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Brilliance@Work: R&D Informatics Expert Laura Aguiar

Photo: James Lee, Chester, NH, USA

Photo: James Lee, Chester, NH, USA

Welcome to Brilliance@Work, a series of profiles about stellar collaboration professionals and their best practices at work.

Laura Aguiar, Ph.D., MBA, MPM, PMP, is the Data Science Decision Support Service Manager for Roche Pharmaceutical Research and Early Development Informatics at the Roche Innovation Center in New York.

Laura Aguiar

Laura Aguiar

Throughout May, Brilliance@Work will feature Marketing Analytics & Data Science experts. Read on to learn how Laura and her data science team support R&D informatics innovation at Roche.

Peggy L. Bieniek, ABC: How does Roche use data to help shape the future of healthcare?
Laura Aguiar: Various types of data are used. For example, market research data is used for constructing/optimizing business strategy, by understanding the physicians’ needs and their expectations for specific disease indication. Genetics and genomics data is used for identifying and assessing biomarkers (predictive drug response biomarkers, drug safety biomarkers, etc.). Real world data (insurance claim data, Electronic Healthcare Record [EHR] data) is starting to be used for optimizing clinical trial study protocols (by understanding disease populations), patient recruitment, etc.

PB: How does R&D informatics support collaboration?
LA: R&D informatics can support collaboration on many levels. It can provide user-friendly collaboration platforms that makes it easier to store and share relevant information in unstructured (file share) and structured databases (for scientific data from collaboration). The R&D informaticians also provide a bridge between collaborators (usually scientists) and Roche, particularly for data and information sharing.

PB: How are big data and data science impacting R&D currently?
LA: We are making use of big data, like Next Generation Sequencing (NGS) data, to identify additional disease indications that certain drugs have the potential to cure (based on their mechanism of action and disease etiology fit) and to identify predictive drug response biomarkers. Using mixed data science capabilities, we combine different types of big data, like NGS, literature, and real world data to support pre-clinical and clinical project teams, scientists, and clinical operation experts.

PB: What will people gain from attending your conference presentation?
LA: People will get insight on the formation and value of a Data Science team in a Research Pharma organization. They will learn how it’s managed, the interface with stakeholders, value through drug project impacts, and employment of a cross-functional team in a global setting.

Want to hear more from Laura Aguiar? Join us at the Marketing Analytics & Data Science Conference on June 8-10, 2016 in San Francisco, California, as she presents “Data Science in Early R&D Informatics.” #MADSCONF