Improving Pharmaceutical R&D Productivity through Accelerated Biomarker Discovery

Biomarker discovery plays a critical role in the development of precision medicine, helping researchers identify key biological markers that can predict disease progression, response to treatment, and overall patient outcomes. However, the path to effective biomarker discovery is filled with complexities. From fragmented data to regulatory hurdles, biopharma organizations often struggle to navigate this critical aspect of drug development.

In this post, we'll explore prominent challenges encountered during biomarker discovery in pharma, and present best practices for overcoming them, ensuring that research teams can unlock the full potential of their data to drive innovation.

The Major Challenges in Biomarker Discovery

Before diving into solutions, it’s important to recognize the key obstacles biopharma organizations face:

In biopharma, valuable research data—including genomic data, electronic health records (EHRs), and real-world data (RWD)—often remains scattered across different databases and departments. This lack of integration hampers efforts to cross-reference datasets, delaying the identification of potential biomarkers.

As multimodal data (such as genomics, proteomics, and transcriptomics) becomes central to precision medicine, the analysis process requires highly specialized skills. This skills gap leads to inefficiencies in analyzing these complex datasets, slowing down patient stratification and biomarker identification.

Managing sensitive clinical and research data while adhering to regulations like 21 CFR Part 11, HIPAA, and GxP compliance is challenging for biopharma companies. Without proper tools and processes, ensuring data security and regulatory adherence can delay research and clinical trial submissions.

Many organizations attempt to build their own data management systems, which can drain resources and require significant maintenance. These systems often lack scalability, and long-term management diverts attention away from core activities like drug development.

Even when data is available, the lack of user-friendly interfaces means that non-technical users must rely on IT or data science teams for access. This bottleneck limits research speed and reduces the agility needed for rapid iteration during biomarker discovery.

Who We Are:

For biopharma organizations looking to harness the full potential of their data, DNAnexus offers an unparalleled solution for biomarker discovery and drug development. Whether you are working with multimodal datasets or seeking to accelerate precision medicine initiatives, DNAnexus is the leading multiomics data management and analysis platform, providing a scalable, secure, and compliant solution that streamlines complex analyses and fosters collaboration.

Real-World Example: Biomarker Discovery in Inflammatory Bowel Disease (IBD)

A compelling example of how DNAnexus supports biomarker discovery can be found in its collaboration with Panomics for Inflammatory Bowel Disease (IBD) research and is covered comprehensively in our eBook. Briefly, using data from the Ovation Biobank, which includes whole genome sequencing, RNA sequencing, and curated EHR records, DNAnexus enabled scientists to:

Validating Key Biomarkers: First, to confirm Researchers used the platform to perform differential gene expression analysis on a dataset containing 211 patients, independently identifying Oncostatin M (OSM) and IL6 were significantly overexpressed in patients with severe IBD. This finding provided critical corroboration of the platform’s ability to drive hypothesis generation, even with low sample numbers. 

Identify Common Disease Signatures Across Datasets: The platform facilitated the comparison of the Ovation Biobank data with established IBD datasets, such as those from the Mount Sinai Crohn's and Colitis Registry. This validation process ensured that the identified biomarkers were robust and applicable across multiple patient cohorts.

Discover New Treatment Options: By exploring gene expression patterns linked to treatment resistance, DNAnexus helped identify alternative therapeutic targets, such as Imatinib and Amiloride, which could be repurposed to modulate the pathways associated with IBD biomarkers. This accelerated the transition from biomarker discovery to actionable therapeutic development.

Best Practices for Effective Biomarker Discovery in Biopharma

Despite the challenges, biopharma companies can adopt a number of best practices to streamline biomarker discovery and maximize the value of their data. Here are some strategies that can help:

A critical step in improving biomarker discovery is consolidating fragmented data sources. Organizations should invest in platforms or technologies that integrate historical research data, clinical trial results, and real-world datasets into a unified system. Doing so allows researchers to draw on lessons from past studies and explore patterns across different data modalities. The ability to cross-reference diverse data sources enables quicker identification of biomarkers, helping teams make more informed decisions.

While omics data analysis often requires specialized skills, new tools and platforms are emerging that allow non-technical users to perform advanced analyses with ease. Look for solutions that provide intuitive interfaces and automated workflows, enabling researchers, clinicians, and biologists to independently run complex analyses. Empowering non-experts with data access accelerates the discovery process, making it possible to identify and validate biomarkers faster, without the constant reliance on data science teams.

Managing regulatory requirements can be a daunting task, but it’s a necessary part of ensuring that biomarker research is both secure and compliant. Biopharma organizations should prioritize tools that not only meet but exceed regulatory standards such as GxP, HIPAA, and GDPR compliance. Additionally, these tools should be scalable, allowing organizations to adapt to growing datasets and evolving regulatory demands without compromising security or usability.

Effective biomarker discovery often requires seamless collaboration across wet labs, data science teams, and clinical researchers. Organizations should leverage trusted research environments that allow for real-time collaboration and secure data sharing between stakeholders. By integrating these collaborative capabilities, biopharma companies can avoid delays in communication and ensure that findings from research are acted upon quickly.

The integration of RWD alongside clinical and genomic data is becoming increasingly important in biomarker discovery. RWD provides insights into how treatments perform in diverse patient populations and real-world settings, making it a valuable complement to clinical trial data. By integrating these datasets into your biomarker discovery efforts, you can generate more robust and comprehensive insights, leading to better patient stratification and personalized treatment plans.

For biomarker discovery efforts to be successful, it's crucial to foster a data-driven culture within your organization. Encourage teams to use data as the foundation for research and decision-making, ensuring that all stakeholders—whether they are data scientists, researchers, or clinicians—are aligned on the importance of leveraging data for insights. When teams have easy access to data and can make decisions based on actionable insights, the discovery process becomes far more efficient and effective.

Conclusion

As the biopharma industry continues to evolve, the integration of bioinformatics into drug discovery workflows becomes increasingly essential. DNAnexus stands at the forefront of this evolution, providing biopharma organizations with the tools they need to transform complex data into actionable insights. Whether identifying novel biomarkers or validating new treatment options, DNAnexus empowers researchers to make faster, more informed decisions, ultimately driving innovation in precision medicine.