Jessica Rine

	With nearly 80% of clinical trials failing to meet enrollment timelines, a better, more efficient approach to trial feasibility is critical. Fortunately, the availability of new analytics tools and an increase in data sources has led many companies to adopt a data-driven approach to decision-making during the feasibility process. Yet while this approach can have a massive impact on a trial’s success, oftentimes it’s not enough: Many companies’ efforts to utilize data in their decision-making are often manual, fragmented, and implemented inconsistently.

	How can companies develop and implement a data-and-analytics-driven approach to clinical trial feasibility and, ultimately, improve the success of their clinical trials? To help companies make evidence-based, end-to-end feasibility decisions, we’ve identified seven key building blocks for success: 

Understand the true patient and epidemiology landscape. 

Real-world data can be used for a variety of purposes in trying to identify the highest concentrations of patients. Epidemiology data can help companies develop heat maps and create a detailed picture of where eligible patients might be located, and which clinical sites and physicians are treating them. Real-world data can also be used to simulate medical events and symptoms to identify patients’ rare diseases. Social media data can be used to employ automated social listening techniques, supplementing RWD to identify eligible patients. You can use RWD to identify undiagnosed or underdiagnosed patients with diseases that physicians often struggle to identify. It can also be used to simulate medical events and symptoms to identify patients for rare diseases, which is an unmet need in the market.

Leverage competitive intelligence and landscape assessments. 

Understanding the competitive landscape is an essential step in the clinical trial feasibility process. A competitive intelligence assessment can help you choose investigators who have capacity or are participating in complementary rather than competitive trials. A competitive landscape assessment can help predict key metrics like the number of patients, sites, and investigators available for your trial, and the projected enrollment rates at a country and site level. A thorough understanding of a particular indication will help you create a plan for a trial that takes all other interventions, whether marketed or investigational, and their treatment guidelines into account.

Develop an effective enrollment forecast. 

Enrollment forecasting is an area where companies typically make critical errors. An aspirational enrollment timeline not supported by data can end up significantly impacting trial budgets. Start by looking at the availability of clinical trial patients based on eligibility criteria, and then mapping sites and investigator-to-patient ratios. Then, integrate all of this data to predict enrollment rates. Poisson-gamma and Monte Carlo are two commonly used methods for predicting enrollment curves. Accurate enrollment forecasting can also allow for accurate budget forecasting. Being able to complete these activities with precision can enable more efficient spending and better use of internal resources.

Identifying eligible patients using real-world data can help you determine which countries or regions are best for patient enrollment. Local clinical trial history can be derived either through open data sets, a company’s own internal data, or ideally a combination of the two. Data on average startup times or trial costs can help you decide where to conduct a particular trial based on your timelines and budget.  

Let data drive global site and investigator selection. 

In addition to external benchmarking data and internal data on historical site performance, companies also have data on site quality and investigator experience that can be factored into site selection decisions, such as average query resolution times, protocol deviations, etc. This data can be compared with historical benchmarks to get an understanding of how your company’s metrics compare to competitors.

A comprehensive understanding of the KOL landscape is an essential component of site selection, as having influential physicians spread out across a geography can drive referrals from the community to treatment centers. Beyond traditional KOL mapping techniques, you can utilize artificial intelligence and machine learning to identify influencers based on authorship of papers and practice guidelines, clinical trial participation, and editorial and leadership positions with journals and professional organizations.

Use global drug and asset profiles for your development strategy. 

Analyzing drug development data is important for head-to-head studies to know where comparable drugs are approved, how much they cost, what their approved label information is, and so on. Looking at competitive biosimilars, generics and branded agents’ approval status and market size should help inform a development strategy.

	Current methodologies for assessing trial feasibility won’t carry companies into the future. As our society continues to rely more heavily on data and digital solutions, companies that don’t implement data-driven decision-making will be left behind. On the other hand, clinical programs that leverage data science and analytics during the feasibility process will be positioned to find the right patients at the right sites and in the right regions. 

Venkat Sethuraman, Sharma Ramanathan Deva Devesa, 

Articles

These seven key building blocks for success are outlined to help companies develop and implement a data-and-analytics-driven approach to clinical trial feasibility.

Jessica Rine

Articles

How Data and Analytics Can Improve Clinical Trial Feasibility