Clinical OMICS

NOV-DEC 2018

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 33 of 51

32 Clinical OMICs November/December 2018 • A centralized database for storage and access to all specialty lab data post-ingestion • Data reconciliation capabilities to expedite his- torically time-consuming reconciliation activities between LIMS and EDC • Assay-specific workflows and quality control parameters, as well as a capability to incorporate custom workflows • Plug-in modules for translational research/biomarker data management • Submission-ready datasets that comply with Clinical Data Interchange Standards Consortium (CDISC) standards and can be adapted to shifts in regulatory requirements • Compatibility with other software tools (e.g., Graph- Pad Prism) In addition, these technologies must have the capability to simultaneously integrate and deliver multiple work- flows with dynamic reporting for real-time visual ana- lytics, large-scale analytics and data sharing. This data integration capability supports key objectives, such as dose evaluation, multimarker biomarker signature devel- opment, biomarker-defined patient stratification, and real- time quality control analysis. It also provides valuable insight into pathways, networks, and compounds for drug positioning and future study design. The ability to visu- alize and analyze biomarker data through user-friendly, intuitive web-based tools enables sponsors, translational researchers, and clinical trial teams to harvest insights from all data sources to inform decision-making. Conclusion Delivering on the promise of precision medicine requires out-of-the-box thinking and informatics platforms designed to efficiently unlock the full potential of data collected. Just as EDC technology helped revolutionize clinical data management, technology-based solutions for biomarker data management will now become a require- ment for modern clinical trial operations. However, tech- nology alone is not enough. Ultimately, success requires a cross-functional team, including data scientists, translational informaticians, bio- marker data management programmers, data managers, and innovative data scientists, with the skill to design, val- idate, and operate technologies engineered specifically to address the challenges of biomarker data management in the new paradigm of biomarker-guided drug development. Combining cutting-edge technology with deep biomarker expertise facilitates flexibility, efficiency, and compliance, reducing cost and bridging the gap between translational researchers and clinical trial teams to optimize the devel- opment process. The authors are part of a team at a biomarker research organi- zation, Precision for Medicine, that developed a biomarker data management technology platform that ingests diverse data, har- monizes it and supports complex analysis in real time. 1 The Economist Intelligence Unit. The Innovation Imperative: The Future of Drug Development Part I: Research Methods and Findings. 2 David W Thomas, Justin Burns, John Audette, Adam Carroll, Corey Dow-Hygelund, Michael Hay. Clinical Development Success Rates 2006-2015. (2016) A BIO Industry Analysis. 3 Press G. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. March 23, 2016. Features of an Ideal Biomarker Data Management Platform (continued from previous page)

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