How to Create a Big Data Culture in Pharma

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A talk presented at the Big Data and Analytics conference in Boston on January 28, 2014. Emphasis on data and information sharing cultures in companies.

A talk presented at the Big Data and Analytics conference in Boston on January 28, 2014. Emphasis on data and information sharing cultures in companies.

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  • The worldwide healthcare ecosystem is a complex network of interactions between providers, producers, payers, and regulators of/for products and services aimed at improving/maintaining the health and wellness of patients/consumers. A massive amounts of information/data circulates through this ecosystem. Health information technology is the umbrella term used to characterize the creation, collection, storage, retrieval, exchange, and analysis of the information in the healthcare ecosystem.

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  • 1. Business Transformation – Becoming a Truly Data- Driven Pharmaceutical Company Chris L. Waller, Ph.D.
  • 2. Overview Pharmaceutical organizations are defining the road map for data integration but how prepared are they to base their decisions and practices on this data? Failure to truly encompass the attributes of a data driven unit will hurt your ability to compete in the market. This presentation will help business line executives and data professionals to understand the steps needed to create a data driven organization, by making the right decisions, while providing some real life examples on companies who have done this successfully. • Defining an information architecture framework for global research and development processes • Enlisting champions and creating an entrepreneurial spirit to empower people to own new processes • Key role players you cannot do without – creating a cohesive strategy and building a winning team
  • 3. Overview Pharmaceutical organizations are defining the road map for data integration but how prepared are they to base their decisions and practices on this data? Failure to truly encompass the attributes of a data driven unit will hurt your ability to compete in the market. This presentation will help business line executives and data professionals to understand the steps needed to create a data driven organization, by making the right decisions, while providing some real life examples on companies who have done this successfully. • Defining an information architecture framework for global research and development processes • Enlisting champions and creating an entrepreneurial spirit to empower people to own new processes • Key role players you cannot do without – creating a cohesive strategy and building a winning team
  • 4. The Worldwide Healthcare Ecosystem Providers Regulators Policy Makers Diagnostic Services/ GCRCs Accreditation Entities Employers Consumers/ Patients $ Care Govt. Programs Regulations Applications and Approvals Licenses Licensed Health Prof’nals Payers Coverage Premium $ Health Plans Health Delivery Systems/ Facilities Nursing and Home Health $ “orders” $ Pharma. (including Biotech) Medical Products Generic Mfgs. $ Products Pharmacies Distributors Producers Info Companies Carve Outs (PBMs, others) Contract Services Global Insurers
  • 5. Healthcare Trends and Technologies Producers Developmen t Research Providers Commercial Medical EMR/PHR HIE Payers NHIN Products Services Regulators Precision Medicine Expansion into Emerging Markets Clinical Trial Design and Execution Next Generation Sequencing -omics Big Data Analytcis Patient Care and Outcomes Comparative Effectiveness Pharmacovigilance Mobility Digital Marketing Social Sentiment Analysis Patient Stratification ePlacebo Virtual Trials Remote Monitoring Trends Social Media Mining Big Data Analytics (Text) Clinical Decision Support Aids Care Augmentation Provision Tele-health (mHealth, eHealth…) Technologies Big Data Analytics Behavioral Modification Tools -etics
  • 6. A week in the lab can save an hour of data mining. Today’s real problem – how to use what we already know! Experiments Data Yesterday Data Data Data True/False Data Data Mining Tomorrow Massive Databases
  • 7. Cheminformatics Platform at Merck PCC Lead Identification Get Me The Data Preclinical Phase 3 Lead Lead First in Human Candidate to Optimization Optimization to to First in Human Phase 2B File What Do I Make Next? Now, Help Me Make It End User Interface, Analytics Tools, Chemist Sharepoint (one.merck.com/cheminfo) WorkBench Sharepoint Integration and(one.merck.com/cheminfo) Services Model/Workflow Sharepoint (one.merck.com/cheminfo) Core Merck Data Repositories Sharepoint (one.merck.com/cheminfo) Transactional IT Applications Sharepoint (one.merck.com/cheminfo) Local (Project Team) QSAR Models Sharepoint (one.merck.com/cheminfo) Ligand-based Design Support - Sharepoint (one.merck.com/cheminfo) Structure-based Design Support
  • 8. Need to converge activities to gain the most value and leverage Today Chemistry Independent Pairwise Processes SAR Chemistry Screening Chemical Genomics Genomics Future State Converged Processes Build Systems To Find Correlation In The Data Screening Pathways Genomics The Greatest Information Content & Value Is In The Intersection Of The Data “Chemical-Biology” Chemistry Screening
  • 9. Translational Research Platform at Merck PCC Pre-Lead Optimization Chemical Biology (chemical probes predict targets) Lead Identification Phase IIb Preclinical Phase 3 Lead Lead First in Human Candidate to Optimization Optimization to to First in Human Phase 2B File Early Development Clinical Trials (ADMET predictions) Get Me The Data What Do I Make Next? Now, Help Me Make It End User Interface, Analytics Tools, Chemist Sharepoint (one.merck.com/cheminfo) WorkBench Integration and Sharepoint (one.merck.com/cheminfo) Model/Workflow Services Sharepoint (one.merck.com/cheminfo) Core Merck Data Repositories Sharepoint (one.merck.com/cheminfo) Transactional IT Applications Systems Biology (off target activity prediction) Chemical Pharmacology (toxicity predictions) -
  • 10. From Two Crows Consulting in 1999 (1) What Are The Questions (2) Agile Process First – Find All The Data and Layers (3) Then Build The Solution on SOA Framework
  • 11. Information Models Source: http://www.inmoncif.com Source: http://www.w3.org Source: Apache Software Foundation
  • 12. Hybrid Solutions Source: Cloudera and Teadata
  • 13. Overview Pharmaceutical organizations are defining the road map for data integration but how prepared are they to base their decisions and practices on this data? Failure to truly encompass the attributes of a data driven unit will hurt your ability to compete in the market. This presentation will help business line executives and data professionals to understand the steps needed to create a data driven organization, by making the right decisions, while providing some real life examples on companies who have done this successfully. • Defining an information architecture framework for global research and development processes • Enlisting champions and creating an entrepreneurial spirit to empower people to own new processes • Key role players you cannot do without – creating a cohesive strategy and building a winning team
  • 14. Information Silos An information silo is a management system incapable of reciprocal operation with other, related management systems.
  • 15. Information Silo Causes • Technology – Enterprise data systems are too rigid, slow, prone to outages, hard to use… • Process – Legacy processes don’t factor in the need for information sharing (the technologies didn’t exist)… • People – People are not properly incentivized for collaborative work and lack trust…
  • 16. Information Silo Effects • • • • • Limits productivity Stifles creativity Hampers innovation Inhibits collaboration <Fill in the blank with your favorite pejorative expression>
  • 17. Information Silo Solutions • Provide technologies that support information sharing processes and reward collaborative behaviors (people).
  • 18. Information Integration Technologies (Life Sciences) • • • • • Standard Data Models (CDISC, etc.) Standard RDB Platforms (Oracle, etc.) Standard Ontologies (W3C, etc.) Semantic Platforms (IOInformatics, etc.) All of the above (Open PHACTS)
  • 19. Collaboration Platforms (Life Sciences)
  • 20. Collaborative Business Culture Why Don’t People Collaborate (Share Information)? • • • • • • • • Not knowing the answer. Unclear or uncomfortable roles. Too much talking, not enough doing. Information (over)sharing. Fear of fighting. More work. More hugs than decisions. It's hard to know who to praise and who to blame. http://blogs.hbr.org/cs/2011/12/eight_dangers_of_collaboration.html
  • 21. Collaborative Business Culture • 10% of Senior HR Execs and 39% of Employees Believe that their Companies Effectively Encourage Collaboration • Mutual Trust (Lack of) is a Significant Barrier to Collaboration – 31% of Developed Market R&D Staff Trust Emerging Market Colleagues – 22% of Emerging Market R&D Staff Trust Developed Market Colleagues Source: Research and Technology Executive Council Research
  • 22. Stimulating Information Sharing (NIH/FDA) Reports > Harnessing the Potential of Data Mining and Inform ation Sharing 12/ 9/ 11 10:17 AM Home > About FDA > Reports, Manuals, & Forms > Reports About FDA With the establishment of NCATS in the fall of 2011, NIH aims to reengineer the translation process by bringing together expertise from the public and private sectors in an atmosphere of collaboration and precompetitive transparency. Through partnerships that capitalize on our respective strengths, NIH, academia, philanthropy, patient advocates, and the private sector can take full advantage of the promise of translational science to deliver solutions to the millions of people who await new and better ways to detect, treat, and prevent disease. Harnessing the Potential of Data Mining and Information Sharing Previous Section: Expedited Drug Development Pathway 1 FDA currently houses the largest known repository of clinical data (all of which is deidentified to protect patients’ privacy), including all the safety, efficacy, and performance information that has been submitted to the Agency for new products, as well a an increasing volume of post-market safety surveillance data. The ability to integrate and analyze these data could revolutionize the development of new patient treatments and allow us to address fundamental scientific questions about how different types of patients respond to therapy. As noted in PCAST’s Report to the President on Health Information Technology, IT has the potential to transform healthcare and— through innovative capabilities—improve safety and efficiency in the development of new tools for medicine, support new clinical studies for particular interventions that work for different patients, and transform the sharing of health and research data. FDA currently houses the largest known repository of clinical data (all of which is de-identified to protect patients’ privacy), including all the safety, efficacy, and performance information that has been submitted to the Agency for new products, as well as an increasing volume of post-market safety surveillance data. The ability to integrate and analyze these data could revolutionize the development of new patient treatments and allow us to address fundamental scientific questions about how different types of patients respond to therapy. It would also provide an enhanced knowledge of disease parameters— such as meaningful measures of disease progression and biomarkers of safety and drug responses that can only be gained by analyses of large, pooled data sets — and would allow a determination of ineffective products earlier in the development process. Additionally, the ability to share information in a public forum about why products fail, without compromising proprietary information, presents the potential to save companies millions of dollars by preventing duplication of failure. FDA sometimes sees applications from multiple companies for the same or similar products. Although we may have reason to believe that such a product is likely to fail or that trial design endpoints will not provide necessary information based on a previous application from another company, we are currently unable to share this information. As a result, companies may pour resources into the development of products that FDA knows could be dead ends. To harness the potential of information sharing and data mining, FDA is rebuilding its IT and data analytic capabilities and establishing science enclaves that will allow for the analysis of large, complex datasets while maintaining proprietary data protections and protecting patients’ information. Scientific Computing and the Science Enclaves at FDA Historically, the vast majority of FDA de-identified clinical trial data has gone un-mined because of the inability to combine data from disparate sources and the lack of computing power and tools to perform such complex analyses. However the advent of new technologies, such as the ability to convert data from flat files or other formats like paper into data that can be placed in flexible relational database models, dramatic increases in supercomputing power, and the development of new mathematical tools and approaches for analyzing large integrated data sets, has radically changed this situation. Furthermore, innovations in computational methods, including many available as open-source, have created an explosion of statistical and mathematical models that can be exploited to mine data in numerous ways to enable scientists to analyze large complex biological and clinical data sets. The FDA scientific computing model provides an environment where communities of scientists, known as enclaves, can come together to analyze large, integrated data sets and address important questions confronting clinical medicine. These communities will be project-based and driven by a specific set of questions that will be asked of a dataset. Each enclave is defined by its participants, datasets, and sets of interrogations to be performed on the data. Enclaves may be comprised of internal FDA scientists and reviewers working together or outside collaborators working with FDA scientists under an appropriate set of security controls to protect the sensitive and proprietary data of patients and sponsors, respectively. Engagement of industry sponsors as part of community building will be vigorously pursued, leveraging expertise from the companies that submitted the data in a public-private partnership model. The scientific computing environment will also provide a dedicated infrastructure for application development and software testing for FDA scientists and reviewers. This will allow FDA staff to develop new applications to improve review, monitoring, and business processes in an environment separate from where regulatory review data is assessed. Additionally, the scientific computing environment will be used to evaluate novel software developed outside of FDA and to rapidly incorporate innovative developments in support of FDA regulatory reviews. This ability to “test drive” new applications outside the regulatory review environment has the potential to shorten traditional FDA development cycles and facilitate the adoption of new software that can enhance quality, efficiency, and accuracy of FDA regulatory reviews, as well as streamline the adaptation of new higher-powered analytical tools into FDA review and research efforts. http:/ / www.fda.gov/ AboutFDA/ ReportsManualsForm s/ Reports/ ucm 274442.htm Page 1 of 3
  • 23. Stimulating Information Sharing (NHS, EU) Prime minister David Cameron has announced a package of measures designed to boost the UK's life sciences industry. These include a £180 million fund to support innovation and plans to allow healthcare companies access to NHS patient records to support research. Horizon 2020 is the financial instrument implementing the Innovation Union, a Europe 2020 flagship initiative aimed at securing Europe's global competitiveness. This conference will explore how EU funding can promote economically and socially sustainable innovation models with the aim of more openness, easier accessibility and higher result-oriented efficiency.
  • 24. Caveats A well-constructed system can enable scientist to test but also generate new hypotheses using wellcurated, high-content translational medicine data leading to deeper understanding of various biological processes and eventually helping to develop better treatment options. Active curation and enterprise data governance have proven to be critical aspects of success.
  • 25. The Future: Virtual Life Sciences • Forrester has identified three themes driving the future of collaboration and information sharing technology – The global, mobile workforce • 62% of workforce works outside an office at some point (this number is growing) – Mobility driven consumerization • Cloud-based collaboration solutions are being used in conjunction with numerous devices – The principle of “any” • Need to connect anybody, anytime, anywhere on any device
  • 26. Life Science Information Landscape A rapidly evolving ecosystem Yesterday Today Tomorrow Big Life Science Company Yesterday Today Tomorrow Innovation Model Innovation inside Searching for Innovation Heterogeneity of collaborations. Part of the wider ecosystem IT Internal apps & data Struggling with change Security and Trust Cloud/Services Data Mostly inside In and Out Distributed Portfolio Internally driven and owned Partially shared Shared portfolio 26
  • 27. The Evolving Life Sciences Ecosystem  Evolving paradigm for the discovery of medicines (Collaborative)  A vision that points towards open innovation and collaborations  Open research model to collectively share scientific expertise  Enhance speed of drug discovery beyond individual resource capabilities (Speed)  Limited research budgets and capabilities driving greater shared resources  Goal to see all partners succeed by accelerating the SCIENCE  Synergize Pfizer’s strengths with Research Partners (Knowledge)  Pair Pfizer’s design, cutting edge tools, synthetic excellence with research partners (academics, not-for-profits, venture capitalists, or biotechs) to develop break through science, novel targets, and indications of unmet medical need  Current example of academic and not-for-profits partners (Discover and Publish)  Drive to publish in top journal with science receiving high visibility and interest Body clock mouse study suggests new drug potential Mon, Aug 23 2010 By Kate Kelland LONDON (Reuters) - Scientists have used experimental drugs being developed by Pfizer to reset and restart the body clock of mice in a lab and say their work may offer clues on a range of human disorders, from jetlag to bipolar disorder. a few months ago we entered into a collaboration with the giant pharmaceutical industry Pfizer to test some of their leading molecules for potential relevance to HD. Contacts:  Travis Wager (travis.t.wager@pfizer.com)  Paul Galatsis (paul.galatsis@pfizer.com)
  • 28. Overview Pharmaceutical organizations are defining the road map for data integration but how prepared are they to base their decisions and practices on this data? Failure to truly encompass the attributes of a data driven unit will hurt your ability to compete in the market. This presentation will help business line executives and data professionals to understand the steps needed to create a data driven organization, by making the right decisions, while providing some real life examples on companies who have done this successfully. • Defining an information architecture framework for global research and development processes • Enlisting champions and creating an entrepreneurial spirit to empower people to own new processes • Key role players you cannot do without – creating a cohesive strategy and building a winning team
  • 29. Collaboration and Information Sharing Barometer • Does your company.. – …motivate and link innovation efforts by identifying and routinely communicating key areas for innovation activity? – …have a strategy that allows for geographically dispersed staff to access the resources necessary to collaborate and share information? – …have tools that support rapid collaboration, such as data sharing and analysis or crowdsourcing platforms?
  • 30. People: Some Questions to Ask • What is the staff structure as it relates to data reporting? • Do staff members have the training they need to understand relevant data? • Do staff members understand how to glean insights and actionable steps from data? • Do staff members have good working relationships with data analysts? http://wholewhale.com/data-culture-building/
  • 31. Process: Some Questions to Ask • Are staff accessing and communicating data across teams well? • Do staff act on data or regularly share learnings from experiments? • Are goals set in a way that can be tracked through metrics? • Does the organization use a Gather<Analyze<Insight method? • How often do staff receive data feedback? http://wholewhale.com/data-culture-building/
  • 32. Technology: Some Questions to Ask • Are tools in place to analyze large data sets (beyond Excel)? • Are consistent naming and storage conventions in place across databases? • Are dashboards and metrics updated as automatically as possible? • Is data stored in a way that reporting can be done across the organization? • Are semi-annual security audits and passwords changed? http://wholewhale.com/data-culture-building/
  • 33. Overview Pharmaceutical organizations are defining the road map for data integration but how prepared are they to base their decisions and practices on this data? Failure to truly encompass the attributes of a data driven unit will hurt your ability to compete in the market. This presentation will help business line executives and data professionals to understand the steps needed to create a data driven organization, by making the right decisions, while providing some real life examples on companies who have done this successfully. • Defining an information architecture framework for global research and development processes • Enlisting champions and creating an entrepreneurial spirit to empower people to own new processes • Key role players you cannot do without – creating a cohesive strategy and building a winning team
  • 34. Thank You • Chris L. Waller, Ph.D. • chris.waller@merck.com • http://www.linkedin.com/in/wallerc