Optimizing Safety Surveillance During Clinical Trials Using Data Visualization Tools

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1 Optimizing Safety Surveillance During Clinical Trials Using Data Visualization Tools Laura McKain, M.D., Medical Director, Pharmacovigilance; Tammy Jackson, Director, Preclarus Development, Clinical Innovation; Cindy Elko-Simms, M.D., Vice President, Pharmacovigilance, of PPD Pharmaceutical companies must continuously monitor the safety of investigational products in development for adverse events that may be unexpected, occur at an increased frequency or severity, or result in an unexpected outcome. Ongoing safety signal detection leads to optimal patient protection and is essential to obtaining regulatory approval. Adequate surveillance requires integration of safety data from multiple sources, across multiple trials and even multiple indications during clinical development in order to characterize the developing safety profile. New data visualization tools enabled by the adoption of data standards allow for interactive reviews of safety information that make required surveillance more comprehensive and efficient. Traditional Safety Surveillance Traditional clinical drug safety assessment is accomplished through the review of data listings, summary tables and figures. Data collection is planned jointly by crossfunctional members of the study team including pharmacovigilance and biostatistics. Programmers produce standard listings, tables and figures that allow the study team to investigate the results. There may be a significant lag between data entry and the delivery of formatted data to the reviewers. It can be challenging to anticipate in advance the optimal format for reviewing the data since there may be many data sources to consider. Because data may not be integrated within listings, reviewers often have to manually correlate data across listings, which can be very time consuming. If potential safety trends are detected, the team may require more in-depth analysis to explore potential mechanisms or associations. This hypothesis testing may require additional programming or laborious manual review of the data, potentially adding time to process and delaying actions to protect patients from risk. Data Standards Enable Use of Better Tools Data standards facilitate efficiency and enhanced quality by structuring how studies are designed, as well as how data are collected, transformed and analyzed. Though not a part of national regulatory agencies, the standards developed by the Clinical Data Interchange Standards Consortium (CDISC) are recognized by the U.S. Food and Drug Administration (FDA) and other regulatory bodies as the preferred standards for our industry. Being able to rely on CDISC data benchmarks not only creates process efficiencies, Page 1 of 6

2 but also enables innovation. For example, having a standard study data tabulation model (SDTM) domain for key clinical trial data enabled the creation of a visualization template the Preclarus patient data dashboard that can be deployed to surface safety information across all studies and indications. Through the collaboration between CDISC and The Coalition for Accelerating Standards and Therapies (CFAST) companies are given the opportunity to contribute to the creation of the standards during the public review phase. As a result of these combined efforts, multiple CDISC therapeutic area standards have been released, which have further facilitated the ability to develop standard dynamic visualizations for exploration of therapeutic specific data in near real time. Graphical Visualization Functionality Patient data dashboards allow for large amounts of data to be integrated from multiple sources and displayed coherently, as outlined in table 1. Reviewers easily can navigate from aggregate data to the patient level in an intuitive manner. Dynamic features allow for rapid manipulation of the data so reviewers can investigate possible safety trends without relying on ad hoc programming for initial interrogation. If a possible signal is identified, it then can be interrogated using validated data and additional methodology. Aggregate Review of Events Page 2 of 6

3 Reviewers can use the patient data dashboards to browse aggregate blinded data to assess the frequency, severity and outcome of events. Not only can they establish event frequency in individual trials, but they also can examine occurrences among pooled data and across indications by simply selecting the trials they wish to examine. The aggregate displays contain a menu of filters that allows the user to assess whether there are safety issues specific to particular age groups, genders, ethnicities, underlying medical conditions or use of concomitant medications. They can use filters to explore subsets of events, such as serious adverse events, events rated as severe intensity, or those deemed causally related to or leading to discontinuation of the investigation product, as portrayed in image 1. This ability to dynamically explore the data ensures that emerging trends are identified and tracked early in the clinical development program so actions to protect patients can be taken in a timely fashion. Explore Trends in Laboratories and Investigations The dashboards allow laboratory data to be investigated so that clinically significant lab test results can be quickly identified. Graphical displays like those in image 2 detailing the change from baseline make outliers immediately evident. Laboratory data can be integrated with other parameters such as dosing and the occurrence of adverse events, so trends can be identified. The data can be displayed using special formats to investigate particular safety concerns, such as Page 3 of 6

4 liver toxicity, while filters can be applied to examine trends in special populations. It s also possible to select patients with laboratory values in a particular range and rapidly visualize in aggregate the types of adverse events experienced by that subgroup or whether there are commonalities in their medical history or use of similar concomitant medications. Drill Down to Individual Patient Data One of the most powerful features of the patient data dashboards is that they allow reviewers to rapidly navigate from aggregate level data down to patient level data. For example, the tool allows for review of individual patients who: Experience a particular adverse event Have outlying laboratory values, vital signs or ECG findings Discontinue the study or investigational product prematurely Have particular medical history Again, even at the patient level, data are displayed graphically and multiple data sources are integrated so events and findings may be visualized in a temporally organized visual format. In the example in image 3, the timing of the reported adverse events related to liver function are correlated visually with study drug Page 4 of 6

5 dosing and selected laboratory findings. The result is a rich graphic narrative of the patient s entire course during the trial. The tool also allows for simultaneous visualization of multiple patients at one time in order to identify common trends or characteristics. The cost of developing new pharmaceutical products is enormous and the investment in a program that must be halted due to a safety concern or in a product that must be removed from market due to a previously undetected safety event can be substantial. It is critical that sponsors take measures to fully understand the emerging safety profile of their drug during clinical development. The use of standardized data within a data visualization tool enables thorough and efficient exploration of the product s benefit/risk profile and ensures that information can be accurately presented in the new drug application. Sponsors would be well advised to embrace this technology as a means of prioritizing patient safety, identifying potential concerns earlier than traditional methods and improving efficiency within their clinical development programs. Source URL (retrieved on 10/06/2015-1:21pm): Page 5 of 6

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