Saama continues our new blog series, “Saama to the Rescue,” in which we profile common, real-life challenges encountered by various levels of clinical trial leadership every day, and how Saama solutions can pave the way for more efficient and effective clinical operations.

We recognize that, in any clinical study, trial leadership at every level have an abundance of details to manage, people to supervise, deadlines to meet, and budgets administer, among other things. What trial leadership does not have is an abundance of time to solve the inevitable challenges that arise in relation to these various moving parts. Saama can help.

Our fifth installment focuses on a fictional Trial Manager in a trial planning scenario.

Situation: My name is Jean, and I just had my regular one-on-one meeting with my Portfolio Lead. He tells me he has a meeting later today with a Therapeutic Area (TA) Lead who wants to discuss plans to pursue a new indication/study for Compound Y, a PCSK9 inhibitor that is the current star of our company’s portfolio. He needs the information by 2pm, and Dr. X from the clinical team has been assigned to work with me.

Challenge: I need to ensure that my Portfolio Lead is prepared for this last-minute meeting, so Dr. X and I must come up with a detailed estimate on the number of sites and countries, as well as protocol feasibility, for this newly proposed clinical Trial. Having worked with Dr. X before, I know that he has certain ideas about how things should be done. However, because of the quick turn-around needed, we don’t have the time for lengthy debates today.

Solution: I meet Dr. X in his office, and we quickly go through our Clinical Trial Management System (CTMS) to identify a few historical studies, picking two. Dr. X pulls out those two protocols, while I log in to Saama’s Life Science Analytics Cloud (LSAC). LSAC’s AI-driven capabilities allow us to start simulating the protocol’s inclusion and exclusion criteria and obtain a quick view into the enrollment funnel and how it would be affected by each criteria. As usual, Dr. X wants to add more MRIs scans. I challenge him by pulling up the complexity index through LSAC, which demonstrates the impact additional MRIs will have on protocol execution. He’s impressed with what the platform can show, and we are able to quickly proceed.

Once we are happy with the trial design, we move to the next part of LSAC to create a heat map of countries and sites where potential patients exist. We structure the map to also depict the sites’ historical performances and predictions on how they would perform in this new trial, the treating clinics which could be referral centers, and other relevant information. Dr. X and I agree that we are happy with where we are at in terms of design, number of countries and sites needed, and the rate of reaction, backed by LSAC-provided data for the same.

I walk my Portfolio Lead through the data, toggling back and forth with the LSAC to demonstrate why we made certain decisions and assumptions. He is very satisfied and heads to the meeting. By end-of-day the verdict is in: the meeting went very well, the committee was impressed with the options and data provided, and the study is likely to come up for funding request later that week.

Summary: The AI-driven capabilities of Saama’s LSAC enabled Dr. X and me to easily and rapidly determine the proposed trial’s logistical details and overall feasibility. With LSAC, we were able to simulate different scenarios of the protocol design while balancing operational efficiency. The result was compelling, insightful data and scenarios that equipped my Portfolio Lead to effectively paint a comprehensive picture of the potential trial.

This concludes the “Saama to the Rescue” blog series. To learn more or request a demo of Saama’s Life Science Analytics Cloud, click here.