How four data-driven protocol design analyses can increase chances of study success in dermatology clinical trials

Nearly 60 percent of clinical trials have protocol amendments,¹ often required to address shortcomings in the study design, endpoints which do not support study objectives, excessive assessments which do not support study endpoints,² or eligibility criteria. Such amendments are a major undertaking, leading to significant timeline delays and additional cost. It is imperative to understand the parts of a clinical trial protocol that could cause potential problems, proactively address them, and maximize the likelihood of study success.

In dermatology clinical trials, one of the most common protocol amendments is the removal of unnecessary procedures. Research has found that 35 percent of procedures in a typical phase III trial are for non-core data, making up a quarter of study costs and costing the industry $4-6 billion annually.¹ Beyond the cost, collection of unnecessary data has implications for recruitment and retention of study subjects. Dermatology participants are of relatively good overall health, and often work or have family commitments. High numbers of procedures or site visits can cause disruption to their lives, and increase the burden placed on patients who participate in clinical trials. Extraneous procedures and excessive visits can impact a patient’s willingness to enroll in a trial and can contribute to noncompliance and patient dropout. According to GlobalData, low accrual rates account for 19 percent of study failures in psoriasis studies, 17 percent in atopic dermatitis studies, and 28 percent in melanoma studies globally.³

Avoid the potential impact of protocol amendments

The potential impact of an unoptimized study protocol, and subsequent amendments, on enrollment, retention, execution, and costs are clear. Fortunately, it has been shown that nearly half of all protocol amendments are avoidable.¹ Utilizing appropriate real world patient data and design analytics can highlight areas for optimization, allowing sponsors to gather the right data from the start.

Four analyses needed to optimize your dermatology clinical trial protocol

1. Design consistency analytics

   It is important that protocols are audited for internal consistency, to ensure each objective has a matching endpoint with appropriately associated measurements. When designing clinical trials with similar objectives to previous studies, it can be tempting to use past trial designs as a template. This can lead to the inclusion of procedures that may not match clinical endpoints chosen to support the objectives of the study. For example, the percentage change of body surface area (BSA) is a gold standard endpoint for the efficacy objective in many dermatology study designs. However, BSA must be supported by the correct measurement tools, or the results cannot be determined with enough confidence to support the endpoint.

2. Patient and site burden analytics

   Dermatology clinical studies encompass a wide variety of indications, including rare diseases, and there may be a limited number of investigators and sites able to conduct the trial. With a smaller pool of qualified sites, investigators can be much more selective with the trials they choose to run, leading to competition to attract participants. A burden analysis is an effective way to get a clear picture of study design impact from the site and patient point of view. Streamlining the study to decrease burden will help increase the likelihood of sites taking on a trial and successful patient recruitment. This analysis highlights potential activities that could be condensed or eliminated to reduce burden without impacting the overall objectives or endpoints, or actions that could improve patient satisfaction, compliance, and retention.

   Eligibility criteria and expected screen failure rate provide a strong indicator of recruitment potential. Through the use of real world patient data, various inclusion and exclusion criteria can be analyzed, and their impact on study integrity and enrollment can be quantified. This is particularly useful for clinical trials in rare dermatologic diseases, as recruitment can be challenging due to a small population of patients with the required disease profile.⁴ Without sufficiently assessing eligibility criteria, many participants could be excluded who are in the target population and relatively healthy, but do not meet all criteria.

3. Study procedure analytics

   As noted previously, it is important to identify extraneous and costly procedures in trial protocols to support the improvement of patient recruitment and retention. If patients learn that invasive procedures or frequent visits are required, they may be deterred from taking part, be noncompliant, or may drop out of the study. Furthermore, procedures that are unnecessary or conducted at inconsistent timepoints can increase site burden.

4. Competitor trial analytics
   
   It is important to compare a planned trial design to those of recent trials in the same indication and phase. This analysis will review similarities and differences in choice of primary and secondary endpoints, use of placebo, and/or eligibility criteria. Sponsors may then see how their design is unique and whether there are gaps in the design. Evaluation of competitive designs help guide decisions that can positively impact positioning of your molecule.

Optimize your dermatology clinical trial design

Ultimately, conducting a clinical trial is an expensive and time-consuming enterprise, and many variables can impact the outcome. It is critical to begin planning early to set up your study for success. By using data-informed analytics to pressure-test your study design early, you can minimize the risk of protocol amendments, and gain efficiencies in cost and execution.

To discover how our data-informed protocol assessment can optimize your dermatology protocol and trial design, please contact us today.

References

1. Getz KA, et al. The Impact of Protocol Amendments on Clinical Trial Performance and Cost. Ther Innov Regul Sci. 2016;50(4):436-441. Available at: https://journals.sagepub.com/doi/abs/10.1177/2168479016632271

    

2. Crowley E, et al. Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project. Trials. 2020;21(1):535. Available at: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-020-04388-x

    

3. GlobalData. Clinical trial data for psoriasis, atopic dermatitis, and melanoma. Accessed March 2021. Available at: https://www.globaldata.com/

    

4. Prodinger C, et al. Profiling trial burden and patients' attitudes to improve clinical research in epidermolysis bullosa. Orphanet J Rare Dis. 2020;15(1):182. Available at: https://ojrd.biomedcentral.com/articles/10.1186/s13023-020-01443-3