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 second installment focuses on a fictional Trial Manager in a trial conduct scenario.

Situation: My name is Jean, and I’m the Trial Manager for Compound Y, a PCSK9 inhibitor that is the most promising product in our sponsor’s pipeline. This is a high-profile study for me to manage, and I am determined that it proceed on-time and on-budget. However, when I log into Saama’s Life Science Analytics Cloud (LSAC) one morning to track progress, I notice that enrollment has been red-flagged – we are behind by at least one quarter in 6 out of 10 sites.

Challenge: I need to figure out the cause of this delay as soon as possible, and what can be done to rectify it. Delayed enrollment can have serious implications for the conduct, timing and budget of the study. We’re only in the initial phases of start-up, and missed milestones at this point don’t bode well for future progress. I won’t allow this trial to fail on my watch.

Solution: I immediately click on each of the six sites in turn to assess the causes for delayed enrollment. I see that, in each location, patients are being screened at a good rate, but the screen failure rate is higher than expected, plus also notice that the number of patients discontinuing or dropping out of the trial is also above predictions. I also notice that the data entry in electronic data capture is on track, but the percent of source data verification and the number of incomplete data clarification forms are very high.

LSAC’s AI-driven capabilities enable me to drill down immediately, and I notice that all of these issues are seen in Spain, Belgium and Poland. Out of five countries participating in this study, the three European countries have the highest amount of variance. LSAC’s ability for outlier detection lets me zoom in on Spain, Belgium and Poland and examine these risk-prone sites in greater depth. I notice that the issues related to screen failure are consistent across sites run by Drs. A and B in Spain, Drs. C and D in Belgium, and Drs. E and F in Poland. Warning bells start to go off in my head – could there be a potential macro issue with the study protocol to account for these anomalies? I partner with the CRAs for these 6 sites and reach out to each site, and my hypothesis is unfortunately confirmed. The protocol is too restrictive in regards to inclusion and exclusion criteria.

I quickly create a report through LSAC depicting the key variables involved in the enrollment delays, and schedule time with my trial medical monitor later that day to discuss these variances and potential solutions.

Summary: Thanks to LSAC’s ability to provide a focused view into each country and study site, I was able to quickly and accurately identify the causal factors behind the enrollment delays. The platform’s unique and comprehensive problem-solving abilities enabled the trial sponsor to move accurately and quickly mitigate the risk, and consider a protocol amendment now versus waiting for more time and resources to be wasted.

To learn more or request a demo of Saama’s Life Science Analytics Cloud, click here. “Saama to the Rescue” will continue next week, when we take a look at the problems commonly faced by IT executives, and the insights LSAC can provide to inform quick and effective solutions.

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