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Unprecedented advances in diagnostic and other scientific technologies are creating an ability to generate high-content and high-throughput molecular and cell-based assays at unprecedented rates—giving translational researchers massive amounts of complex data earlier in the clinical development process. That’s the good news. However, that news is tempered by the challenge of obtaining valuable insights from such complex data through the assimilation of biomarker data (specialty and esoteric lab) and clinical data (efficacy or pharmacokinetics)—a process that historically has been tedious and difficult. Furthermore, in order to unlock the full potential of these data once collected, highly specialized data management resources must be assembled to support informatics and data science.

Delivering on the promise of precision medicine requires new approaches and informatics platforms to address these new challenges. Proper assimilation of collective data by specialized resources will allow for the identification and stratification of  high-responding patient cohorts, improve safety profiles, effectively guide dose selection, and reduce the time and cost of clinical trials. Studies introducing complex and content-rich biomarkers to the realm of decision-making processes now require new technology-enabled services, namely biomarker data management (BDM).

Just as the need to more rapidly collect and analyze global clinical trial data sparked the creation of clinical trial platforms, such as electronic data capture (EDC), interactive response technology (IXRS), and clinical trial management systems (CTMS), the new era of precision medicine and biomarker-guided research is driving the need for new analytical platforms. The diversity and complexity of biomarker assays, including flow cytometry, next-generation sequencing (NGS), multiplexed immunohistochemistry (IHC), and immunosequencing—further complicated by the fact that the data are generated in different formats across varying geographical locations—present the problem of rapidly and effectively harmonizing, interpreting, and making these data accessible to inform biomarker-guided decisions for the current or future studies. Not dissimilar to the early days of electronic clinical data management. The next stage of electronic data capture must focus on biomarker data if we are to advance clinical trial designs to more effectively deliver, while lowering costs.

The increasing complexity and throughput of biomarker assays, coupled with the desire to have these data inform immediate decision-making, require assay-specific workflows, plug-in modules for translational research/biomarker data management, and submission-ready data sets that comply with CDISC standards. These workflows and the ability to generate integrated data sets compliant with evolving regulatory standards are critical components of new informatics platforms. For example, workflows and modules to support immunosequencing, gene expression profiling, and flow cytometry are necessary for effective integration of these unusually complex and diverse data sources. Although workflows exist for each separately, informatics platforms that can integrate and deliver these workflows simultaneously with dynamic reporting are essential for generating biomarker patient profiles, assay-specific profiles, and real-time QC analysis. Such comprehensive biomarker profiles have been lagging, often limiting the value and accessibility of these data.

Biomarker data management addresses these challenges. The concept of BDM is to support and facilitate the harmonization of biomarker and specified clinical data generated in today’s drug development process. Combining subject matter expertise with advanced informatics/technology platforms streamlines this effort, ultimately reducing cost and time. Once these complex data are synthesized, translational informatics teams can focus on the scientific questions that will drive the development process forward. Seamlessly integrating biomarker data with clinical results allows real-time analysis and decision-making to guide biomarker-driven patient selection via adaptive randomization, dose selection, and future trial designs.

Translational science is no longer an optional element in modern clinical studies; it must be regarded as an integral component. The onslaught of diverse and complex data sets requires a BDM strategy to be in place during the planning stages to more effectively integrate all data. Translational teams with diverse expertise can harness the prospective findings within a multifaceted analysis of complex biological variation to help guide the decision-making process in clinical studies. Leveraging a secure, scalable, cloud-based technology platform to harmonize data from different sources to be visualized and analyzed more easily can greatly reduce the time needed to make these decisions. Implementing a BDM platform is the best way to obtain transformative insights so that precision medicine can be fully realized.

 

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