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 32 of 51 November/December 2018 Clinical OMICs 31 ranging from targeted genomic panels to high-content or high-throughput experiments. These assays have a broad range of applications—addressing patient selection, char- acterizing mechanism of action (MoA), guiding dosing, and optimizing study design. Technologies are evolving as well, and data are now being generated at unprecedented rates from flow cytometry, next-generation sequencing, immunophenotyping, mutational analysis, gene or protein expression, multiplexed immunohistochemistry, circulat- ing tumor cells, and more. Specialized labs and associated technologies have emerged to deliver on these assays and many have had to develop proprietary technology, meth- ods, or panels. The diversity and complexity of biomarker assays is further complicated by the fact that the resulting data are generated in different formats and data structures, creating challenges around data harmonization, interpreta- tion, and accessibility. As in most industries today, the generation of significant volumes of data in biopharmaceuticals has become more cost-effective and more critical for driving decisions that impact the success of a drug development program. How- ever, there has historically been no efficient way to lever- age all of the data generated. Instead, data is generated, but not managed and analyzed efficiently due to the limitations of teams and the lack of appropriate technology. Accord- ing to a survey by Forbes, 3 nearly 80% of scientist time is spent collecting, cleaning, and organizing datasets. With only a fraction of generated data being leveraged, there is a tremendous opportunity to create additional value and insights if these data can be made seamlessly accessible for visualization, large-scale analytics, and, ultimately, sharing. From Biomarker Data to a Biomarker- Guided Drug Development Data Asset In order to optimize value creation from generated data, drug developers and innovators need to be able to perform rapid data interrogation within and across platforms, tri- als, geographies, and even companies. Data interrogation capability, which can be either visual or analytic, facilitates hypothesis rule out, as well as generation of novel hypoth- eses to shape and guide a drug development program. Beyond data interrogation, the biomarker data management system must also be able to disseminate information seam- lessly within and across organizations. Of course, data interrogation and analysis first requires a harmonized data set that is quality controlled and correctly mapped together. This task becomes increasingly complex with each incremental assay, each additional lab, and each subsequent study. In fact, a typical Phase 1 immuno-oncol- ogy study with a standard assay package may generate >10 million data points. The level of effort required here would easily consume multiple advanced data scientists with backgrounds ranging from the computational to biologi- cal sciences. Automation is almost a requirement to do this effectively particularly given the near real-time turnaround needed to support on-study decisions. In development programs where biomarkers are used to support go/no-go decisions between trial phases, the lack of tight integration between clinical operations and biomarker data can lead to longer timelines, increased costs and even delayed approvals. The right technology must also be able to organize these data effectively and efficiently as part of end-of-study activities and regulatory submissions. With the recent explosion of partnerships and licensing deals and the emergence of novel combination therapies, the ability to share data within and across both platforms and organizations will be critical to realizing value. Organi- zations that are able to provide access to complex, organized data sets can help convey early signs of positive biological response in early-phase tri- als, which can further sup- port increased partnership, licensing, and fundraising activities. Increasingly, early biomarker data is being used to inform the viability of novel therapeu- tics. Whether it enhances the profile, or simply acts as the canary in the coal mine, these insights are transforming how invest- ments are contemplated. Technologies That Are Driving Value Today, technology platforms that are linking seamlessly across other technologies, such as electronic data capture (EDC) or laboratory information management systems (LIMS), have the potential to address key value drivers. To handle the complexity and throughput of biomarker assays and make data available to inform decision-making, these technology platforms must provide: • Automated pipelines for the ingestion of data from an unlimited number of labs for all types of bio- marker assays seksan Mongkhonkhamsao / Getty Images (continued on next page) Tobias Guennel, Ph.D

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