Scientific Information as a Business Asset Driving Productivity at Merck Research Labs Through Novel Approaches to Scientific Information Management Speaker: John Koch Merck & Co.
Scientific Information as a Business Asset Driving Productivity at Merck Research Labs Through Novel Approaches to Scientific Information Management
Speaker: John KochMerck & Co.
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Overview
• Information Management Challenges Currently Facing R&D Organizations
• The Value of Better Information Management
• Merck’s Scientific Information Architecture and Search (SIAS) Group
• Approaches for Improving Information Management
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R&D decisions rely on high quality information to steer programs and the pipeline
145 Knowledge Assets“Target validation plan”
250 Business Groups“Early Development team”
1849 People“John Smith”
1144 Information Types“Clinical Trial Name”
110 Organization Units“Analytical Chemistry”
492 Sources“Electronic Lab Notebook”
66 Business Processes“Integrative assessment of liver
toxicity”
86 Decisions/ Gateways“Determine Patient
Stratification Biomarkers”
472 Activities“Refine model”
125 Roles“Statistician”
R&D Information LandscapeR&D decisions rely on high quality information to steer programs and the
pipeline
Over time BioPharma has created and stored tremendous amounts of data, information and knowledge; there are
100,000’s of information elements
Companies must make effective, efficient use of the vast quantity of
information it houses, creates, and has access to externally to make sound
decisions
The volume and sophistication of internal information and that available through external sources continues to grow at a rapid and accelerating rate
Therefore, the ability to readily find, access, and use information is absolutely critical
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The Problem
1000’speople
100’sinformationtypes
1000’srepositories
100’sdecisions
100,000’sknowledgeassets
Scannell et al. 2012 Nature Rev. Drug Disc. 11, 191
100’steams
$
InformationComplexity
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KnowledgeInformationData
Combine internal and external data
Integrate & Analyze Present Decide
Culture of Single Use
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Today Next 2-3 Years Beyond
Culture of Single Use
“Find & Access”
Dec
isio
n M
akin
g Q
ualit
y
Vocabulary Management
Embedded Stewardship
Information Flows Modeled
Effective Search
Integrated Information Architecture
IM Challenges Characterized
Fragmented tools,
processes
Systematic categorization
of data
Info
rmat
ion
Man
agem
ent M
atur
ity
As knowledge workers understand and embrace improved information management practices, better decision making can be enabled by better access to information
Organization-Wide Information Re-Use
? Better Information Management Better Decision Making: Better analysis, more transparency and collaboration, better workflow management, faster decisions
Dec
isio
n Q
ualit
yA
dopt
ion,
Mat
urity
Improving R&D Decision Making
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Engaging the business: Focus Area Key Questions
User Interface Engine Content Creators
Creators
ContentEngineQuery Results
Interface
What information is required to make those decisions? Who needs that information? How do they use that information used to make those decisions?2
What are the critical business processes? What major decisions are associated with those processes?1
How is that information created? Who creates it? Where is that information stored?3
How is that information accessed (searched for, found, displayed)?4
What challenges are associated with accessing and using that information?5
How can access to and use of that information be improved? What value will those improvements deliver to the business?6
UsersMorville & Callendar. 2010 Search Patterns
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Information Management CapabilitiesA
rchi
tect
ure
Sear
chA
cces
s
IM Capabilities DescriptionSearch tools that enable users to locate scientific information across various sources, both structured and unstructured, in various formats and across functional groups
Capability for users to identify colleagues with specific skills, expertise, or tacit knowledge through a search tool and / or standardized profiles or tagging
System of access policies that prudently permits access to information and has clear procedures for granting or restricting access
Shared practices for creating, storing, sharing, and maintaining explicit and tacit information
Organization of critical data sources to make them more conducive to search, retrieval, analysis and re-use through techniques including tagging and indexing
Well-maintained record of critical information and data sources across the organization, including how the information is used or linked to other sources
Improving Information Management requires specific capabilities to enhance information search, access, and architecture
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2
3
4
5
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Expertise Location
Access
Data Stewardship
Data Structuring
Key Data Assets
Scientific Search
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ILLUSTRATIVE
Leaders in Search & Information Management:
Indexing of complex hierarchical relationships from relational database tables
Multi-faceted, interactive filtering of search results based on document metadata
Implementing solutions for searching non-text information (e.g., enterprise video search)
Advanced search analytics Integration with social
media
Highly scalable / extensible Service-Oriented Architecture
Seamless information flow between departments / sites
Includes a data services and exchange layer
Reusable and configurable code modules
Closed-loop data flow via integrated data sources across the product life cycle
Consistent, personalized, real-time access for internal and external users
Enterprise-wide technology to capture, create, and share knowledge / best practices
Data stewardship standards and processes that ensure consistency of data quality, storage, and exchange
BioPharma and other industry players have demonstrated innovative, peer-leading Search, Access, and Architecture capabilities
Capability Maturity Stages
Basic
Developing
Functional
Advanced
World-class
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2
3
4
5
OpenAccess
DataStewardship
DataStructuring
Key DataAssets
ScientificSearch
ExpertiseLocation
ArchitectureSearch Access
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Basic
Developing
Functional
Advanced
World-class
Data access permissions that reflect a balance between security and accessibility
A culture of collaboration enables information access across divisions
Designated roles and responsibilities to champion data stewardship
Employees know what information to store and where to store it
Well defined best practices, search processes, and rules
Employees understand the search content and participate in helping steward data
Query experts help conducting complex searches
Intuitive tools and applications ensure all information is searchable
Well established processes for categorizing, structuring and storing information
Clearly defined data assets in key business areas
Well-defined links between key data assets to enable interoperability between different information types
What does “good” Search look like for R&D?
Addressing identified challenges will produce a future state with capable people, processes and technologies to enable fluid information exchange and better decision making
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2
3
4
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Current State
Capability Maturity Stages
Search Access Architecture
Access DataStewardship
DataStructuring
Key DataAssets
ScientificSearch
ExpertiseLocation
ILLUSTRATIVE
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SIAS has developed a flexible, repeatable business engagement and problem solving approach
Scope Pilot: Define scope of problem, including specific business impact and value proposition
Define Requirements: Define use cases; prioritize and select use case(s) to test in Pilot
Select / Model Use Case(s): Model information flow for selected use case(s), select pilot platform
Execute Pilot(s): Build test environment; create / update processes / standards; test use case & determine if needs are met
Build Business Case / Roadmap: Develop business case & roadmap for scale-up; validate with business users and sponsor
Scale Solution: Expand coverage / capability to new information types, sources, users; measure adoption, performance, value realized
Embed and Maintain: Assess long-term production viability; define long-term roadmap; take viable solutions to production scope / capability
Monitor / Measure: Continue to track performance; re-visit unaddressed business issues
Target and Engage Business Area: Build relationships in target areas; gauge IM needs
Identify Pain Points: Document high level business processes, identify & map key information types & sources, characterize pain points
Validate / Prioritize Issues: Define impact of pain points, detail / prioritize use cases aligned to business impacts, develop business case
Solve (Pilot Solution)
Execute Pilot(s)
Define Requirements
Scale and Embed
Build Business Case / Roadmap
Monitor / Measure
Scope Pilot
Model Use Case(s)
Scale Solution
Embed and Maintain
Target & Engage Business Area
Identify Pain Points
Validate & Prioritize Issues
Engage and Diagnose
SIAS follows a consistent process for diagnosing and solving specific business area IM issues, then embedding and transitioning those solutions
1-6 months 6-18 months1-3 months
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Drive an integrated approach to improve Information Management & Search
Targeted IM solutions: Deliver improvements in processes, technologies, and / or behaviors that improve data quality / availability
Stewardship: A set of shared practices for creating, storing, sharing, and maintaining information that is conceived, sustained, and improved by business Information Stewards
Address complex, specific business needs with appropriate processes / capabilities Deep coverage of information sources
Search: Deploy a search capability to make information more accessible, explorable and useful for scientists
Addresses broad, high-level search use cases Provide exploratory and analytic capabilities to drive value high ROI opportunities Big Data framework that can deliver use cases beyond scalable search
Define, communicate, embed, and monitor good stewardship practices Create a vital link between business, information, and technology
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Knowledge Assets
Business Groups
People
Information TypesOrganization Units
Sources
Business Processes
Decisions/ Gateways
Activities
Roles
The R&D Information Landscape is increasingly complex
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sIFM is a method of documenting and modeling the flow of information through an enterprise (from data generation to knowledge creation) that allows both targeted analysis (e.g. information flow through a specific business process for a select organization), as well as holistic analysis (e.g. complex, cross-organizational information flows, processes, and knowledge transitions) across the information continuum.
PPDM
GHH
MCC
• Regulatory
MRLMMD
PharmSci
Merck
Traditional Business Analysis
Multiple BA resources working to develop project/area-specific analysis artifacts using a variety of methods and representations (not connected; shared and stored in isolation)
Multiple BA resources working to represent information flows in a common way, so that related information entities are connected, complex interactions can be visualized, understood and analyzed, and project/area-specific ‘views’ of the model can still be generated
Semantic Information Flow Modeling
Semantic Information Flow Modeling (sIFM)
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Results in disparate analysis artifacts (ppt, excel, word/text) with related information within them that aren’t linked
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Applying sIFM
Ontologies / Taxonomies / Relationships
Enhanced workflows, stewardship models
Improved Integration, Search, Decision support
Applying sIFM to represent and analyze complex information domains, and knowledge transitions, in order to successfully identify and implement technologies that enhance information/knowledge structure, interoperability, and search.
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Information Management SolutionQUICK – Overview
SIAS characterized several information management challenges which dictated the need for a knowledgebase of definitive pre-clinical compound data for Pharmacology / Drug Metabolism
Dispersed Historical DataA lengthy, complicated process is required, on a regular
basis, to retrieve information off hard-drives, shared drives, and outdated repositories
Duplicative Data Capture / ProcessingThe precedent of creating Excel copies of data for
upload to Teamsites consumes resources and creates islands of potentially outdated data
Access / Storage of Definitive DataUnable to effectively manage definitive data for
compounds
Challenges
Incomplete Data UploadA large portion of the data generated is not uploaded
into structured repositories
Harmonizing Reporting StandardsInadequate governance over data upload protocols and
non-standardized assay reporting formats limit data usability for cross-compound comparisons
Solution
QUantItative PharmaCology Knowledgebase (‘QUICK”)
Single, authoritative portal for access to definitive, integrated data sets of clinical and
pre-clinical metabolism and in vivo pharmacology experimental results
Exposed data will be targeted, but not limited to, addressing hypothesis generating questions
relating to predictive modeling such as human dose prediction, study avoidance, and BIC benchmarking of candidate selection, and
translational PK/PD modeling Data will be made available in a well-structured
and searchable format allowing easy data representation and integration with existing and
future data analysis and visualization tools
Centralized & Structured Data
Improved Retrieval & Access
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Information Management SolutionQUICK – Expected Value
Improvement Opportunities Description
Improve Data Collation / Reporting Efficiency for Definitive Pre-Clinical Data
Reduce time to collate definitive datasets by ~95%
Enhance Analytical Productivity and Opportunities
50-75% increase in efficiency of analysis (comparisons of results from prior assays)
Enhance CollaborationImproved collaboration through stewardship and metadata management, increasing productivity by 50% for modeling and simulation; increased pharmacology / drug met. productivity
Study AvoidancePotentially eliminate unnecessary studies due to faster access to more accurate definitive datasets, resulting in better study selection and confidence in progressing / killing compounds
QUICK enables decisions to avoid costly studies through better design and decision making and greater productivity through better data quality, structure, and accessibility; improved data collation capability; and improved collaboration and cross-functional information sharing
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Acknowledgements
• SIAS• Informatics IT• MRL-IT• MRL• Deloitte