Clinical Trials: How Far We’ve Come

Let’s start with the good news. Over the last decade, clinical trials have undergone significant improvements, driven by advancements in technology, changes in regulatory guidelines, and increased recognition of the benefits of a patient-centric approach. Here are five key examples of progress in clinical trials:

1. Digitalization of clinical data management. Global acceptance of electronic data capture (EDC) systems and electronic health records (EHRs) has streamlined data collection and management, making trials more efficient and reducing errors associated with paper-based processes. This digitalization enables real-time data analysis and facilitates remote monitoring, improving trial oversight, and patient safety.
2. Adaptive trial designs. Adaptive trial designs allow for modifications to the trial protocol based on accumulating data, which enhances efficiency by allowing adjustments to sample sizes, treatment arms, or endpoints during the trial, increasing the likelihood of successful outcomes. Adaptive designs also help accelerate the identification of promising therapies or the termination of ineffective ones.
3. Patient engagement and inclusion. Patient engagement strategies aim to incorporate patient perspectives and preferences, ensuring that trials are more relevant, accessible, acceptable to participants and improve patient care. This involvement leads to higher retention rates, improved patient compliance, and more meaningful outcomes.
4. Decentralized and remote trials. The COVID-19 pandemic accelerated the adoption of decentralized and remote trial models. These approaches leverage telemedicine, remote monitoring devices, and mobile health applications to reduce the need for patients to visit physical trial sites, making participation more convenient and inclusive. Remote trials have the potential to increase patient enrollment, diversify participant demographics, and enhance data collection.
5. Real-world evidence (RWE). Real-world data (RWD) and RWE are being increasingly used to supplement traditional clinical trial data. RWD is collected from various sources, such as electronic health records, wearable devices, and patient registries. By integrating RWD into clinical trials, researchers can gather additional insights into treatment effectiveness, safety, and long-term outcomes, improving the understanding of a therapy’s real-world impact.

The bad news? Running efficient and cost-effective trials is still challenging. Clinical trials are complex – and the more complex the trial design, the more difficulties a company may face in designing and selecting appropriate endpoints. Defining clinically meaningful and statistically robust endpoints that align with regulatory requirements and patient perspectives will always be a challenge. Additionally, adaptive trial designs, while advantageous, can introduce additional complexities in trial planning and execution – and these types of trials are increasing across the industry. However, this latter challenge can be mitigated by using skilled biostatisticians with experience in complex trial types.

The participation challenge
 

One challenge that is a hallmark of clinical trials is enrolling sufficient participants within the required timeframe. Recruitment difficulties can lead to delays, increased costs, and a lack of diversity in the study population. This is especially true in rare disease trials, where patients that meet trial entry criteria are few and far between. In a traditional clinical trial, you may have to set up multiple trial sites for very few patients, which increases the cost of the trial. Here, decentralized trial approaches can help by allowing patients to enroll from around the globe, with less reliance on physical trial site locations.

Though decentralized trials (also known as virtual trials or remote trials) offer advantages in terms of convenience, patient participation, and data collection as noted above, not all trials are suitable for this approach. Various factors determine the potential for virtual visits, including the nature of the trial, therapeutic interventions, data collection requirements, and the patient population. Trials suitable for virtual trials may include observational studies, non-invasive interventions, chronic disease management, post-marketing surveillance, and patient-reported outcomes research.

On the other hand, trials that will require in person visits are likely to be interventional trials, phase I safety studies, or imaging or biomarker assessments. Vulnerable or high-risk populations also benefit more from in-person trials. 

It's important to note that deciding to conduct a trial virtually or require in-person visits depends on carefully considering the specific trial objectives, participant characteristics, regulatory requirements, and available technologies. Hybrid approaches that combine virtual and in-person elements may also be used to strike a balance between convenience and the need for physical interactions.

The digital age
 

Despite advances in data management for clinical trials, data integrity can also be a significant challenge. Clinical trials generate vast amounts of data, which must be managed and integrated across multiple sites, systems, and stakeholders. Ensuring data quality, standardization, and interoperability are crucial for meaningful analysis and interpretation – and to avoid fraudulent data. 

In my view, one of the most important changes in clinical trials is digitalization. EDC is today the minimum standard for clinical trials and data collection, but we can do even better by adopting newer technologies such as electronic clinical outcome assessment (eCOA). eCOA systems enhance the collection of clinical data outside of traditional research sites and improve data quality through the use of smartphones, tablets, or wearables – either provided to patients or used on their own devices in a “bring your own device” trial.

Even with rigorous statistical planning and adaptive trial designs, clinical trials have a level of uncertainty driven by the human element and the inherent variability of disease progression, treatment responses, and patient adherence. Uncertainty related to clinical outcomes, sample sizes, and statistical power can also impact trial design and efficiency.

It’s also important to remember that clinical trials operate within the broader healthcare ecosystem, which is influenced by external factors, such as healthcare policies, market dynamics, patient preferences, and geographical variations. These external factors can affect patient recruitment, impact trial timelines, and influence generalizability of trial results.

The pharma industry must continue to improve its clinical trials. Digital technologies have helped address some of the challenges posed by the pandemic and introduced new trends in the practices of contract research organizations and pharmaceutical/biotech companies when conducting clinical trial analysis. Digital data-capturing technology has ushered in a significant shift, rendering traditional paper-based record-keeping methods obsolete. Advancements in technology also enable more patient-centric approaches to conducting studies, with trials employing data-capturing technologies for remote data collection, reducing reliance on clinical sites. As the industry moves in this direction, it will be crucial for CROs and sponsors to have a comprehensive understanding of virtual, decentralized, and hybrid trial approaches, as well as a solid grasp of real-world data.

Placing patients at the center of clinical trials is crucial, which means involving patients in trial design, ensuring informed consent processes are clear and understandable, minimizing burdensome procedures, and considering patient preferences and perspectives. A more patient-centric approach allows for improved patient recruitment, retention, and supports more credible and valid data. 

Embracing digital and virtual technologies can make this possible. However, pharma will need to understand how to handle and analyze the huge increase in data, including real-world data. Trials will need to be carefully designed and processes must be put in place to validate and verify data. Done successfully, the gains are significant: enhanced efficiency, reduced costs, and more accurate data.