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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5

Today Next 2-3 Years Beyond

Culture of Single Use

“Find & Access”

Dec

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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

1

2

3

4

5

6

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

1

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

1

2

3

4

5

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