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.

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.

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

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