Modern biomarker-guided drug development has been driven, in part, by the need to target specific patient populations and create efficient, cost-effective methods for developing transformative therapeutics. A key component of the discovery process involves characterizing the cascade of biological pathways and processes that ultimately drive response and potentially define patient populations.
Sponsors frequently invest substantial time and capital into advancing novel technologies and approaches in this realm, particularly as the cost of using complex assays to interrogate biological pathways decreases. And yet, we have only uncovered the tip of the iceberg when it comes to utilizing the massive volumes of data these new technologies create. In particular, we are in the early stages of effectively aggregating and harmonizing this data to make it actionable. Additionally, the rise of platform licensing and partnership structures has led to the prioritization of enabling the efficient consumption of complex analytical results and establishing a foundation to foster collaborative analyses to fuel the drug development process.
In this article, we explore biomarker data trends and challenges, and share how drug developers, translational scientists, and clinical researchers evaluating biomarkers in early-phase studies can:
- Move beyond data overload and leverage cutting-edge technologies to seamlessly transform millions of data points into actionable insights
- Harness advanced informatics and visualization tools to harmonize disparate sources of biomarker data and warehouse them for effective on-study use (e.g., dose selection), as well as downstream use
- Build flexibility, efficiency, and compliance into the rapidly evolving biomarker-informed development process
- Accelerate go/no-go decision-making to reduce time and cost
Biomarkers in Clinical Trials
Since 2013, there has been a sharp increase in the number of clinical trials citing a biomarker-guided precision medicine design. Evaluating biomarkers in clinical trials and integrating specialty lab data (e.g., flow cytometry, gene expression profiling, immunosequencing) with pharmacokinetics, safety lab, and clinical data can provide a more comprehensive picture for assessing the efficacy, pharmacodynamic effect, and safety of an investigative compound. A recent report revealed that drugs developed using a precision medicine design had a higher likelihood of launch across all therapeutic areas, with the most significant difference in oncology (see Figure 1).
The message: The impact of a precision medicine design may be even more powerful when biomarkers are used for patient selection. A study of clinical development success rates over the 10-year period from 2006 to 2015 demonstrated that the use of biomarkers for patient selection was associated with a three-fold increase in the likelihood of success from Phase 1 to approval.
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