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Biomarker Data Management in Clinical Trials: Addressing the Challenges of the New Regulatory Landscape
Biomarkers and specialty labs are core to modern clinical trials.The broad and ever-
growing 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.
Introduction
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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.
■ Safety lab data—hematology, chemistry
■ PK lab data
■ Specialty biomarker assays
- High-content flow cytometry
- Multiplex cytokine panel
- Gene expression measured by Nanostring
PK lab work andmultiplex cytokine panel
Immunophenotypingby flow cytometry
Clinical sitesand local labs
Gene expressionby Nanostring
Phase 1/2 Study Objectives
On-studyPull 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
4 specialty lab assayscoming from 3 specialty labs, and other protocol requiredlab tests coming from local labs at clinical sites
Generate and deliver CDISC-compliant data sets for specialty lab data to be used in analysis andsubmission, with both rigor and fast turnaround
LAB 3LAB 1 LAB 2
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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
Specialty biomarker labs- Multiplex cytokine panel- High-content flow cytometry- Nanostring gene expression
Specialty PK group- Drug concentrations over time- Modeling generates other PK
parameters (e.g., Cmax
, tmax
, AUC)
Local labs at clinical sites- Hematology- Chemistry
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
External to EDC system- Typically manual processing by PK group- Basic reconciliation with EDC, but remainder
is handled separate from CDM workflow
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)
Pharmaco-kinetic Concentrations (PC)
Pharmaco-kinetic Parameters (PP)
Related Records (RELREC)
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)
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
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
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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.
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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.
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.
1 2 3 4 5 6
Multiple clinicalsites participating
in clinical trial
Ongoing data entryby study coordinators via
EDC system user interface
Data processing withedit checks, queries,manual review and
clinical programming
Store clinical data incentralized clinical Db
and extract data inEDC supported format(s)
Transform data toCDISC-compliant data sets usingSDTM programming pipeline –
approach and efficienciesvary by organization
Generate applicableSDTM domains containingclinical data for use inanalysis and submission
Ongoing bulk upload ofraw specialty lab data
via web-based applicationfor managing biomarker data
Data processing withassay-specific QC,
bioinformatics workflows,and lab management
Store biomarker data incentralized relational Db
and extract data in structuredmulti-purpose format
Transform data toCDISC-compliant data sets using biomarker SME and
SDTM programming pipeline(standards, macros)
Generate applicableSDTM domains containingbiomarker data for use in
analysis and submission
One or morespecialty labs
generating data
Technologyfor SpecialtyLab Data &
Biomarker DataManagement
ElectronicData Capture
& Clinical DataManagement
1 2 3 4 5 6
Applications for biomarker data visualization and reporting can aid
in on-study decision making
.RCC .CSV .FCS
Clinical Data Management
Biomarker Data Management