Optimize clinical feasibility planning with a data-driven, indication-level approach

The number of registered clinical trials has increased significantly in the last five years, but even so, less than 5% of the addressable population participates in research and 80% of trials suffer delays while clinical research sites continue to underperform. When reviewing the R&D pipeline, we see that large biopharma companies have moved away from a highly diverse portfolio to instead focus on a few therapeutic areas and indications. Meanwhile, their study teams engaging in feasibility planning for a portfolio of studies continue to take a study-centric approach and operate in silos, often unaware of symbiotic trials in their own organizations. Teams that operate in a disparate fashion have a negative impact on clinical trial feasibility planning, as they compete for the same resources, create redundancies and cost their companies opportunities to build strong relationships with clinical trial sites.

Thankfully, there’s a better way forward. At ZS, we have found that a data-driven approach at the indication level can help optimize feasibility planning and offer benefits to any team. A strategic operational planning approach that is data-driven enhances how companies:

• Identify studies that can be conducted together
• Develop mutually beneficial relationships with sites
• Optimize site outreach

Business units, asset leads and study planning teams should consider how they can take a robust, proactive approach to align feasibility planning with their overall business strategy. We recommend study teams shift their mindset to focus on indication-level planning, rather than using a study-centric model. This can include categorizing studies based on therapy area, inclusion and exclusion criteria and similar parameters to ensure that country and site-level decision-making is aligned with overall business strategy.

Specifically, teams can take a structured approach by implementing a study grid map (see Figure 1) that identifies competing and complementary trials. Study grid mapping is an effective way to determine early in the feasibility planning process which studies can be run together and which need to be run exclusively. Asset leads and study planning teams in the same therapy area can perform study grid mapping to visualize the ongoing and planned trials in the portfolio. This exercise should be revisited as conditions change and new assets or studies are planned.

Sponsors can choose studies, countries and site allocations—and eventually develop strong relationships with sites—by using metrics in conjunction with the study grid mapping exercise.

The first step is to understand and map the universe of available research sites and the clinical trial activity for a given therapeutic area or indication level. An integrated data strategy with historical trial data and real-world data or real-world evidence can help predict capacity for known research sites. This strategy can also help determine the probability for clinical trial activation for research sites—whether it be clinical trial sites or referral centers—through quantitative network mapping.

Next, develop a list of ranked sites and a customized portfolio site engagement strategy. When ranking the sites, we recommend committing to the top ones for multiple studies. Pre-selecting sites allows studies to get up and running faster and makes it more natural to develop preferred partnerships with top performing sites. Preferred status and central partnerships will ensure prioritization of studies during times of high competition and make it easier to contract with big centers or network hospitals.

For new or untapped sites, it’s important to build complete profiles, onboard and train teams and finalize site contracts. This can help reduce cycle times for site startup and increase participation rates, which offsets saturation in highly competitive portfolios. This also helps achieve diversity, equity and inclusion goals.

Finally, mapping out site capacity close to the time of study initiation can help create an optimization model that allows your company to allocate and reallocate sites for different studies. We recommend understanding competitors’ site partnerships and preferences, as they can be helpful as you determine your company’s needs.

Rather than creating individual surveys for each study, drafting one survey workflow with sites segmented by profile can reduce administrative burden for both the sponsor and the site. To make the number of questions more dynamic, use branching logic to customize the site engagement experience. Teams can avoid asking redundant questions by creating a repository of previous survey responses and by only adding questions to new surveys that are truly needed.

Sites should be given some level of flexibility to choose which studies they want to run. Sponsors often don't consider a site's perspective when making these choices but seeking their input can help break down silos and forge relationships.

Using a strategic approach and leveraging data to drive feasibility planning at the indication level is worth exploring for pharma companies of all sizes. Four noteworthy benefits of such an approach include:

• Efficient utilization of resources: You can reduce operational, administrative and financial burdens while enhancing portfolio-level strategies that reduce cycle times and accelerate patient recruitment.
• Informed decision-making: It’s easier to choose the right site the first time, which leads to increased study site participation rates.
• Long-term site relationships and partnerships: Seeking buy-in from sites and effectively using their resources will help to forge long-term relationships.
• Process efficiency: When selecting a country and site, data makes it possible to move away from a transactional, study-centric approach to a strategic one. This allows teams to optimize internal and external resources, as well as R&D investments.

These are just a few of the many reasons ZS is helping clients transition to a strategic, data-driven decision-making model for feasibility planning at the indication level. As data capabilities become more robust in the coming years, the case for leveraging them will only grow.