Improved Data Quality & Integrity for Faster Regulatory Approvals

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1 Improved Data Quality & Integrity for Faster Regulatory Approvals Medidata and other marks used herein are trademarks of Medidata Solutions, Inc. All other trademarks are the property of their respective owners. Copyright 2015 Medidata Solutions, Inc.

2 2 A study examining all new molecular entity (NME) applications first submitted to the FDA between 2000 and 2012 found that several potentially preventable deficiencies account for significant delays in the approval of new drugs. 1 The difference in approval time between NMEs receiving first cycle approval and those requiring multiple cycles is 17.9 months, so any delay can materially impact revenues. 1,2 Moreover, estimates show that up to one in six NMEs fail first cycle approval due in part to data integrity issues. Data quality and integrity issues are leading contributors to deficiencies in clinical trials. 3,4 Assessing data quality prior to regulatory submission and database lock can save months of manual investigation and surface unknown data integrity issues that would otherwise materially impact a submission. 5 Figure 1: Impact of poor data quality on regulatory approvals for NMEs Current Approaches Curr ent approaches to data quality that analyze late in the game or only at the site level create the conditions for data errors to slip through the cracks undetected, that is, until they are flagged in an FDA review. Moreover, approaches that rely heavily on manual investigation eat up time both before submission and in responding to FDA queries, contributing to the 17.9 month lag between submission and approval. Organizations would place themselves in a better position for regulatory review if they adopted a data quality approach that avoids these pitfalls. This approach should: Identify errors earlier in the clinical trial Find errors that you didn t know to look for Better explain errors down to the patient level For a cost-effective, efficient approach, analysis should also be automated. By incorporating these characteristics into its data quality approach, an organization can better prevent avoidable mistakes and improve the likelihood of first round regulatory approval.

3 3 Medidata Trial Assurance: Preventing avoidable mistakes How Medidata Trial Assurance Works Medidata Trial Assurance captures each of these essential characteristics with its expert service teams, proprietary algorithms and automated analyses. The service uses our patent-pending statistical algorithms to mine all necessary clinical databases and automatically identify study, site and subject level anomalies, outliers, potential fraud or misconduct and procedural issues enabling sponsors to work more effectively and attain faster, safer clinical trial data reviews. Medidata Trial Assurance integrates data from clinical and lab systems to provides a holistic report for each subject, making it easier for teams to detect and track critical data changes throughout the execution of a clinical trial. Organizations will better position themselves for regulatory review if they avoid common data quality pitfalls by adopting an approach that: Finds errors they didn t know to look for Identifies errors earlier in the clinical trial Better explains errors down to the patient level Identify errors earlier in the clinical trial. Medidata s Trial Assurance assesses data quality with fewer data points (patients and sites). It detect errors at a patient level even for sites with single patient enrollments. Organizations don t need to wait until critical mass enrollment; they can begin analysis and address anomalies early. Find errors that you didn t know to look for. Trial Assurance correlates all types of data including non-numeric data across case report forms. While statisticians program for a problem, Trial Assurance s algorithms make it possible to find problems that haven t been programmed and would otherwise go undetected till regulatory review. Better explain errors down to the patient level. The Trial Assurance Service automatically creates patient profiles to visualize patient history using data across several sources, including clinical and lab. This ensures that a small group of patients exhibiting a problem at each site is not lost at a site level comparison, as the example below demonstrates.

4 4 Make Actionable Insights Accessible and Easy to Understand Medidata Trial Assurance integrates and analyzes data across multiple domains, generating patient-centric reports to allow for quick and easy clinical trial review. It leverages trial data to create and display a customized report and interactive visualizations of patterns, such as the relationship between adverse events and concomitant medications along with visit dates and time on study drug. Medidata Trial Assurance includes a comprehensive analysis, a report and detailed presentation of results. It provides our customers with immediate actionable insight to improve clinical trial performance and data quality. Medidata Trial Assurance can lead to faster regulatory approval and protect your blockbuster from avoidable failure. Figure 2: Medidata Trial Assurance Trial Assurance in Practice Drawing from our experience in data analysis, Medidata biostatisticians have developed a process that analyzes both clinical and laboratory data to find patterns in a study regardless of the disease area or type of study design. Trial Assurance grades the study and individual sites for data quality based on how well the data follow the identified patterns. The following example illustrates sample errors resulting from data variability discovered during a Medidata Trial Assurance engagement.

5 5 Figure 3: Errors due to data variability Medidata Trial Assurance includes a comprehensive analysis and detailed presentation of results. It provides our customers with immediate actionable insight to improve clinical trial performance and data quality. Summary In this specific example we found that one site had a strange pattern in the blood pressure data. Three-quarters of the patients at this site had unusually stable diastolic blood pressure: it was always equal to 80. Just a flat line. Source data verification cannot pick this up because the source data will likely also say 80. Though blood pressure may not seem like critical data points, the fact that the site is not properly completing the assessments could indicate other, potentially larger quality issues at those individual sites. Using standard statistical tools for analysis on the entire set of patients, these two values would not have been identified as unusual. Medidata Trial Assurance analyzes patients at individual sites as well as across all sites. This ensures that a small group of patients exhibiting a problem at each site is not lost at a site level comparison. The development and use of improved statistical tools to maintain quality control and quality assurance become more important as costs of clinical trials continue to rise. The approach outlined in this paper should be widely used, since it has the ability to ensure the data quality and data integrity of clinical trials in all regulatory environments.

6 6 Top 5 Questions You Should Ask Are you concerned About the integrity and quality of data you have and its impact on your results and regulatory approval? 2. With the amount of time that it takes to review data quality and data integrity prior to database lock? 3. That you do not have the best tools to help with assessing data quality? 4. That you are spending too much time on low value tasks, such as cross-referencing spreadsheets in Excel, and not enough time on important high value tasks, like meaningful clinical interpretation? 5. About not having a comprehensive view of laboratory and clinical data in assessing data quality in the right context? If you have answered yes to any of these questions, it s time to call Medidata and discuss this new approach to assessing the data quality and data integrity of your clinical trial. Medidata Trial Assurance helps you protect your blockbuster from avoidable failure. About Medidata Medidata is the leading global provider of cloud-based solutions for clinical research in life sciences, transforming clinical development through its advanced applications and intelligent data analytics. The Medidata Clinical Cloud brings new levels of productivity and quality to the clinical testing of promising medical treatments, from study design and planning through execution, management and reporting. We are committed to advancing the competitive and scientific goals of global customers, which include over 90% of the top 25 global pharmaceutical companies; innovative biotech, diagnostic and device firms; leading academic medical centers; and contract research organizations. info@mdsol.com mdsol.com Sacks, Leonard V., et al. Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, , Journal of the American Medical Association. 2014; 311(4): Mills, George, et al. Why NMEs and Therapeutic Biologicals Fail in the First FDA Review Cycle, The RPM Report. March Bhatt, Arun. Quality of clinical trials: A moving target, Perspectives in Clinical Research. 2011; Oct-Dec; 2(4): Institute of Medicine. Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making: Workshop Report. Washington, DC: The National Academies Press, Medidata Clinical Cloud Cloud-based clinical research solutions Innovative technology Data-driven analytics Reduced costs Improved time to market Faster decisions Minimized risk