Big Data in Translational Science

Transcription

1 Big Data in Translational Science Albert Wang Associate Director, Translational R&D IT Bristol-Myers Squibb 2015 AAPS Annual Meeting

2 Agenda Perspectives on Big Data Big Data in Translational R&D Selected Initiatives at Bristol-Myers Squibb

3 Why are we talking about Big Data?

4 Why are we talking about Big Data? #1: Because our capability for generating data is growing - everywhere. Author 00 Month Year Set area descriptor Sub level 1 4

5 The Five V s of Big Data e.g., sentiment, social media, weather conditions, etc. IBM Institute for Business Value Analytics: The real-world use of big data Value Deriving value from data Until we can turn data into value, it is useless. 5

6 Why are we talking about Big Data? #2: Because new technologies have proven applicable to big data problems across a variety of domains. Virtualized/Distributed Storage NoSQL Databases MapReduce Artificial Intelligence (machine learning, NLP, etc.) 6

7 Why are we talking about Big Data? #3: Because there are still significant problems to solve. Data Agility (Real-Time) Data privacy/security Usability/Accessibility 7

8 What does this mean for Translational R&D? Discovery Development Translational Medicine Bench Translational Research Bedside

9 Translational R&D leverages a variety of data types Pharmacology data Clinical Trial Data Epidemio logical data Genomic Data Tissue Data TR&D Analytics Patient Genotype Medical/H ospital Data Molecular Data Real World Data

10 Complex Interdependency of Data

11 Increased Need for Data Sharing Across Partners Institute Partner CRO Hospital Academia Photo: 123rf.com Foundation Efficient information sharing across multiple partners Easy access to the data Transparency with collaborators Photo: istockphoto.com/123render 11

12 6 Areas of Pharma and Healthcare Life Science Data Owner: Academic, Government Example datasets: NGS data Imaging data Literature, & conferences text Signalling Pathway Data / Models Epidemiology Related Big Data Business Intelligence Data Owner: Pharma / Biotech / Academia Example datasets: News, Blogs, Social Media, Patents, Literature, Web pages, Financial Reports

13 How do we grow an infrastructure to support TR&D Big Data? Benefits Doing What We Can Now - Building for the Future Data Entry and storage Data Access Retrieval Improved Query and Navigation Enhanced Analysis and Visualization Data Integration Decision Support Data mining, Analysis, Modeling, and Interpretation Time

14 Future State Infrastructure Data Sources Integration Data feeds Outputs Users Analysis Insights Decisions Discovery Scientist Informatician/ Data Scientist Biomarker Scientist Project team Clinician External Collaborator Decision Support: query, analysis, knowledge capture Knowledge analysis, mining, modeling, interpretation Information Data curation, standardization, integration Data Discovery data. Sample properties, availability, location, Biomarker data Real World data, EMR, Claims, Structured and unstructured data Clinical: Subject demographics, treatment, response.

15 Case Study: Sensor data in atrial fibrillation INFORMED STUDY DESIGN ENABLED STUDY EXECUTION CHALLENGED DATA MANAGEMENT BMS used real-world pacemaker data analysis to revise the required # of subjects and observation period for this study Medtronic SEEQ patch... Wear each patch up to one week Patches replaced by subjects at home Mobile base station transmits ECG data to cloud in real time Alerts if safety event detected or device not reapplied correctly Patients carry wireless transmitters Reports generated ECG data streamed to the cloud Subjects needed after querying sensor dataset: 80 Subjects needed before querying sensor dataset: 160 Observation period reduced from 4 weeks to 2 weeks of continuous monitoring while on therapy. vs. Holter monitor Wear up to two days Replaced by ECG technician at clinical site Base station stores data; needs to be docked No real time alerts Clinical database could not accept reports: conversion process required Clinical DB ANALYSIS = W.I.P. 15

16 Biosensor Data Consumption Data Processing Scheme Common Sandbox Distributed Sandbox 16

17 Biomarker-driven Translational Research DWG Lead TR Biology Discovery Teams Translational Research Scientist Clinical Biomarker DM Physician Clinical Biomarker GCR Clinical Teams DM Physician Biomarker Tech Matrix GBS Bix Biomarker Tech Matrix Patient Information Purchased Tissue Collections Clinical Trials Patient Information Sample Information Specimen Repositories Viral Sequencing Metabolomics Proteomics IHC Flow Cytometry Gene Expression Genetics Cytokine Profiling Other assays 17

18 Biomarker Repository: Conceptual Architecture Samples IHC Flow Genomics Protein Clinical Ad hoc biomarker data Genomic Data Biomarker File Repository Clinical Data (CDMS, SAS environment, etc.) Specimen Biorepository TR&D Data Hub (Hadoop) Biomarker Repository Visualization Analytics 18

19 Virtual Systems Pharmacology 19

20 Acknowledgements Translational R&D IT Anastasia Christianson Kaushal Desai Peter McDonald Marko Miladinov Chris Nalbone Xiao Shao David Witt Other Groups Alex Simmonds Fraz Ismat Jeff Guss Tarek Leil Sergey Ermakov Russ Towell Brian Wong