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Approximately only 14% of all drugs undergoing clinical trials eventually get FDA approval. The reasons for failure or delays are numerous, but more often than not, they have to do with misunderstandings of key biological and/or drug development principles or patient population scoping, leading to inadequate study design, inappropriate efficacy markers, and more.
While there are steps to be taken to help ensure a clinical trial’s design is as optimized for success as possible, life sciences companies are increasingly facing more and more challenges when trying to do so, making trial success harder and harder to achieve. Below, we have detailed three of the top clinical design challenges that might compromise designing a study for maximum efficacy. We are also detailing some strategies to help overcome these challenges.
One of the most significant challenges in clinical trial design is ensuring optimal patient recruitment. A well-designed clinical trial requires a sufficient number of participants to guarantee that the study results are statistically valid and reliable. However, recruiting the right patient population and keeping them enrolled can be difficult tasks.
The success of every clinical trial hinges on its ability to enroll a set of patients according to a highly specific set of criteria. Further, when designing a clinical trial, certain trial design decisions can impact recruitment and retention rates. Around 85% of clinical trials fail to retain enough patients, while 11% of study sites fail to enroll even a single patient. Unsurprisingly, patient recruitment and retention are widely recognized as major bottlenecks in the clinical trial process – and some of the costliest. Slow or insufficient recruitment can delay the study and increase costs, while high attrition rates can compromise the validity of the results.
For example, for a phase 3 clinical trial design for a rare disease, finding enough eligible patients can be particularly challenging due to the limited number of individuals affected by the condition. Additionally, competition from other ongoing trials targeting the same patient population may further complicate the recruitment process.
To overcome recruitment challenges, clinical trial designers may employ strategies such as developing a strong patient outreach program or collaborating with patient advocacy groups.
To target patient retention issues, trial designers may consider incorporating patient-centric design elements, such as flexible visit schedules, the use of digital devices for remote data collection, and patient-reported outcomes, to improve participant engagement and retention.
Even before employing such strategies, trial designers and researchers can turn to historical data to assist them in the clinical trial design process. Utilizing historical data may reveal crucial insights that can help overcome or mitigate future recruitment and retention roadblocks.
By having a look at data points from similar studies in the past, such as
trial designers can make informed, data-driven decisions regarding site selection, endpoint selection, and more when in the trial design process. Armed with this data, trial design can be optimized for patient recruitment and retention later on.
While utilizing historical data to help drive optimized patient recruitment and retention is indeed a solution to mitigate recruitment and retention issues, collecting and consolidating this data (and other relevant data for trial design optimization) for use remains in itself a challenge.
Finding accurate and relevant data to use can be difficult for study designers, researchers and data scientists. While there has never been more information available as there is currently, never has such a large fraction of that data been so intangible. Bits and pieces of relevant data are scattered across registries, companies, departments, databases and many other siloes. The overall stream of information isn’t just too vast to plow through manually, it’s also fragmented and exists in too many hard-to-digest formats.
Despite this challenge, utilizing data from public data sources like ClinicalTrials.gov or EudraCT remains essential when designing a new clinical trial for maximum efficacy. Reviewing data from previous trials allows researchers and trial designers to learn from past experiences and gain insights into what worked and what did not. This knowledge can help them design more effective and efficient trials, avoid potential pitfalls, and build on existing evidence.
Public data sources can also provide information on the standard of care, interventions, and methodologies used in previous trials to help researchers and designers design their clinical trial in line with the best practices and adopt the most appropriate study design. And as mentioned above, assessing the recruitment strategies and eligibility criteria used in past studies can help researchers and designers optimize their own recruitment plans and select a suitable study population. This can improve the generalizability of the trial's findings and increase the likelihood of successful participant enrollment.
However, to get a comprehensive overview of current and past clinical studies for a specific condition, time period, or a combination of other search criteria, all public clinical data registries must be consulted. Unfortunately, consolidating the data in these databases is time consuming and often requires manual work.
In addition to consulting public data sources, leveraging internal data sources is just as essential when aiming to design for maximum efficacy. Analyzing past clinical trial data can help identify trends, patterns, and potential areas of improvement. This can lead to more efficient trial designs that minimize potential pitfalls or challenges faced in previous trials.
Regarding patient recruitment challenges, utilizing data from an organization's previous clinical trials provides valuable information on patient demographics, enrollment rates, retention rates, and treatment outcomes. This historical data can help inform the design of new trials, ensuring that they are well-suited to the target population and address the needs of the patient population being studied.
However, when consulting internal data, getting a comprehensive overview of all relevant data can be difficult due to silos and governance challenges. Moreover, it can be difficult for a designer or researcher to know what data even exists within their organization let alone where or how to access it.
On top of that, consolidating and consulting both public data and internal data at the same time for an even more informed overview can be practically impossible due to some organizations’ data management infrastructures and lack of interoperability.
To overcome the difficulties around consolidating and consulting public data and historical internal data, data management tools that enable integration from multiple sources and that allow for smart search and filtering are an ideal solution.
In clinical trial design, an endpoint is a predetermined outcome that is used to measure the effectiveness of a new treatment or intervention being tested. Endpoints are critical to the design of clinical trials, as they help to determine whether a treatment is safe and effective, and whether it should be approved for use by patients. Choosing the right endpoints is essential to ensuring that clinical trials are meaningful and provide accurate information about the treatments being tested.
While selecting the appropriate endpoints is crucial for the success of a clinical, it can be difficult to identify which endpoints are relevant to the disease being studied and clinically meaningful, while also being measurable. Moreover, selecting the wrong endpoints can lead to inaccurate results and can affect the treatment's approval and use in the population.
Additionally, once the endpoints are chosen, it is essential to define them precisely, including how they will be measured and analyzed. Ambiguity or lack of clarity in endpoint definitions can lead to inconsistent interpretation of data across the study sites, which can affect the trial's validity.
To ease the challenges associated with endpoint selection during the clinical trial design process, research teams can conduct a thorough literature review, engage with patients and clinicians, consult with regulatory authorities, use established and validated endpoints, and consider the practicality of the endpoints. Further, looking into historical clinical trial data may reveal alternative endpoints to consider that could help to quicken approval times.
While consulting literature and data about endpoints is indeed an important step in order to ensure optimized trial design, it can still be a difficult and time-consuming task. Compiling a comprehensive overview of literature and data for endpoint selection requires consolidating heterogenous data and information from across sources. Just as with consolidating internal and public data for patient recruitment purposes, utilizing a data management tool that allows for integration from multiple sources is essential. On top of that, a tool with advanced filtering and search capabilities will be ideal so that researchers and designers can quickly narrow down the information that is explicitly relevant to the study they are designing.
Designing a clinical trial for maximum efficacy is a complex and challenging process, with numerous factors to consider and obstacles to overcome. However, consulting relevant datasets and sources can considerably lessen trial design challenges, especially when it comes to tackling patient recruitment and retention obstacles. Utilizing data management and analysis tools to consult such data can further simplify the trial design process by enabling data-driven decision design and set-up.