Managing Complex Biomarker Data Archives - Page 3 of 5

Title: Transcending Biomarker Data Chaos: How Integrating Disparate PK, Clinical & Exploratory Data Enables Deep Translational Insights

Duration: 30 minutes

You will see how teams unlock translational intelligence:

Navigate vast exploratory data – identify trends and data points of interest
Surface insights – analyze biomarker trends in collaborative dashboards across subject IDs, time points, response status, and treatment groups
Access data rapidly – point and click navigation to underlying data files to interrogate the raw data behind the summary values
Interrogate cross assay – map to a common data dictionary to enable rapid interrogation of trends across assay modalities: within and across studies

July 29, 2021 — Translational research teams producing large datasets from biomarker-rich trials quickly face the next challenge: generating actionable insights from their data.

As we described in a previous case study, we have been working with many teams to enable deep mechanistic insight generation from their biomarker data to aid in signature development for selecting patients, predicting therapeutic response, and informing key development decisions.

May 19, 2021 — Biomarker data generated to support clinical development has multiple roles:

Inclusion in end-of-study regulatory submissions. As such, data must conform to FDA data exchange standards and need SDTM programming for analysis and submission.
Supporting on-study decisions. Data must be stored in an accessible, flexibly structured format to adapt to on-study needs. These diverse needs include data visualization, mock TLFs, materials for investor meetings, posters, and reporting for safety review, data monitoring, and dosing decisions.
Enable deep exploration to generate translational insights. Biomarker data generated during clinical trials presents opportunities for sponsors to further characterize mechanism of action and prioritize additional target pathways for their pipelines.

May 5, 2021 — As the data generated during a modern clinical trial has dramatically expanded, sponsors must continuously evaluate novel approaches to help bridge the gap between millions of data points and breakthrough scientific insights (a goal we describe as delivering Translational Intelligence). One such example is assessing signatures of “hot” and “cold” tumors.

Watch the webinar on demand:

Title: Translational Intelligence: Exploring IO Signatures in Clinical Trial Genomic Data

Duration: 30 minutes

You will learn how to:

Visualize gene alterations and tumor mutational burden (TMB) using heatmaps and tables, with subject and time point granularity.
Identify genes of interest and candidate signatures using visualization and statistical methods to relate clinical response data and gene expression data to regions of genomic alteration.
Correlate genomic data with known signatures of immuno-oncology (IO) response through integrations with public data, knowledgebases and sponsor data.
Define patient subgroups to generate deeper biological/ mechanistic insights.

Synthesis and Integration of Genomics Data in Clinical Trials

April 21, 2021 — A modern clinical trial, with well-characterized subjects studied over periods of time, presents unmissable opportunities for sponsors to characterize mechanism of action, prioritize target pathways for their pipelines, and generate as much data as possible to support regulatory filings.

CDISC-Compliant Submission of Biomarker and Specialty Lab Data

April 9, 2021 — With the rise of biomarkers used in clinical trials (e.g., prognostic, predictive, pharmacodynamic) and biomarker assay modalities (e.g. flow cytometry, multiplex protein detection, gene expression profiling), biomarker and specialty lab data are increasingly incorporated into FDA submissions. This data provides insights into key clinical objectives, including pharmacological effects, and drug safety and effectiveness.

Drug developers face operational challenges, however, in preparing complex, often unstructured, biomarker and specialty lab data in compliance with regulatory requirements.

Any study that began as of December 17, 2016 must use Clinical Data Interchange Standards Consortium (CDISC) standards, formats, and terminologies specified in the FDA Data Standards Catalog for New Drug Application (NDA), Abbreviated NDA (ANDA), and certain Biologic License Application (BLA) submissions.

April 2, 2021 — Gene expression biomarkers can help predict clinical outcomes in response to treatment with pathway-targeting therapeutics, thereby helping to identify patient subgroups likely to respond.

In a collaboration with investigators at Isofol Medical AB, the QuartzBio team analyzed the correlation between expression of folate pathway genes and clinical outcomes in response to a combination therapy targeting this pathway in metastatic colorectal cancer, as part of a Phase III clinical trial.

They identified potential prognostic markers with clinical benefit, and presented the results at the 2021 American Society of Clinical Oncology Gastrointestinal Cancers Symposium (ASCO-GI).

View the poster

March 5, 2021 — Applying algorithms to interconnected biomarker and clinical data can yield scientific insights, not only into therapeutic mechanisms, but also into predicting long-term response to treatment.

In a collaboration with investigators at Janssen, the QuartzBio team sought to identify genetic markers correlated with protection against chronic hepatitis B early relapse in patients after withdrawal from treatment. The team identified several genes that were, for the first time, associated with protection against early onset of relapse.

View the poster

February 12, 2021 — Integrating clinical and biomarker data enables both operational insights as well as scientific insights that can help teams make clinical trial decisions on-study.

Our last article defined the synthesis of these insights as translational intelligence, with the potential to illuminate key insights in drug development just as business intelligence is used to optimize business performance. We showed that, for example, having all the biomarker and clinical data linked together enables sponsors to quickly explore the relationship between drug response status and specific biomarkers of interest.

In this post, we further illustrate how translational intelligence works in practice with example applications. Translational intelligence not only illuminates key trends that may be missed through manual data review, but it also facilitates the seamless exploration of interlinked clinical, PK, and exploratory biomarker data assets.