Supporting Biomarker-Driven Clinical Trials Through a Future-Ready Lab

    

With the rise of precision medicine, significant advancements in laboratory infrastructure and workflow are needed to support the complex requirements of biomarker-driven clinical trials. Now that biomarkers have transitioned from nice-to-have exploratory endpoints to essential tools for patient enrollment, clinical decision-making, and treatment monitoring, laboratories must evolve to meet the demands of this new era.

Creating a new paradigm for clinical labs

Biomarkers have become ubiquitous, particularly in oncology clinical programs where they are often a critical component of study designs. This shift has been amplified by the current restrictive funding environment, where there is increased pressure for phase 1 studies to give an early efficacy readout or at least provide insight into pharmacokinetic (PK)/pharmacodynamic (PD) relationships.

While labs are accustomed to fast turnaround times (TAT) for PK assays used to inform dose escalations, sponsors are now asking to review data from the first patients dosed to see if a PD effect is detectable. This demand has led to a new paradigm where PD assays must be performed in tandem with PK assessments, placing new stress on labs.

Beyond the strain associated with performing PD assays in real-time rather than in batches, PD assays are often drug-specific, requiring specialized protocols and training. If this boutique assay enriches or stratifies populations, it’s likely that speed will be combined with compliance to CLIA or ISO 15189, particularly post-phase 1. 

Meeting the needs of biomarker-driven trials

As clinical trials increase in complexity, here is how Precision for Medicine has prepared our laboratories to deliver a wide range of assays with fast TAT under high regulatory standards. All future-ready labs can rise to this challenge by: 

• Adopting platform-agnostic technologies that offer the flexibility and scalability to adapt to the diverse needs of biomarker-driven studies with multiple time-sensitivities. With a platform-agnostic approach, technology selection is driven by the scientific question to be answered and which assay types can be integrated. It may also save setup time and costs if a sponsor already has a research-grade assay on a specific platform as there is no need to prove equivalence on a different platform. Adding multiplexing capability allows for monitoring of multiple biomarkers, while high-throughput next-generation sequencing is essential for genomic biomarker analysis. 
• Streamlining sample management to reduce queries, thereby enhancing TAT and overall efficiency. The process begins at the clinical site, where accurate and complete metadata must be collected alongside the sample to enable rapid accessioning into the laboratory information management system. The use of custom collection kits and advanced biorepository systems that provide real-time visibility into sample status and availability can be invaluable. Close coordination with sponsors and sites allows labs to anticipate sample inflows and to schedule assays accordingly, reducing the time between sample collection and data reporting. 
• Standardizing workflows to maintain consistency and quality. A key aspect of workflow standardization is cross-training of laboratory personnel. In an environment where a wide range of assays are performed within tight timelines and under varying regulatory requirements, cross-training ensures high throughput and flexibility without compromising data quality. Another important consideration is the use of targeted automation for activities such as sample preparation or data analysis. 

Looking ahead to the next generation of labs

At Precision for Medicine, we are seeing drug development become ever more complex and personalized, requiring multiple tests to be performed at the same time to optimize patient treatment. As the volume of data generated increases exponentially, more labs will need to incorporate the sophisticated tools needed for data integration and analysis, including artificial intelligence (AI), in biomarker-driven clinical trials AI will play a pivotal role in identifying correlations among this data and generating insights that guide treatment and accelerate future development. To keep pace, other future-ready labs should also be prepared to validate and incorporate AI and other emerging technologies to deliver the detailed, nuanced data necessary for supporting the continued advancement of precision medicine.