A common struggle for pharmaceutical companies is poor patient recruitment for clinical trials. As a consequence, 80% of clinical trials fail to meet their enrollment goals, suffer delayed timelines and absorb skyrocketing R&D costs. Ultimately, the patient suffers from delays in the release of needed therapies. Currently, pharmaceutical companies are partnering with trial matching startups to enhance clinical study design, matching and data collection. With analytics and platforms, pharmaceutical companies are beginning to streamline the clinical trial process in hopes of increasing new entrants to the market.
We have identified four components that would aid in site selection and optimization to yield elevated site performance and patient engagement:
- Use a data-centric approach to select sites. Data sources such as clinicaltrials.gov, WHO registries, EudraCT and PubMed serve as a foundation to supplement sponsors’ historical data on archetype sites and provide context on the trial landscape. To narrow down the list of potential sites, primary and secondary filters can be applied using comprehensive KPIs that are measurable parameters for robust patient recruitment performance. These KPIs can include historical PSM rates, screen failure rates, previous trial experience, indication and current trial load. The interim site list can vary by site counts and location due to scenario parameters such as timing, enrollment caps, market authorization and competition (internal and external) at the local site level. Consistently implementing and automating the use of data in these decisions can prove to be a valuable advantage in the competitive trial landscape.
- Complement registry findings with institutional knowledge. Although registries and historical data will provide the foundation for site selection with quantitative data such as screen failure and enrollment rates, institutional knowledge from stakeholders can provide qualitative knowledge that highlights site features which may not be recorded in data sets such as investigator’s trial preferences. Institutional knowledge sums all the capabilities of investigators within a given indication and can provide a competitive advantage when selecting sites. Lastly, institutional knowledge provides validation by sponsors and stakeholders to ensure that sites will fit within their trial design and operations strategies. Once validated, the interim lists will contain primary and backup sites that will serve as the site variables in each enrollment and feasibility analysis.
- Leverage predictive modeling for enrollment projections. Predictive modeling requires additional parameters that serve as variables in the modeling analysis. These parameters include but are not limited to: site activation dates, PSMs, planned subjects, countries and trial start dates. The predictive modeling uses Poisson-gamma distribution for enrollment rates over various time periods. While the industry has used Monte Carlo simulations, the Poisson-gamma model provides an analysis of completion times and probability of subjects enrolled with time. Various outcomes can be assessed and quantified, such as last patient enrollment timelines and market entry, with corresponding enrollment projections. These outputs may suggest limiting or adding sites to ensure timelines are met.
- Engage in a continual refresh based on site performance. Once the trial commences, site performance must be monitored to uncover patterns in enrollment for different site types. By tracking sites, enrollment projections can be adjusted based on real-time data (screen failure and enrollment rates) provided by CRA contacts. Gathering this information aids in identifying which sites have low performance rates and identify site archetypes. This allows the trial team to pivot their strategy and prioritize sites for their engagement efforts and request feedback on the issues that sites are facing. With the feedback, sponsors can plan visits to motivate the staff and meet with investigators and coordinators to brainstorm how to improve screening activity. If these efforts are not successful, then sponsors must replace these under-performing sites with a high potential backup identified through a data-centric approach and validated by institutional knowledge. The insights should be captured and further applied to the subsequent trials within the same program.
These four concepts use data-centric approaches to aid in clinical trial planning, leveraging site performance with CRO support and using predictive modeling to assess feasibility and ensure trial success. In addition to assessing and monitoring site performance, there are patient and physician-centric levers that sponsors can pull to identify and resolve issues in enrollment. Sponsors can leverage patient and physician quantitative and qualitative research to understand barriers to successful trial completion by identifying their unconscious biases. These behavioral insights can aid in message development geared towards promoting patient recruitment. Lastly, developing a robust physician referral strategy will ensure access to more patients and help in being distinctive in a competitive trial space. Combining all these techniques will strengthen forecasting and optimization capabilities while making poor patient recruitment a thing of the past.