Managing Complex Biomarker 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 Read More →

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 Read More →

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 Read More →

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 Read More →