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How to transform clinical biomarkers and multiomics data into strategic data products for precision medicine
Clinical biomarkers are crucial for precision medicine. They connect disease biology, patient response, and treatments by linking genes, proteins, and cell behavior to patient outcomes. These measurements show how therapies affect diseases and help develop new medicines.
Proteins are often the stars of the show in biomarker discovery due to their direct role in cell function. However, research now goes beyond just proteins. The rise of high-throughput multiomics – genomics, proteomics, metabolomics, and transcriptomics – has opened new doors in personalized medicine. By integrating this multiomic data and linking it to patient outcomes, researchers can find clinically relevant biomarkers to guide treatment decisions.
Figure 1: The benefits of using biomarker data as a strategic asset
The challenge: Data overload
Multiomics data is complex and layered. Traditional data management just can't keep up. The industry standard often involves manually intensive, ad hoc analysis of poorly described data, which is slow, prone to errors, and difficult to scale. This leads to delayed data access and slower research decisions. To speed up R&D, we need new ways to handle exploratory clinical biomarker data.
Here, we outline the impact of treating biomarker data as an asset, discuss the challenges, and show how to create a cycle of continuous improvement.
The benefits of making biomarker data a strategic asset
Multiomics biomarkers provide a holistic view of biological processes by integrating data from different molecular levels. This comprehensive approach helps researchers and clinicians move past fragmented analysis, sparking innovation in drug development and diagnostics. Organizations that tap into the value of this data could see numerous benefits, including:
More effective clinical trials
Faster drug discovery and development
Enhanced personalized medicine
Better diagnostic and prognostic tools
Stronger regulatory submissions
New revenue streams
The hurdles: What's standing in the way?
While creating biomarker data products offers huge potential, it's not a simple task. Multiomics approaches generate massive, complex datasets that require sophisticated tools to integrate and analyze. Turning this data into a strategic asset presents multiple challenges:
Data quality and integrity: Ensuring data is accurate, complete, and consistent is critical
Data governance and standards: Clear governance and industry standards are essential for making data from different sources work together
Data integration and interoperability: Combining data from diverse sources is complex
Data analysis and interpretation: Advanced analytics and domain expertise are needed to find meaningful insights
Data privacy and security: Protecting sensitive patient information requires robust security
Figure 2: High-level mapping of exploratory clinical biomarker data products
The solution: A virtuous cycle for biomarker data
A smart, strategic approach is needed to get the most value from biomarker data. When you tackle these challenges correctly, they become opportunities for innovation. We view the journey from raw data to a strategic asset as a virtuous cycle, where each step builds on the last, driving continuous discovery.
This cycle has five key steps:
Collect: Gather data from various sources like clinical trials, biomarker labs, and real-world evidence
Integrate and standardize: Once collected, the data must be harmonized. This means aligning formats, terminologies, and units for consistency and implementing quality control. Using a unified platform, such as a Databricks Lakehouse, can speed this up and provide better governance. Making data machine-readable also enables the application of generative AI for tasks such as data harmonization and analysis
Analyze and interpret: With standardized data, you can use statistical, bioinformatic, and machine-learning methods to find associations between biomarkers, disease mechanisms, and patient outcomes. AI and machine learning can fast-track the identification and validation of biomarkers
Create data products: Transform these insights into actionable data products. We've developed a methodology to accelerate this process. Curated data can be used to create visualizations that communicate complex insights, and APIs can make biomarker data more interoperable for different applications
Utilize and create value: Data products enable real-world value. They can inform clinical trial design, help develop companion diagnostics, and support precision medicine efforts. This data also strengthens regulatory filings
By maximizing data use, you unlock its full potential, which drives further discovery and fuels innovation in precision medicine. This, in turn, generates new data, reinforcing the virtuous cycle.
Figure 3: The virtuous cycle for biomarker data
The path forward
The journey from gene to protein to biomarker is complex. In modern precision medicine, data is foundational. As technology evolves, clinical biomarker data products are set to drive big changes in diagnostics, therapeutics, and patient care.
By adopting a virtuous cycle for managing and engineering biomarker data products, life sciences R&D organizations can unlock more value from this unique data. Investing in modern data architectures and processes allows research organizations to build a solid foundation and lead the way in delivering transformative therapies to patients.
