PharmaSUG 2016 - Paper PO17 Standards Implementationn & Governance: Carrot or Stick? Julie Smiley, Akana, San Antonio, Texas Judith Goud, Akana, Bennekom, Netherlands ABSTRACT With the looming FDA mandate to submit CDISC compliant datasetss for regulatory review and approval, BioPharma companies are seeking better methods to implement and govern standards. Traditional standardss management practices have typically involved unstructured or semi-structured who develop policies andd procedures ( governance) around how the standards are to be managed and used. These processes generally include a set of spreadsheetss and other CDISC and internal standards that are read and interpreted by a group of standards experts templates, a relatively manual deviation request process, and auditing or compliance checks. With tools and processess like these, many programmers think of standards and governance as a stick meant to punish them. To them it is another set of burdensome tasks that add no value to their already full plates. However, if implemented using the right tools, processes, and automation, standards and governance can become a carrot or incentive by optimizing business process efficiency. This paper will cover how a metadata repository integrated with SASS and other systems can help change the perception of your organization by facilitating more efficient standards implementation and governance (Figure 1). It will outline how a metadata-driven approach to standards management can be used to automate business processes, enabling not only regulatory compliance, but also better resource utilization and improved data quality. INTRODUCTION Based on a short survey conducted at the CDISC International Interchange, regulatory compliance and business process efficiency were found to be important drivers for implementing standards and governancee 1. The traditional clinical development lifecycle (CDLC) used by most BioPharma companies follows a fairly linear approach (Figure 1) that has worked well in a controlled environmentt with paper driven processes. It is clearly defined and requires each step to be carried out before moving on to the next. However, it is slow and rigid, and therefore does not adapt easily to change or continued discovery and innovation 2. Figure 1. High Level, Linear Clinical Development Lifecycle As a means to address these concerns, the Clinical Data Interchange Standards Consortium (CDISC) was established to develop clinical data standards. CDISC is a standardss development organization (SDO) that partners with industry and regulators to develop data standardss that increase patient safety, data quality, and more efficient regulatory reviews. In 2013 the FDA published a position statement on Study Data Standards for Regulatory Submissions. This statement, preceded by similar language supportive of data standards in PDUFA V, makes it clear that FDA is firmly committed to CDISC dataa standards, which will be required for regulatory submissions for studiess starting in December 2016 or later 4 ). This desire for standardizedd (CDISC) dataa has also been picked up by the regulatory agencies from Europe (EMA) and Japan (PDMA), making implementation of data standards a matter of regulatory compliance. However, as regulation and the number of standards increase, so does the time and effort it takes to develop, manage, and distribute the standards in a way that results in the benefits intended. In addition, implementation of standards is often burdensome and expensive for industry due to their impact on business processes. As a result, organizations have historically performed duplicate late stage programming efforts to convert dataa to standardized formats for regulatory purposes to avoid disruption to their business operations. This inefficient approach resultss in longer cycle times, higher costs, and lower data quality. It can also limit an organization s opportunity to gain new scientific insight and effectively mine their data for safety signals. According to a joint analysis between CDISC and Gartner 3, the earlier standards are implemented within the CDLC, the greaterr the potential benefit. Figure 2 indicates the study activities where CDISCC standards can be applied. 1
Note: Items in red indicate activities that can be streamlined with CDISC standards. Figure 2. Clinical Study Activities By managing the standards, industry can implement more efficient end-to-end clinical lifecycle processes and generate reusable data that feeds the next wave of discovery and innovation efforts. TRADITIONAL STANDARDS MANAGEME ENT In the past standards were been made available by CDISC per clinical data lifecycle stage (e.g. data collection, data tabulation, data analysis) and in unstructured formats. To be able to use these standards internally companies needed to employ experts to interpret the documents and make them available in an electronic format. If they want to reap benefit from implementing standards end-to-end, companies need to additional create and manage the mappings and transformation logic for the standards across the clinical data lifecycle. To manage the standards, governance organizations are typically set up to develop processes to enforce standards usage, check compliance, and to handle deviations. Figure 3. Traditional Standards Management Each time new standardss are published the interpretation process needs to be repeated and consistency between different versions of the standards, as well as derived internal standards, needs to be reviewed and updated. Forming a governance organization can be difficult, especially in larger companies because of the global dispersion of the research & development locations and sensitivities between the different entities within the organization. This 2
can make the organization slow and rigid. Also when standards are implemented, end-to-end representatives from departments responsible for different parts of the clinical lifecycle need to agree on the best approach for deviations to the standards. This involves mutual understanding and readiness to compromise on department processes to accommodate cross-functional needs. To date governance processes are generally manual and include compliance checking using a set of spreadsheets and other templates, and meetings to evaluate implications of deviations. With toolss and processes like these, many programmers think of standards and governance as a stick meant too punish them. To them it is another set of burdensome tasks that add little value to their already full plates. IMPROVED STANDARDS MANAGEMENT In order to overcome the challenges with the current standards implementation and governance practices, standards and metadata for artifacts created and used throughout the clinical development lifecycle, including the mappings and transformations, should be managed in a centralized repository to facilitate their automated reuse. This enables consistency in standards implementation, reuse, business process automation, impact analysis, and governance. As illustrated in Figure 4, rather than the experts manually interpreting the standards and internal derived specifications, new versions of standards or (trial) amendments flow in to the standards repository. Definitions already present in the repository that did not change in the new version or amendment are reused, and applicable changes are implemented in a controlled fashion. The repository should then include governance workflows to review what derived standards will be impacted, perform impact analysis and notify impacted standards owners/stewards about inheritancee options. Standards/Metadataa Repository Change control EDC SASS Gov c workflows Impact Analysis & Inheritance CDW Other... Figure 4. Standards Governance using a Metadata Repository Integration with systems used for clinical study activities, such as Electronic Data Capture (EDC) or statistical analysis (SAS), facilitates creation, management, and use of data standards through automation. Study specificc specifications can for example be pushed to EDC to create a study library or database and at the end of the study the EDC metadata can be imported back in to the repository to check standards compliance. Also mappings and transformation standards managed and governed in the repository can be used by SAS to automatically transform raw datasets into the SDTM format. This approach to standards implementation and governance does not change the high-level business processes, but alleviates the burden by automating and improving quality with centralized management of the clinical data lifecycle, including data lineage and transformations. The Figure below illustrates an examplee of what this might look like for processing data tabulation datasets. 3
Figure 5. Data Tabulation Process using a Central Metadata Repository Rather than the statistical programmer manually interpreting the statistical analysiss plan, data tabulation standards, and prior study datasets, s/he has those definitions already in a centralized repository and can reuse them without having to retype/create them manually. S/he uses the existing conten to create a new set of metadata to represent the study specific datasets and validation specifications, re-using all of the existing metadata content that applies, and only adding items that are missing. Once the metadata for the study dataset specificationss is complete, all of the new content can be reviewed in a metadata governance workflow. Upon approval the SDTM datasets can be rendered based on management of the transformation logic combined with SASS macros to read that logic and automatically generate the datasets, resulting in shorter overall cycle times, more consistency, and greater quality. By managing the standards and operational metadata for each of thee artifacts in the clinical development lifecycle, the lifecycle becomes much more flexible because items are managed at a more granular level, but are defined more broadly, thus facilitating automated reuse. As part of this example, the transformations from raw to SDTM datasets are now managed as an artifact itself and used by SAS. Also, since many of the transformations are based on standards, the definition and structure are already defined. As a result, the programmer has much less manual work and can start it earlier while the statistical analysis plan is still being developed without the risk of duplicating effort if changes are made to the plan before it is finalized. Further, since usage of the standards and operational metadata is being captured, if changes like an amendment occur, all stakeholders will automatically know which artifacts are impacted and how, and they can iteratively apply the necessary changes under a traceable and well-governed process. The programmer will not only know that the SDTM datasetss need to be updated, but will know exactly which datapoints on which version(s) of the datasets need to be changed. CONCLUSION: A STICK OR A CARROT? While the BioPharma industry has long been talking about standardss and automation of businesss processes as a means to accelerate drug discovery and development, governance and standards compliance is still often viewed as an additional burden to existing study programming. In addition to study level programming, standards governance needs to be put in place to manage the compliance to regulatory expectations. However if managed well, standards and metadata can be used to drive automation of business processes like database creation, CRF annotation and SDTM dataset generation, resulting in shorter cycle times, higher quality submissions, and overall cost savings as illustrated in i Figure 6. By automating the use of standards within the organization with the implementation of a metadata repository solution that supports the capabilities discussedd in this paper, programmers can change their 4
view of governance from being a stick that punishes them to the carrot that motivates them by providing tooling that simplifies their processes. Figure 6. Benefits of using a Central Metadata Repository 5
REFERENCES 1. Goud, J. Metadata Management: The Big Drivers for a Solution Available at: https://resource.akana.com/white-papers/pharma-metadata-management-drivers 2. Smiley, Julie. Moving to a Standards-Based, Agile Clinical Development Lifecycle Available at: https://resource.akana.com/white-papers/moving-standards-based-agile-clinical-developmentlifecycle 3. Rozwell, C., Kush, R.D., Helton, E. Newby, F. and Mason, T. Business Case for CDISC Standards: Summary Available at: http://www.cdisc.org/stuff/contentmgr/files/0/ff2953ea8dbc8e81080f0e44ba6714c7/misc/ businesscasesummarywebmar09.pdf 4. US Department of Health and Human Services Food and Drug Administration, Providing Regulatory Submissions in Electronic Format Submissions Under Section 745(a) of the Federal Food, Drug, and Cosmetic Act; Guidance for Industry Available at: http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm384686.pdf CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: Name: Julie Smiley Judith Goud Enterprise: Akana Akana Address: 12100 Willshire Blvd, Suite 1800 6-9 The Square City, State ZIP: Los Angeles, Los Angeles, CA 90025 Stockley Park, UB11 1FW Country: USA UK Work Phone: +1 210 710 2901 +31 (0)6 1241 7026 E-mail: Julie.Smiley@akana.com Judith.Goud@akana.com Web: https://www.akana.com/solutions/healthcare SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. 6