DRIVING CLINICAL DATA STANDARDS BEYOND COMPLIANCE

Transcription

1 SMARTER DRUG DEVELOPMENT DRIVING CLINICAL DATA STANDARDS BEYOND COMPLIANCE Binding FDA requirements take effect in 2016 will your data standards platform do more than simply comply? INTRODUCTION In December 2014, FDA released the finalized Guidance for Industry, Providing Regulatory Submissions in Electronic Format Standardized Study Data. This guidance requires that studies that start after December 18, 2016, must incorporate certain data standards per the FDA Data Catalog. 1 Of particular note are the FDA-approved versions of the CDISC Study Data Tabulation Model (SDTM v3.1.2) and Analysis Data Model (ADaM v.1.0) standards. This was followed in May 2015 by guidance that sponsors must electronically submit (in ectd format) all NDAs, ANDAs, BLAs and master files as of May 5, 2017, and INDs beginning May 5, For all of these submission types, sponsors must electronically submit any amendments, supplements and reports, even if the original submission was submitted to FDA prior to implementation of the electronic submission requirements. While the FDA began accepting certain submissions electronically as early as 2003, and approved CDISC SDTM 1.0 as a data format for submission in 2004, the above regulations are notable in that they are binding. Thus, non-conformance may result in FDA refusal to review a submission (there is a very limited list of study types that can obtain waivers or exemptions). The FDA s data standards and electronic submissions guidance is planned to expand and evolve over time, particularly with respect to specific therapeutic area guidance documents under development. THE FDA S DATA STANDARDS AND ELECTRONIC SUBMISSIONS GUIDANCE IS PLANNED TO EXPAND AND EVOLVE OVER TIME, PARTICULARLY WITH RESPECT TO SPECIFIC THERAPEUTIC AREA GUIDANCE DOCUMENTS UNDER DEVELOPMENT. Leading biopharma companies have been working closely with industry groups (such as CDISC) and the FDA on supporting the evolving development of data standards and electronic submission requirements. 1 The FDA Data Standards catalog can be found online at 2 See

2 Nonetheless, given the magnitude and breadth of the requirements, companies should identify and address any capability gaps so that they are prepared to be fully compliant. While it may be most expedient to focus on quick-fix solutions that address any immediate compliance needs, we believe that these latest regulations should motivate all sponsors to ensure that they have an up-to-date, comprehensive data standards strategy in place. As outlined herein, we recommend adoption of an end-to-end data standards platform as the foundation of this strategy. The data standards platform should dynamically interface with the various data management tools and processes needed to perform specific objectives, such as electronic submission. Fully embracing this approach requires a paradigm shift from a solely compliance-driven approach to data standards to a more strategic approach that leverages the full power of standards to enable efficient clinical development. WHAT ARE THE CURRENT DATA MANAGEMENT PAIN POINTS AND HOW DOES AN END-TO-END DATA STANDARDS APPROACH HELP SOLVE THESE ISSUES? Clinical trial teams today often spend a significant amount of time on customizing the data ecosystem 3 for each trial in a manner that does not provide benefit or re-use beyond that individual trial. Examples of such customization include use of trial-specific libraries and ad-hoc integration coding. Furthermore, clinical trial teams today are often faced with delays at critical milestones (such as database lock) due to missing data or datacleaning issues, or experience delays during the submissions process to resolve data-quality questions from regulatory authorities. Finally, downstream users of clinical data often find it difficult to perform more sophisticated analyses (e.g. cross-trial comparisons) due to metadata or data inconsistencies. 3 Ecosystem is defined broadly here as the processes, tools, systems, etc. used to capture, assemble and make useable various forms of data to support clinical trials. 2 North Highland Life Sciences Perspective

3 PLAN & SET UP COLLECT DATA ANALYZE & REPORT SUBMIT EXPLORE Protocol Statistical Analysis Plan ecrfs Analysis Datasets Tables, Lists, and Figures Define.xml files for ectd Cross-trial and patientlevel analyses FIGURE 1 - Clinical Research Deliverables Enhanced By Leveraging Standardized Content An end-to-end data standards platform is defined here as a platform that can efficiently support the entire data lifecycle from data capture in the clinic through submission, follow-up reporting and exploratory analyses (see Figure 1 above). Such a platform enables the use of a common data structure and data elements and the reuse of standards-defined content across multiple trials. It also ensures quality and consistency by incorporating built-in checks against reference data sets.* A major challenge is designing the standards infrastructure so that it can support the multiple existing systems that support data management needs from electronic data capture (EDC) to analysis packages such as SAS DD. Different data models are needed to support needs of varying complexity. This ranges from data capture on a per-subject basis, to analysis tables and figures of all subjects or sub-populations, to fully integrated ready-for-analysis data sets that integrate data from multiple sources. The latter must also include all necessary derived data, and be delivered with appropriate metadata. Ideally, an end-to-end standards platform can support all of these needs without customized mapping. AN END-TO-END DATA STANDARDS PLATFORM EFFICIENTLY SUPPORTS THE ENTIRE DATA LIFECYCLE, AND ENSURES QUALITY AND CONSISTENCY BY INCORPORATING BUILT- IN CHECKS AGAINST REFERENCE DATA SETS. * Note that the platform encompasses both data and analysis standards, but we use the shorter term "data standards platform" here to cover both. 3 North Highland Life Sciences Perspective

4 WHAT DOES A COMPREHENSIVE DATA STANDARDS PLATFORM LOOK LIKE? There are a several key components that are essential to a robust clinical data standards platform (see Figure 2 below). At the core of the platform is a library of standards entities. Each organization needs to assess how extensive a library to build, but at minimum it should incorporate key modules covered in the pending FDA regulations such as SDTM, ADaM and textual components. The standards platform also has to be designed to offer options and variants to managed standards, since no one standard will fit all clinical trial needs. Keep in mind that the goal is not to standardize all activities, but to optimize benefits by standardizing the most commonly used elements of the clinical trials data ecosystem. THE GOAL IS NOT TO STANDARDIZE ALL ACTIVITIES, BUT TO OPTIMIZE BENEFITS BY STANDARDIZING THE MOST COMMONLY USED ELEMENTS OF THE CLINICAL TRIALS DATA ECOSYSTEM. Having all the right standards modules defined and documented is only part of the solution. The key to standards adoption is ease of use. Standards consumers (e.g., data management personnel, statisticians, programmers) must be able to easily access and apply the standards that are applicable to the specific clinical programs they are working on. To enable adoption, the user interface should be designed to provide an efficient click-to-value (i.e., a minimal number of user actions is required to execute a particular task). Clinical Data Warehouse CTMS INTERNAL INTERFACE etmf Internal Systems Internal Reference Standards EXTERNAL INTERFACE Industry Reference Standards Library of Standard Entities User Interface Validation Tool External (CRO) Systems STANDARDS MANAGEMENT AND ADMINISTRATION FIGURE 2 - Conceptual Diagram For A Clinical Data Standards Platform 4 North Highland Life Sciences Perspective

5 A complete standards platform should include a validation tool to ensure ongoing alignment with the FDA Data Catalog or any relevant industry standards from sources like CDISC. Additional consideration should be given to any necessary validation against internal company standards or other important reference benchmarks. Validation tools provide additional value in key areas such as standards development, trial development and regulatory submissions. More advanced validation tools can be leveraged to ensure that the standards framework remains intact and that standards quality is maintained throughout the clinical lifecycle. Another important consideration is the compatibility (or even more sophisticated integration) of the standards platform with various external systems that support the overall data management activity. The systems include data-capture tools, data- analysis tools, and document-authoring tools. As part of the standards management function, appropriate business processes should be defined so there is a consistent methodology for applying and integrating the standards as part of clinical development workflows. These processes should be referenced in a formal Standards Adoption Policy that can be used as a guide for internal trial teams and external partners such as contract research organizations. Like any system that is operationalized, establishing processes and administration is the backbone for success. Any new processes should be nimble, easy to understand and provide a clear roadmap for activities. Similar to the user experience highlighted above, burdensome processes will hinder adoption. The new standards platform must perform better than the historical ways of working. A COMPLETE STANDARDS PLATFORM SHOULD INCLUDE A VALIDATION TOOL TO ENSURE ONGOING ALIGNMENT WITH THE FDA DATA CATALOG OR ANY RELEVANT INDUSTRY STANDARDS FROM SOURCES LIKE CDISC. Finally, the standards effort should include provisions for ongoing standards administration (stewardship and governance) to ensure ongoing alignment with regulatory guidance and any internal policies and procedures. Special care should be taken in the development and alignment of the administration roles as they can impact and require interface with several clinical functions. 5 North Highland Life Sciences Perspective

6 WHAT TYPES OF CHALLENGES AND CONSIDERATIONS SHOULD YOU PREPARE FOR? While adoption of a standards-driven approach to data management clearly can provide quality and efficiency advantages, special attention needs to be given to implementation approach. In particular, what is the best way to apply any new tools and processes to ongoing programs in early development phases? What is the best way to handle legacy data? The potential for disruption of ongoing studies and the high cost of legacy data conversion needs to be evaluated against the likelihood of reaching the submission phase and benefits of providing standardized data sets to regulatory authorities with the initial submission. A major barrier to adoption is the inertia of historical processes and ways of doing work that are perceived on a functional level as efficient but actually hinder broader optimization. To ensure adoption of a standards-driven approach that can address these issues, users must be convinced that the new approach will not require them to invest additional time and effort in the study set-up process. Moreover, the way in which clinical trials are conducted and the manner in which data is generated and processed has changed considerably over the last five to 10 years and continues to evolve. Some of these developments may create challenges and add complexity to a data standards program. The important point to keep in mind here is that standards should always be an enabler and should not be imposed in a way that hinders adoption of innovative clinical development strategies or technologies. STANDARDS SHOULD ALWAYS BE AN ENABLER AND SHOULD NOT BE IMPOSED IN A WAY THAT HINDERS ADOPTION OF INNOVATIVE CLINICAL DEVELOPMENT STRATEGIES OR TECHNOLOGIES. The shift toward increasing use of contract research organizations (CROs) requires decisions around cross-organization data sharing and standardization. Furthermore, new types of trials and trial designs may not neatly fit into current models of data standardization. Examples include adaptive trials, multi-year mega outcomes trials and multidrug trials with complex patient stratification protocols. Finally, ongoing advances in mobile technology, wearable devices and point-of-care testing may require new ways of data capture and collection not contemplated by current standards. 6 North Highland Life Sciences Perspective

7 HOW DO YOU GET FROM HERE TO THERE? Designing and implementing a comprehensive end-to-end data standards platform is a very challenging task. One of the biggest challenges is that there are not many robust tools and solutions available in the market today that provide the needed capabilities out-of-the-box or with minimal configuration. Vendor and technology solutions must be carefully evaluated in light of an organization s existing maturity with respect to standards and capacity for change. We recommend taking a phased approach that can deliver concrete benefits in incremental steps. Nonetheless, a comprehensive needs assessment and environmental scan is a critical first step in order to develop an integrated approach that is aligned with business needs and priorities. Some specific tips are: Don t jump too quickly into content development. First understand key interdependencies to avoid doing things out of order. For example, it is important to define metadata and metadata validation needs early in the process. Build in flexibility for standards consumption (e.g., ability to easily extract snippets of information from a given standards document). Focus on features that ensure a high level of user adoption, using techniques such as click-to-value analysis that measures how much user effort is required to generate a desired output or result of benefit to the user. THE DIRECT BENEFITS OF ADOPTING AN END-TO-END DATA STANDARDS PLATFORM ARE REALIZED IN THE AREAS OF QUALITY, EFFICIENCY, AND REDUCED CYCLE TIMES. Incorporate design parameters (e.g., open architecture, robust API layer) that ensure long-term adaptability to changing future needs. Furthermore, as a prerequisite to implementing a technology solution, a clinical development organization should focus on changing its data quality mindset from clean at the end to building quality in from the beginning. For example, a typical practice today is to develop a trial build by marking up a build from a previous trial that is similar. This practice propagates errors from the previous trial and does not allow for inclusion of any updates to reference standards. With a comprehensive standards platform in place, a user could instead build the new trial quickly from fully current libraries based on certain key selection parameters. 7 North Highland Life Sciences Perspective

8 WHAT ARE THE BENEFITS OF AN END-TO-END PLATFORM? The direct benefits of adopting an end-to-end data standards platform are realized in the areas of quality, efficiency (cost savings) and reduced cycle times. By building quality in from the beginning, trial teams will eliminate costly and time consuming data-cleaning steps at critical milestones later in the clinical development process. Efficiencies are realized through less rework, fewer time-consuming manual steps (due to automation of processes such as metadata validation), and simplified data transfers with CROs and other external partners. In addition, the organization as a whole will see reduced variability across trials, which will facilitate cross-trial and patient-level analysis. Clinicians and support staff will have more time to perform complex data exploration and analysis, rather than spending time on operational activities. A robust data standards platform also provides indirect benefits by enabling complementary capabilities that rely on the standards foundation in order to work properly. Examples include sophisticated tools for Authoring and Redaction (allowing creation and management of clinical documentation to be quickly and easily integrated with standards elements) and Metadata Browsing (to facilitate rapid search and retrieval of any type of metadata for application to a specific trial or analysis). WHY NOT MOVE FROM BEING COMPLIANCE- DRIVEN INTO A FORWARD-THINKING, STRATEGIC APPROACH? Recent FDA guidance documents on data standards and electronic submissions provide an opportunity for companies to reassess their approach to data standards. Rather than simply taking a compliancedriven approach, forward-thinking companies can derive significant benefits by adopting an end-to-end data standards platform. This should be implemented as part of an overall strategy to embed high-quality data right from the beginning of the clinical development process, thus avoiding costly and time-consuming data-checking, coordinating and cleaning steps later in the process. 8 North Highland Life Sciences Perspective

9 ABOUT NORTH HIGHLAND North Highland is a global management consulting firm that delivers unique value, relevant big ideas and strategic business capabilities to clients around the world. The firm solves complex business problems for clients in multiple industries through an integrated approach, and offers specialty services via its Data and Analytics, Managed Services, and Sparks Grove divisions. North Highland is an employee-owned firm that has been named as a Best Firm to Work For every year since 2007 by Consulting Magazine. The firm is a member of Cordence Worldwide ( a global management consulting alliance. For more information, visit northhighland.com and connect with us on LinkedIn, Twitter and Facebook. To learn more or to have a discussion about your clinical data standards, contact: Bob Pietrobono Vice President, Life Sciences Practice Lead bob.pietrobono@northhighland.com Alan Susser R&D Lead alan.susser@northhighland.com Or visit us at: Copyright 2016 The North Highland Company. All Rights Reserved.