Biomarker Data Management in Clinical Trials: Addressing the Challenges of the New Regulatory Landscape

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Darrell Parsons
6 years ago
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1 Biomarker Data Management in Clinical Trials: Addressing the Challenges of the New Regulatory Landscape

2 Introduction Biomarkers and specialty labs are core to modern clinical trials.the broad and evergrowing set of assays can range from targeted panels to high-content or high-throughput experiments. Biological techniques are seemingly boundless and continuously evolving, especially in complex areas such as oncology, immunology, and genetics, with data being generated from flow cytometry, next generation sequencing, immunosequencing, mutational analysis, gene or protein expression, immunohistochemistry, circulating tumor cells, cytogenetics, and others. Evaluating biomarkers in clinical trials and integrating specialty lab data with PK, safety labs, and clinical data provide a more complete picture for assessing drug efficacy and safety. The process presents a unique challenge for drug developers, however, as they simultaneously endeavor to conduct innovative clinical research while complying with regulatory requirements for submission. Prior to 2017, there was some flexibility in how data could be submitted to FDA; this changed in December 2016 when FDA s binding guidance document on study data exchange standards, issued in December 2014, went into full effect. Any study beginning after December 17, 2016 must use the appropriate FDA-supported standards, formats, and terminologies specified in the FDA Data Standards Catalog (see section II.C) for NDA, ANDA, and certain BLA submissions. The current catalog specifies use of the CDISC SDTM, SEND, ADaM and Define-XML standards, as well as CDISC Controlled Terminology. When it comes to specialty lab data, the data exchange standards in many cases are still developing, and SDTM programming typically requires expert input to determine how complex lab data can be mapped appropriately. Even when implementation guides exist, mapping requires an in-depth understanding of the complexity, quality control, and processing that are appropriate for each assay in order to transform raw data files into CDISC-compliant data sets.

3 A Case Study in Preparing Specialty Lab Data for FDA Submission The key challenges and steps involved in transforming specialty lab data into CDISC-compliant data sets that conform to FDA data exchange standards can be illustrated in the context of a phase 1/2 dose escalation and expansion study of an immuno-oncology (IO) compound. Phase 1/2 Study Objectives On-study Pull together in real-time, visualize and report on specialty lab data in context of other labs and clinical data to aid in clinical decision making End of Study Generate and deliver CDISC-compliant data sets for specialty lab data to be used in analysis and submission, with both rigor and fast turnaround 4 specialty lab assays coming from 3 specialty labs, and other protocol required lab tests coming from local labs at clinical sites LAB 1 LAB 2 LAB 3 PK lab work and multiplex cytokine panel Gene expression by Nanostring Immunophenotyping by flow cytometry Clinical sites and local labs Safety lab data hematology, chemistry PK lab data Specialty biomarker assays - High-content flow cytometry - Multiplex cytokine panel - Gene expression measured by Nanostring 3

4 Managing Lab Data: Use of Technology and the SDTM Workflow For context on the challenges of processing specialty lab data and transforming these data into CDISC-compliant data sets, the typical workflows for local labs and PK data are illustrated (Figure 1) with consideration to differences in data processing (eg, technology driven vs manual) and SDTM mapping (eg, single, well-defined domain vs multiple, more complex domains). Of particular importance is the use of EDC technology and lab management tools in the more established workflow for local labs. By extension, technology engineered for specialty lab data could have a similar impact on industry s ability to deliver specialty lab data under the same regulations in an efficient, high quality manner. Figure 1. Source Data Processing SDTM Workflow SDTM Domains Local labs at clinical sites - Hematology - Chemistry Within EDC system - Technology-enabled data processing in EDC (queries, edit checks, remote review) - Lab management tools to manage reference ranges Single domain - Generally most complex domain for data coming from EDC, but significantly less complex than specialty labs - SDTM programming is part of typical DM and programming workflow Laboratory Test Results (LB) Specialty PK group - Drug concentrations over time - Modeling generates other PK parameters (e.g., C max, t max, AUC) External to EDC system - Typically manual processing by PK group - Basic reconciliation with EDC, but remainder is handled separate from CDM workflow Multiple domains - Generally more complex than local labs and requires coordination with PK group - SDTM programming for PK may be handled by PK vendor further coordination Pharmaco-kinetic Concentrations (PC) Pharmaco-kinetic Parameters (PP) Related Records (RELREC) Specialty biomarker labs - Multiplex cytokine panel - High-content flow cytometry - Nanostring gene expression External to EDC system - Typically manual processing, often by translational team - Highly specialized QC and processing pipelines - Handled separate from CDM workflow and often not fully processed until post-study Multiple specialty domains - CDISC implementation and controlled terminology is partially defined (e.g., SDTM IG-PGx) - Historically, these biomarker data have been treated as exploratory and not included in the data submission to FDA Laboratory Test Results (LB) Custom Domains Biospecimen Events (BE) Pharmaco-genomic Findings (PF) Pharmaco-genomic Biomarker (PB) Biospecimen Findings (BS) PGx Methods and Supporting Information (PG) Subject Biomarker (SB) Related Specimens (RELSPEC) 4

5 Challenges of Handling Specialty Biomarker Data in SDTM Workflow 1. Complexity in biomarker assays. To enable mapping of raw data into SDTM, extensive processing is typically needed that requires in-depth understanding of the biological assay. For example, NanoString technology outputs Reporter Code Count (RCC) files that require sample level checks (RNA integrity, field-of-view ratios, and binding densities), background correction, and normalization to obtain usable gene expression values. 2. Lack of structure in biomarker data upstream of SDTM mapping. Generally, the source biomarker data will be delivered in disparate file formats with inconsistent structure across assays and data sets. This is further compounded by the lack of standards across labs. All of this combined makes it difficult to standardize downstream programming pipelines. 3. Meeting submission timelines. Delivery of submission-ready data sets for downstream use is a time-sensitive component of activities post database lock. Simply adding the handling of complex, often messy biomarker data within the traditionally rigid, process-driven SDTM workflow without consideration to new ways of dealing with these data is a recipe for failure. Historically, specialty lab data has been out-of-scope of the rapid turnaround delivery schedule post-database lock, but this is no longer the case. Applying New Technologies to Managing Specialty Lab Data and SDTM Mapping Biomarker data being submitted to the FDA will be subject to FDA data exchange standards for regulatory submission, making it critical to organize these data effectively and efficiently as part of the end-of-study activities. Additionally, biomarker data are often used to support on-study decisions. Given this dual role, development of a robust end-to-end solution for managing biomarker data needs to consider regulated objectives, such as SDTM programming for analysis and submission, as well as provide flexibility to meet on-study needs, such as data visualization and reporting for safety review, data monitoring, and decisions on maximum tolerated dose. Similar to how EDC technology helped revolutionize clinical data management, a technology-based solution for biomarker data management is now required to meet the needs of modern clinical trial operations. However, technology alone is not enough to achieve success in biomarker data management; it also depends on biomarker subject matter experts and associated biomarker data management processes that provide a rigorous, agile biomarker data management system for clinical trials. 5

6 This new model harmonizes disparate sources of biomarker data and stores them in a centralized database for more effective on-study and downstream use. From that point, experts with knowledge of specialty labs and CDISC standards can map biomarker data to the correct SDTM domain. This approach, as illustrated in the chart below, produces downstream efficiencies through more effective variable mapping and development of reusable macros and codes. Beyond enabling timely delivery post-database lock, it also provides for maximal use of biomarker data to inform decisions throughout the study by providing clinical trial professionals and sponsors with centralized, on-demand access to biomarker data. Figure 2. Electronic Data Capture & Clinical Data Management Multiple clinical sites participating in clinical trial Ongoing data entry by study coordinators via EDC system user interface Data processing with edit checks, queries, manual review and clinical programming Store clinical data in centralized clinical Db and extract data in EDC supported format(s) Transform data to CDISC-compliant data sets using SDTM programming pipeline approach and efficiencies vary by organization Generate applicable SDTM domains containing clinical data for use in analysis and submission Clinical Data Management Technology for Specialty Lab Data & Biomarker Data Management.RCC.CSV.FCS One or more specialty labs generating data Ongoing bulk upload of raw specialty lab data via web-based application for managing biomarker data Biomarker Data Management Data processing with assay-specific QC, bioinformatics workflows, and lab management Applications for biomarker data visualization and reporting can aid in on-study decision making Store biomarker data in centralized relational Db and extract data in structured multi-purpose format Transform data to CDISC-compliant data sets using biomarker SME and SDTM programming pipeline (standards, macros) Generate applicable SDTM domains containing biomarker data for use in analysis and submission With biomarkers being an integral part of modern clinical trials, it is necessary to bring new approaches to the management and processing of biomarker data. Combining advanced technology with biomarker expertise provides flexibility, efficiency, and compliance. Addressing the fundamental gap in clinical trial operations and translational research with this approach, in parallel to traditional clinical data management, will result in immediate gains and mitigate risks to interim and final study deliverables. 6

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