Managing Complex Biomarker Data Archives - Page 3 of 5

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.

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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.

Register here for the webinar

Title: Critical On-Study Biomarker KPIs for Modern Trials – Part 2: Integrating Data Silos to Unlock Scientific Insights and Empower Translational Teams

Duration: 45 minutes

You will learn how to:

Expand your use of exploratory biomarker data in combination with clinical metadata for on-study analytics
Centralize integrated biomarker and clinical data as a single source of truth to expedite drug development decisions and enable translational exploration
Leverage dashboards to support collaboration and quickly surface actionable insights

February 3, 2021 — In this brief, we explore Translational Intelligence – that is, navigating the vast biomarker data ecosystem to quickly uncover insights that would be missed by visual inspection alone, and which enable translational teams to focus on the right pathways, biological mechanisms, and / or patient populations.

February 1, 2021 — Translational and clinical researchers everywhere are harnessing public multiomics data sets to accelerate R&D. Renée Deehan, Ph.D., VP of Computational Biology at QuartzBio, described the enormous potential of public, real-world data at a panel discussion hosted by OBN.

January 27, 2021 — Non-profit and governmental organizations have invested immense financial and labor resources, with generous contributions from participants, to create large-scale data sets. The Cancer Genome Atlas (TCGA) is one such publicly accessible resource, and represents a uniquely comprehensive collection of multiomic and clinical data, from which drug development researchers can derive knowledge about disease, treatment, and the biology of individual tumors.

December 10, 2020 — Biomarker-guided trials frequently have an international footprint of sites to reach targeted patient populations and involve a complex network of specialty labs. Join us for our upcoming webinar, where QuartzBio’s Tobi Guennel will discuss how his team is breaking down data siloes to create actionable insights into availability and quality of samples and data. These insights allow clinical and translational teams to streamline trial operations and enable early course correction.

November 12, 2020 — In this article, QuartzBio Managing Director Scott Marshall discusses critical steps to integrating clinical, PK, and exploratory data on-study to advance operational and translational insights (without all the manual effort that has become all too common). With our experience working across dozens of clients and hundreds of studies, we’ve identified four key components necessary to efficiently centralize.