Pistoia Alliance US Conference 2015 - 1.1.2 Innovation in Pharma - Chris Waller

1. Innovation at the “Edge”
Chris L. Waller, Ph.D.
(with help from Jack and Hunter)

2. Platforms and the “Edge”

3. The “Edge”

4. Insert picture of kid solutions crossing canyon
Now What?

10. That’s a jet pack!

11. A portal.

12. A robot shark.

13. Create and Capture Value
OnStar Platform
• OnStar will give certified developers
“safe access” to ATOMS, OnStar’s
Advanced Telematics Operating System,
the car industry’s largest cloud-based
automotive platform.
• The information includes status about
the car such a its exact GPS location,
whether doors are locked, condition of
the battery if it’s a hybrid or EV, even the
ability to remotely unlock the car.
• Those attributes made for a happy first
partner, RelayRides, a community car-
sharing service.

14. Platform Economics
14
Price
• Platforms drive out point solutions and application silos
• Platform adoption drives down the cost of new services
• Lower cost of service development drives innovation
• APIs allow for third party contribution
Platform Layer 1
Platform Layer 2
Quantity
Enterprise platforms drive economies of scale, business agility, rate of innovation,
and information velocity across divisions … eats points solutions for lunch.
App
1
App
2
Adapted from Marshall Van Alstyne

15. Platforms and MSD (Merck & Co.)

16. Platform Definition
Types of IT Platforms
• Business Capability – Software, data and integrations that
directly enable a set of business functions & activities (e.g., ERP,
Customer Engagement)
• Application Delivery - Software and data services on top of which
Business Capability platforms or stand-along applications are
designed, built and deployed (e.g., Data Analytics,
Web/Collaboration)
• Infrastructure – Core, ubiquitous foundational network,
hardware, and system software (e.g., Network, UC)
16
PlatformDevelopers
Interface
Developers, Customers /
Users
New Features and
functionality
Platform Ecosystem
A set of highly-related information and technology capabilities that when combined,
provide economic value to Merck’s business through faster speed to market and reduced
unit costs . They should be planned, delivered and managed as a whole set of capabilities
(rather than independently).1
Platforms create and capture new value for Merck

17. The Global Innovation Network

18. The Scientific Modeling Platform

19. “Analytics” Continuum at MRL
Analytical
complexity/depth
Descriptive
Analytics
Prescriptive
Analytics
Predictive Modeling / Simulation /
Optimization
What will happen if ..? What’s the best choice?
What are the alternatives?
What should we do?
Statistical and Mathematical Analysis
What is the cause?
Is my hypothesis correct?
Enquiry Analytics
Data Exploration & Mining
Analysis / Visualization /
Query / Drill down / Alerts
Hypothesis generation
What is the problem? Is there a
pattern? What is a good question to
ask? When is action needed?
Ad hoc and Custom
Reports
How did it happen?
Standard Reports and
Dashboards
What happened?
JM Johnson, DRAFT 6/5/2014
Based on a similar slide from Booz Allen Hamilton
Predictive
Analytics

20. Anatomy of Analytics
20
Shared libraries,
models, algorithms,
indexes and self-
service
IT as Platform
Liberation and
integration of data
(internal and external)
Standard Processes . Data Stewardship . Unified Software
Bigger questions,
actionable insights
Fraud Detection
Real World Evidence
Molecule Simulation
Pricing / Promotion
Inventory /
Working Capital
Over-Payments

21. Imagine a world where…
• …primary activity, pharmacokinetic, and pharmacodynamic models are
linked and support early discovery programs.
• …comparator models support programs in discovery and development.
• …model supported trial design, clinical planning and trial avoidance are
integral parts of all our early and late stage development programs.
• …real-time visualization and simulation allow us to see impact of
assumptions, comparison of models, and understand uncertainty.
• …quantitative decision making is routinely used integrating knowledge across
the discovery / development continuum and regulatory and patient decisions.
• …model aided drug approvals are achieved.
• …models can be ultimately be used at the “bedside” to optimally inform
dose selection, patient selection and that the models update in real-time with
each patient.

22. Level 4Level 3Level 2Level 1
What Keeps Us From Doing This Today?
EDDS
Data
EDDS
Models
PCD
Data
PCD
Models
Clinical
Data
Clinical
Models
Real
World
Data
Real
World
Models
Discovery Pre-clinical Clinical Outcomes
While we are beginning to see sharing of models and integration of data WITHIN
functional domains, we are still advancing sub-optimal POC entities.
Technology: Siloed information and model management solutions
Process: Siloed workflows
People: Siloed thinking
Root Causes

23. What Does the Future Look Like?
EDDS
Data
EDDS
Models
PCD
Data
PCD
Models
Clinical
Data
Clinical
Models
Real
World
Data
Real
World
Models
Discovery Pre-clinical Clinical Outcomes
Cultural, behavioral, and technical barriers between functional domains are
eliminated and data, models, and knowledge are used holistically to advance the
most promising entities.
Data Models
Integration Layer
Delivery Layer
End User Experience Layer
Merck Scientific
Modeling Platform
Merck Information
Management Platform
Nirvana

24. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and External,
Structured and
Unstructured)
Models and Simulations
(Data)
Workflows
(Best Practices)
Learning Loops (DMAIC Cycles) within the functional domains of Pharma R&D Support:
• Adaptive Research Operating Plans
• Adaptive Clinical Trials
• Behavioral Modification…
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl

25. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and External,
Structured and
Unstructured)
Models and Simulations
(Data)
Workflows
(Best Practices)
Cross-domain DMAIC Loops…

26. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and External,
Structured and
Unstructured)
Models and Simulations
(Data)
Workflows
(Best Practices)
Can we construct pan-R&D workflows that incorporate existing data, predictive models, and best practices
to drive design, predict full product lifecycle, and increase probability of success?

27. Platforms Power Applications and Enable Business Outcomes
27
Translational Medicine IT Preclinical Development IT Clinical, Regulatory, & Safety
IT
CORE & OCMO IT
QSAR Workbench,
ADMET Workbench,
Spotfire, Excel
M&S Workbench,
Model Explorer,
Spotfire
A&R Workbench
HEM Workbench,
Excel, Spotfire
Cross-functional Analytics & Predictive Modeling (Scientific Modeling Platform)
Validate
Model
Cross-functional Information Access & Interoperability (Scientific Information Management Platform)
Business Outcomes
Decrease SDV / GCD Cost Decrease Time to Market
Increase in Analysis of Real
World Data
Ensure 100% Compliance
Increase Analytics Based
Decision Making
Increase Biologics
contribution to 40%
Increase use of modeling for
trials and submissions
Scientists can find
Information they need
Improve POC Success to 60%
Build
Model
Store
Model
Recall
Model
Publish
Model
Execute
Model
Retire
Model
Enhance
Data
Ingest
Data
Integrate
Data
Filter
Data
Aggregate
Data
Transform
Data
Serve
Data
Cross-functional Information Creation and Collection (Enterprise and Laboratory Platforms)
Enhance
Data
Create
Data
Import
Data
Curate
Data
Control
Data
Transform
Data
Serve
Data

28. Platforms Enable Innovation
• New Collaborations: Fundamental to the development of the platform, and an area of
precompetitive interest, is the creation of vocabularies, metadata, and ontologies for the
management, integration, and appropriate usage of models. Additionally, APIs for will needed to be
standardized to support integration of COTS and custom packages.
• New Capabilities: Once the Scientific Modeling Platform is in place, there will be opportunities to
innovate (1) in the data provision/model sources area (e.g., IMI2/RADAR), (2) in the areas of model
lifecycle management services (e.g., model validation), statistical/analytical methods (e.g., new
algorithms), and (3) in the overall end-user experience through the creation of new applications and
user interfaces.
• New Business Models: Additionally, as a cloud-hosted and publically available resource (much like
the Google predict API), we envision the Scientific Modeling Platform providing a unique ecosystem
for the broad-scale creation and distribution of models to support pre-competition and open science
and potential monetization of modeling related assets (e.g., data ingestion services, model-ready data
sets, data analysis services, predictive modeling services, models, …).

29. Key Messages
• Platforms provide stable foundations on which to innovate.
• Platforms have edges (APIs) and are open systems.
• Platforms provide tremendous financial benefits.
• Platforms support agile delivery of applications.
• Platforms are transforming Merck & Co. (MSD).

30. Thanks!
My Team
Charlie Chang, Director, Early Discovery Modeling Platforms
Kam Chana, Assoc. Director, Preclinical/QP2 Modeling Platforms
Mark Kruger, Assoc. Director, CORE/HES Modeling Platforms
Eric Gifford, Principal Scientist (On Rotation), Model Standards
Extended Team
Lindsay Augusterfer (Business Analyst)
Nicole Glazer (SIM Interface, Portfolio)
David Kniaz (Business Analysis/Architecture)
Mark Schreiber (Information Architecture)
Greg Tietjen (Clinical Architecture)
Tom Rush (tPKPD, Modeling and Simulation COP)
Daniel McMasters (Early Discovery Modeling SME)
Ryan Vargo (QP2 Modeling SME)
Erik Dasbach (HES Modeling SME)
Matt Walker (GIC/Engineering Interface)
Mike Stapleton
Susan Shiff
Frank Brown
Sandy Allerheiligen
Jason Johnson
Special Thanks!
Jim Ciriello
Doug Redden
Patrick Graziano
Clark Golestani
Extra Special Thanks!
Jack Waller
Hunter Grossman

31. Questions?