Simulated Strategies for Better Trials

Getting patients and doctors into clinical trials has become the most delay ridden aspect of the drug discovery and development process over the past 10 years. Patient and investigator recruitment and retention are often cited as the most costly and time consuming aspects of trial operations.¹ However, there is good news for managers seeking new ways to facilitate the overall clinical trial process, especially enrollment of subjects. Simulation exercises, made possible today by customizable decision support technologies and well-defined predictive analysis strategies, can help clinical trial managers cut costs, reduce risk, forecast outcomes, and increase efficiency.

Simulation solutions

Increasingly, pharma companies are turning to simulation—a merger of strategy, analysis, and technology that enables them to run through virtual scenarios of their major initiatives. A new application of this technology is in the critical area of clinical trials. The end tools allow the user to forecast the resources needed, predict and adjust the anticipated costs, establish realistic goals, measure and mitigate their risks, and in the end make better business decisions. For example, the solution can be used in conjunction with existing or expected recruitment data to help predict how patient recruitment sites will perform based upon a predetermined protocol using a set of input templates.

Photography: Getty Images Illustration: Paul A. Belci

In this context, the protocol is a study plan on which all clinical trials are based. The plan is carefully designed to safeguard the health of the participants as well as answer specific research questions. By definition of the U.S. National Institutes of Health, a protocol describes the following:

• What types of people may participate in the trial

• The schedule of tests, procedures, medications, and dosages

• The length of the study.

Decision support technology can run multiple replications of many different scenarios that take into account the clinical trial process's variability and complex resource interdependence. These scenarios generate realistic data on how a company's subject recruitment process will perform. This includes enrolling recruiting sites, setting up training sites, getting subjects recruited, dosing them, seeing them through the entire cycle, and then closing out the clinical trial.

Implementing decision support

Companies that want to implement a simulation strategy must first determine if they have a need. To gain the greatest benefit, a company engaging in simulation should have more than one clinical trial per year. It should also be a relatively sizable mid cap or a larger firm that runs clinical trials frequently to justify the time and effort required for implementation.

Second, trial managers should communicate with other key participants and decision makers early in the study to determine if there is a specific need to complete a trial on time and within a certain budget—and what those parameters are. It is critical that trial managers talk not just to the doctors and scientists conducting the study but also to the senior management in the company and other trial managers.

The next step, data evaluation, has a significant impact on how simulation is carried out in the clinical trial. Companies and their consultants or technology providers must review the available portfolio data and determine if it is sufficient to support full predictive analysis. It's important to know how long a particular site takes to get up to speed, when it's fully trained, when it starts enrolling subjects, and what possible delays could arise.

Performance Measures Plot

Many times individual sites have their own data, or the pharmaceutical company has collected data from past experiences. Relevant information includes site type, delays to recruitment of the site, delays to training of the site, different cycle times, and more. The accuracy of the data regarding delays to site qualification and rate of subject enrollment is critical to the success of the trial. The risks of using data that is based on estimates rather than actual results can be mitigated by expanding the bounds of the distributions that are used for these entries in the simulation.

If a company and site have no data of their own, consultants will create a template of similar sites in the same country and give it a suitable range of variability to accommodate margin of error. For example, if you were to look at doing training and site recruitment in Germany, the parameters, regulations, and cycle times that are specific to that country would need to be considered in order to achieve the most accurate results possible. Both approaches share the same goal: to ensure data is accurate and dynamic so that companies can arrive at realistic and actionable answers.

Distributions are then built around the data and put into a decision support model. They capture a company's designated recruiting process and timeline and ensure that it fits within the boundaries of a given tool set.

Clinical Trial Milestone Summary Table

Push and pull

Once the protocol is prepared and approved and the data fully entered, testing begins in one of two ways. The "push" method involves putting the information in the simulation technology tool and running multiple scenarios to determine project deadlines. This produces site enrollment and subject recruitment curves with future confidence intervals.

The "pull" method, on the other hand, tells managers the best set of inputs for reaching a given goal and achieving cost savings. First, project leads define all possible sites and related costs. Then they run a genetic goal-seeking algorithm optimizer to blend the effects of site qualification requirements, screening volume and losses and startup and subjects costs.

This step helps determine the optimum test center combination for a given protocol and often at the lowest possible cost or the earliest required date since these two measurements are often mutually exclusive. It also takes into account the delay time from site selection until the start of subject enrollment due to regulatory requirements for each site (see Figure 1).

The information is typically relayed on a scaled up recruitment prediction curve that pinpoints the cycle's milestones and goals. This includes first subject first visit, first dosage final subject, final dosage, and the end point event or the point when the final subject exits the study population.

Decision support technology can be expanded to include the subject screening protocol for most therapeutic areas. This option is helpful in cases where data is available on the different criteria that can result in subjects being disqualified from participating in the clinical trial. This data can be combined with the projected patient screening volume to projected subject enrollment in the trial. The technology then tracks this population of randomized subjects until they complete the designated requirements for treatment and monitoring (see Table 1 and Figure 2).

Return on investment

Simulation will provide the maximum value in cases where there is a choice regarding the selection of trial site enrollment. Simulation can aid in identifying the group of sites that will best achieve the trial objectives for subject enrollment using both the trial completion date and the trial cost as the objective functions of the optimization process. Once the enrolling sites have been chosen and the trial is underway, this tool can continue to provide value by combining both actual and projected subject enrollment to ensure that the projected schedule is met or exceeded.

If problems occur and result in subject enrollment falling below expectations, simulation can aid in minimizing its impact on the overall trial. The comparison between actual and projected enrollment will not only make it possible to identify problems at an early stage but such a comparison can also can be used to evaluate the best option for recovering from the problem, by evaluating whether it would be helpful to supplement the original list of enrolling sites.

Effect of Nonproducing Test Centers on Subject Enrollment

While simulation is used most effectively from the beginning of a clinical trial, it can be implemented at any stage of the trial. Once the enrolling sites have been characterized for subject enrollment rates, simulation can be used to create a subject enrollment curve along with a projected completion date. This curve can be used to continue to track the progress of the trial and function as both an early warning indicator and a corrective action method in a manner similar to that noted earlier.

Conclusion

In short, the practice of simulation in clinical trial subject enrollment enables managers to capture test center variability in a single cohesive summary. And it brings a single flexible tool to companies that can track and address any situation that arises in the organization, granting greater visibility over the entire project lifecycle.

Companies also gain tomorrow's history today through predictive analysis. Simulation can help identify counter intuitive events in their systems, such as sites that offer high volume enrollment but may not be the best choice if they have lengthy qualification and enrollment startup delays. It can also provide an early indication of when and where trials are derailed. This awareness can be combined with additional simulation scenarios to quickly identify options to help the trial recover and complete its objectives with a minimal disturbance to the original targets.

In addition, the program's results help trial managers produce more realistic, accurate goals. This makes the trial's management case much stronger before senior executives and increases the chance that the trial will receive support. It also facilitates cost avoidance, where companies can recognize a project's viability early in the process. If it won't be economically feasible to reach the subject recruitment goals in any realistic time frame, management can sunset the project and save valuable resources.

Lastly, adopting simulation strategies provides risk mitigation. Companies that are new to the clinical trial process or embarking on a trial for a disease that they've never targeted before are presented with the opportunity to gather realistic, projected answers to some of their most pressing questions:

• How long will this trial take?

• Who do I need to participate?

• How much is it going to cost?

This, in turn, can alleviate the risks of choosing the wrong site and subjects, underestimating the clinical trial's time frame, and going significantly over budget.

Even at its best, the site and subject recruitment process is highly variable and risk-ridden. Clinical trial simulation helps pharmaceutical companies find the right answer the first time around—and much more quickly than traditional methods. When implemented effectively, clinical trial recruitment initiatives such as trial simulators significantly reduce timelines and meet recruitment targets ahead of schedule. And that means the product is one step closer to hitting the marketplace and achieving rapid return on investment, the ideal outcome.

References

1. Cutting Edge Information, "Clinical Operations: Accelerating Trials, Allocating Resources & Measuring Performance" (2006), http://www.cuttingedgeinfo.com/clinicaltrialbenchmarking/index.htm.

Kurtis E. Shampine is vice president and general manager at ProModel Life Sciences Solutions, 7540 Windsor Drive, Suite 300, Allentown, PA 18195, (860) 443-8882, email: kshampine@promodel.com.