Data-Driven Pharma: Steps to Build a Winning Strategy

Business Transformation –
Becoming a Truly Data- Driven
Pharmaceutical Company
Chris L. Waller, Ph.D.

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

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

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

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

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
Core Merck Data Repositories
Transactional IT Applications


Local (Project Team) QSAR
Models


Ligand-based Design
Support

-


Structure-based Design
Support

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

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
WorkBench
Integration and Sharepoint (one.merck.com/cheminfo)
Model/Workflow Services
Core Merck Data Repositories
Transactional IT Applications

Systems Biology
(off target activity
prediction)

Chemical Pharmacology
(toxicity predictions)

-

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

Information Models

Source: http://www.inmoncif.com
Source: http://www.w3.org

Source: Apache Software Foundation

Hybrid Solutions

Source: Cloudera and Teadata

Information Silos
An information silo is a management system incapable of reciprocal operation with other,
related management systems.

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…

Information Silo Effects
•
•
•
•
•

Limits productivity
Stifles creativity
Hampers innovation
Inhibits collaboration
<Fill in the blank with your favorite pejorative
expression>

Information Silo Solutions
• Provide technologies that support information
sharing processes and reward collaborative
behaviors (people).

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)

Collaboration Platforms
(Life Sciences)

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

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

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

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.

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.

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

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

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)

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?

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/

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?

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?

Thank You
• Chris L. Waller, Ph.D.
• chris.waller@merck.com
• http://www.linkedin.com/in/wallerc

Data-Driven Pharma: Steps to Build a Winning Strategy

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