital clinical research and successful trial execution. Her experience includes pre-clinical purchasing and clinical outsourcing roles at AstraZeneca and Sanofi, followed by almost 15 years in business development with the likes of ERT, Synteract, Datatrak, and Axiom Real-Time Metrics. Keyes joined Cmed Technology in 2020 where she drives the sales and marketing for the encapsia clinical technology platform.