Finding the Sweet Spot

Oct 01, 2009
By Gen Li


(JOHN FOXX/GETTY IMAGES)
New drug development is risky because it involves testing potentially lethal compounds on vulnerable and frequently sick patients. The dangers are enhanced by the growing insistence of regulators that potential medicines be tested on ever larger populations based on hard-to-quantify characteristics of health status, age and gender, and in diverse geographic settings that are often unfamiliar to the sponsors. As part of our continuing coverage of best practices in the conduct of clinical trials, Pharm Exec asked a leading trial management expert to examine how quantitative measurement and analytics can enhance the predictability of what is arguably the most daunting challenge facing sponsoring companies—patient recruitment.

Numerous surveys show that finding the right patients to participate in a clinical trial can spell the difference between success and failure in getting a promising compound blessed by regulators and launched to the market. Poor recruitment strategies pump up costs, divert scarce resources, consume more time and ultimately delay these very patients – and tens of thousands of others – prompt access to the medicines they need. An accessible and feasible quantitative approach to patient enrollment that helps sponsors identify specific opportunities and improve their procedures is long overdue.

Traditionally, patient enrollment has been measured by the number of patients enrolled per site per month, which is called the "enrollment rate." This measure implies that patient enrollment is a site performance issue. However, site enrollment performance is not the only factor that drives enrollment cycle times. Experience buttressed by data indicate that the sponsor's business processes, as measured by indication-specific site activation timetables, is actually the determining factor in accelerating enrollment cycle times. Therefore, the traditional way of viewing patient enrollment – being determined by site performance alone – is incomplete. Site activation, an important part of clinical trial start-up activities, actually plays the key role influencing overall enrollment cycle time.

MEASURING SITE ACTIVATION

There are different ways to measure effectiveness in trial site activation. The cycle time of site activation is one. Site activation starts from the time the first site is activated or initiated, and ends when the last site is activated. The less time between the first site activation and the last site activation, the more effective the overall activation process will be. As in the process of enrollment, site activation cycles depend on many variables, making it difficult to compare one clinical trial against another.

Recently, PhESi developed the concept of the Site Effectiveness Index (SEI), which defines how effectively selected sites in a clinical trial are utilized. There are two ways to measure SEI. One is the percentage of selected sites open for enrollment over the duration of the enrollment cycle time. Another is from a single participating site perspective; its percentage of time open for enrollment compared to the overall clinical trial enrollment duration. As a percentage, SEI is always represented as a number larger than zero and smaller than one. A real-world case study will help explain the concept of SEI.


Table 1: Comparison of Clicinical Trials A and B
Clinical Trial A and Clinical Trial B are two Phase III metabolic disease clinical trials in the same indication with the same design, sponsored by two different companies. By drawing two charts, we see the site activation curves for the two clinical trials along the time line. (See Table 1.)

Clinical Trial A activated 44 sites while Clinical Trial B initiated a total of 204 sites. It took less than four months for Clinical Trial A to reach the peak of site activation (i.e., for all 44 sites to be actively recruiting), while it took about 10 months for Clinical Trial B to get its selected sites up and running.


Table 2: Measures of Site Activation
Because Clinical Trial A and Clinical Trial B are similar in many ways, any deeper insight requires a methodology to compare the two trials. To achieve that, we define the X axis as the percentage of enrollment duration and the Y axis as the percentage of maximum number of sites activated, instead of the actual number of sites activated. (See Table 2.) By doing that, we see that area underneath the black colored line (Clinical Trial A) covers more than half of the chart area, and the area underneath the red colored line (Clinical Trial B) covers less than half of the chart area.

The two straight lines tell us that more than half of the Clinical Trial A's 44 sites' enrollment capacities were utilized, compared to less than half of Clinical Trial B's 204 sites' enrollment capacities. Using a specific mathematical formula, we can calculate the Site Effectiveness Index (SEI) is 0.55 for Clinical Trial A, and 0.45 for Clinical Trial B. That means that if we were able to improve site activation in Clinical Trial B to be as good as in Clinical Trial A, we would need only 167 sites to get the same job done, instead of 204 sites. That's a reduction of 18 percent of the sites selected.