Building a Culture of Model-driven Drug Discovery at Merck

Building a Culture of Model-
Driven Drug Discovery
Chris L. Waller, Ph.D.

Forward-Looking Statement
This presentation includes “forward-looking statements” within the meaning of the safe harbor provisions of the United
States Private Securities Litigation Reform Act of 1995. Such statements may include, but are not limited to, statements
about the benefits of the merger between Merck and Schering-Plough, including future financial and operating results, the
combined company’s plans, objectives, expectations and intentions and other statements that are not historical facts.
Such statements are based upon the current beliefs and expectations of Merck’s management and are subject to
significant risks and uncertainties. Actual results may differ from those set forth in the forward-looking statements.
The following factors, among others, could cause actual results to differ from those set forth in the forward-looking
statements: the possibility that all of the expected synergies from the merger of Merck and Schering-Plough will not be
realized, or will not be realized within the expected time period; the impact of pharmaceutical industry regulation and
health care legislation in the United States and internationally; Merck’s ability to accurately predict future market
conditions; dependence on the effectiveness of Merck’s patents and other protections for innovative products; and the
exposure to litigation and/or regulatory actions.
Merck undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information,
future events or otherwise. Additional factors that could cause results to differ materially from those described in the
forward-looking statements can be found in Merck’s 2011 Annual Report on Form 10-K and the company’s other filings
with the Securities and Exchange Commission (SEC) available at the SEC’s Internet site (www.sec.gov).

Thoughts on Strategy and Culture
• “Culture eats strategy for breakfast.”
– Peter Drucker and Mark Fields, Ford
• “Culture eats strategy for lunch.”
– Dick Clark, Merck
• “Culture eats strategy for dinner.”
– Chris Waller, Merck
• Peter Drucker often argued that a companies culture would trump any attempt
to create a strategy that was incompatible with it's culture.
• “Company cultures are like country cultures. Never try to change one. Try,
instead, to work with what you’ve got.”
– Peter Drucker

Cost to Develop and Win Marketing Approval
for a New Drug Is Increasing!
BOSTON – Nov. 18, 2014 – Developing a new prescription medicine that gains marketing approval, a process often lasting longer than a decade, is estimated to cost $2,558 million, according to a new study
by the Tufts Center for the Study of Drug Development.
The $2,558 million figure per approved compound is based on estimated:
Average out-of-pocket cost of $1,395 million
Time costs (expected returns that investors forego while a drug is in development) of $1,163 million
Estimated average cost of post-approval R&D—studies to test new indications, new formulations, new dosage strengths and regimens, and to monitor safety and long-term side effects in patients required by
the U.S. Food and Drug Administration as a condition of approval—of $312 million boosts the full product lifecycle cost per approved drug to $2,870 million. All figures are expressed in 2013 dollars.
The new analysis, which updates similar Tufts CSDD analyses, was developed from information provided by 10 pharmaceutical companies on 106 randomly selected drugs that were first tested in human
subjects anywhere in the world from 1995 to 2007.
“Drug development remains a costly undertaking despite ongoing efforts across the full spectrum of pharmaceutical and biotech companies to rein in growing R&D costs,” said Joseph A. DiMasi, director of
economic analysis at Tufts CSDD and principal investigator for the study.
He added, “Because the R&D process is marked by substantial technical risks, with expenditures incurred for many development projects that fail to result in a marketed product, our estimate links the costs of
unsuccessful projects to those that are successful in obtaining marketing approval from regulatory authorities.”
In a study published in 2003, Tufts CSDD estimated the cost per approved new drug to be $802 million (in 2000 dollars) for drugs first tested in human subjects from 1983 to 1994, based on average out-of-
pocket costs of $403 million and capital costs of $401 million.
The $802 million, equal to $1,044 million in 2013 dollars, indicates that the cost to develop and win marketing approval for a new drug has increased by 145% between the two study periods, or at a
compound annual growth rate of 8.5%.
According to DiMasi, rising drug development costs have been driven mainly by increases in out-of-pocket costs for individual drugs and higher failure rates for drugs tested in human subjects.
Factors that likely have boosted out-of-pocket clinical costs include increased clinical trial complexity, larger clinical trial sizes, higher cost of inputs from the medical sector used for development, greater focus
on targeting chronic and degenerative diseases, changes in protocol design to include efforts to gather health technology assessment information, and testing on comparator drugs to accommodate payer
demands for comparative effectiveness data.
Lengthening development and approval times were not responsible for driving up development costs, according to DiMasi.
“In fact,” DiMasi said, “changes in the overall time profile for development and regulatory approval phases had a modest moderating effect on the increase in R&D costs. As a result, the time cost share of total
cost declined from approximately 50% in previous studies to 45% for this study.”
The study was authored by DiMasi, Henry G. Grabowski of the Duke University Department of Economics, and Ronald W. Hansen at the Simon Business School at the University of Rochester.

Progressive, Unsustainable Decline in Productivity
Reported by Matthew Herper, Forbes 5/22/2014 “Who’s the best in drug research…”
http://www.forbes.com/sites/matthewherper/2014/05/22/new-report-ranks-22-drug-companies-based-on-rd/
2014 New Drug Approvals Hit 18-Year High
2014 was a good year for pharmaceutical
innovation – the best, in fact, since the
industry’s all-time record of 1996. FDA
approved a total of 44 drugs –

The productivity crisis in pharmaceutical R&D
Fabio Pammolli, Laura Magazzini & Massimo Riccaboni
Nature Reviews Drug Discovery 10, 428-438 (June 2011)
28,000 compounds from Pharmaceutical Industry Database
We are unable to predict success.
Failure Rates Increasing at all Stages of R&D

I can predict the future…with 99.4% accuracy.

Press Release v1 (Merck BHAG Realized)
Merck’s revolutionary model-driven approach to drug development leads to breakthrough therapies in Oncology and Neuroscience.
Boston, MA, November 4, 2024
In the last 12 months Merck has released breakthrough treatments for cancer and mental health in record time by using it’s revolutionary modeling platform for
human drug response.
By working with regulatory authorities world wide and leveraging public private partnerships, Merck has been able to develop deep models of human disease
allowing them to go straight to human trials. This has allowed them to greatly reduce the traditional timeline for drug development and by-pass controversial and
expensive animal trials.
Head of modeling Dr. Smith said that the approach was made possible by developing deep and accurate models of each individual in a clinical trial. “We actively
recruited patient populations and made use of sophisticated bio-sensors, nanotechnologies and real-time analysis to develop comprehensive predictive models of
their genetics, metabolism and disease”. Over a period of several years Merck modelers received constant streams of data from these volunteers giving them
unprecedented understanding of their disease. They combined this with large publicly funded datasets and crowd sourced and internal modeling methods.
“We are moving to a new paradigm in drug discovery where we enroll patients before we start therapeutic development” said Smith.
Merck believes that it’s modeling platform and methodology can be used to rapidly develop cures for other diseases and is actively seeking patients to donate
their health information as well as development partners to license this platform in new disease areas.
Note: This is completely fake and does not represent any forward looking statements on behalf of Merck.

Press Release v2 (Merck BHAG Realized)
Merck’s “Virtual PipelineTM” Powers Decision Making
Boston, MA, November 4, 2024
Merck released details today on a revolutionary platform that it created to support all aspects of the drug discovery and development process.
This 10 year journey began in 2014 with the acknowledgement that the pharmaceutical industry must transform in order to survive the mounting
financial and regulatory pressures.
In collaboration with regulatory agencies world-wide, Merck created the Virtual PipelineTM by adopting a Product Lifecycle Management (PLM)
mentality and completely and permanently altered the pharmaceutical research and development landscape.
“The existence of the Virtual PipelineTM and the ability to fully simulate the entire lifecycles of therapeutic agents allowed our business development
team to make an informed decision to acquire Iliad Pharmaceuticals’ entire portfolio with the intent to launch a drug that will see Merck re-enter the
infectious disease therapeutic area. It is our expectation that Merck will enter the market with First and Best-in-Class agents grossing in excess of
$10BN per annum.”, reported Dr. Hootie N.D. Blowfish, Head of Strategic Acquisitions.
While too early to verify, Merck projects that the Virtual PipelineTM will enable their research scientists to reduce the time from target identification
to product launch by as much as 40% with associated cost savings nearing 50%.
Note: This is completely fake and does not represent any forward looking statements on behalf of Merck.

Questions, questions, questions…
Research Development Commercial Medical
Drug Protein Target ResponseSystem Individuals PopulationsPathway
What entity should I make?
How active is my entity?
What other activities does my entity possess?
How can I make it?
Do I have the starting materials?
What dose is required?Is it likely to be metabolized?
Is clearance going to be a problem? What is the most effective formulation?
How can I make it in bulk?
What disease should I target?
What targets are involved?
What mechanisms are involved?
How are my competitors doing?
Is my compound more effective than comparators?
How much can I charge for this?
Can I patent this?

Transform
Deliver
Aggregate
Access
Drug Protein Target Response
Answers, answers, answers…
System Individuals PopulationsPathway
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)

Cheminformatics…paving the way for predictive
sciences at Merck (An Update)

Tools for Expert Modelers in Early Discovery
Model Generation and Capture
Automate model-building to drive consistency and share
best practices
Automate model capture and registration to ensure
consistent way to find and consume models
Automate updating of models to ensure latest data and
highest quality
Build QSAR Models Publish QSAR Models

Tools for Early Discovery Project Teams
Enrich Simple Drawn Compounds with Calculated Properties
Transform how chemists interact with their data
Transform how tools are delivered to the desktop
Transform how IT builds and supports applications
Two Clicks
Equally easy access to the same calculations from other familiar applications

Model Usage is Growing…
Compounds registered as ‘GENERAL_SCREENING’ excluded from analysis

Resulting in Higher Quality Compounds!
Descriptor Function X1 X2 X3 X4
QSAR_CLint_rat_hepatocyte Decreasing 45 100
QSAR_CLint_human_hepatocyte Decreasing 25 60
QSAR_Clearance_rat Decreasing 15 35
ClogD_pH_7.4 Hump Function 1.5 23 3 3.5
Polar_Surface Hump Function 65 75 125 140
Molecular_Weight Hump Function 420 475 530 580
Courtesy: Kerim Babaoglu
Multiparameter Optimization (MPO) Analysis Drives Design of More Desirable Compounds
More Desirable Compounds Display Lower (Better) Human Dose Calculations
(Scaled from Experimental Rat PK Data)
Design/Synthesis Cycle
DesirabilityScore
Legend:
Green = Good Dose
Yellow = Moderate Dose
Red = Poor Dose

And, Decreased Lead Optimization Cycle Times

Execution
Service
(AEP Runner)
JobsXMLDB
AEP Cluster
Runner
(Predict)
AEP Grid
(Build/Learn)
AEP Grid
(Build/Learn)
AEP Grid
Runner
(Build/Learn)
Publication Service
Checks new models for
validating, complete
metadata and assigns
identity. At the mid-term,
this service is embedded in
QSAR workbench only.
Execution Service
Launches job requests on
appropriate infrastructure.
This service is provided out-
of-the-box by AEP.
GEMS
Information Service
Returns a listing of published
services (models) the user is
allowed to see and run
QSAR Workbench ALDaS Insight / ADMET Workbench
SOAP/HTTP
SOAP / HTTP
ODBC /
JDBC
AEP standard functionality
Logical Architecture Overview
Information
Service
Publication
Services
Service
Metadata DB
PSN Project
Other Service Consumers

-
Extensible and Leveragable Informatics Platform
A Service Oriented Architecture (SOA) That Invokes Connectivity
Sharepoint (one.merck.com/cheminfo)Translational Solutions Architecture
Get Me The Data What Do I Make Next? Now, Help Me Make It
Sharepoint (one.merck.com/cheminfo)Service Oriented Architecture Framework for Reusable Services Development
Sharepoint (one.merck.com/cheminfo)Merck Master Data and Data Architecture
Sharepoint (one.merck.com/cheminfo)Transactional Applications and Data Repositories
Lead
Identification
Lead
Optimization
Preclinical
Candidate to
First in Human
First in Human
to
Phase 2B
Phase 3
to
File
Pre-Lead
Optimization
Lead
Optimization
Early
Development
PCC Phase IIb
Chemical Biology
(chemical probes
predict targets)
Systems Biology
(off target activity
prediction)
Clinical Trials
(ADMET predictions)
Chemical Pharmacology
(toxicity predictions)
Sharepoint (one.merck.com/cheminfo)
Local (Project Team) QSAR
Models
Ligand-based Design
Support
Structure-based Design
Support
Hit Lead

Drug Protein Target
Response
interacts
with
and elicits a
What is the Scope of Scientific Modeling?
distributes to
site of action
through a
in
System
IndividualsPopulations
Pathway
in a
within
that respond to
Each arrow represents an
opportunity to develop and utilize
a predictive model in lieu of more
resource and time-consuming
experimentation!

The Modeling and Simulation Landscape
Drug Protein Target ResponseSystem Individuals PopulationsPathway
A wide variety of solution providers…
NONMEM®
…incorporating a wide vide variety of technologies.
Note: Illustrative Purposes Only
QSAR Workbench ModSpace
NavigatorInsight Analytics
GastroPlus
DDDPlus
ADMET Simulator
Phoenix
WinNonlin
SimCyp
Trial Simulator
Life Sciences
Data Hub
Foundation / PLP
Derek Nexus
…offering a wide variety of tools…
DILIsym

Data ingestion
transformation
DATA
Data integration
Warehousing
Data stores
Authoritative
Repositories
Client
tools
PRESENTA
TION
LOGIC
Data Access; Infrastructure Access (HPC); Access control
Private
Services
Domain
Users
Data Sources
Shared
Services
Scientific Information Management
Data
Delivery
Service
Data Platform
Data Mart or
View
Data Mart or
View
Data Mart or
View
Data Mart or
View
D360
ChemCart Scientific Information Platform API
Scientific Information Platform API
Scientific Information Common Data Model
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
Data
Ingestion
Service

Model (Lifecycle) Management
Model ingestion
transformation
MODELS
Model integration
Warehousing
Model stores
Authoritative
Repositories
Client
tools
PRESENTA
TION
LOGIC
Model Access; Infrastructure Access (HPC); Access control
Private
Services
Domain
Users
Model Sources
Shared
Services
Model Repository
File
System
Document
s
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
MLM
Service
MLM
Service
MLM
Service
MLM
Service
MLM
Service
D360
ChemCart
ADMET
Workbench
WebModel Mobile
Apps
Scientific Modeling Platform API
Scientific Modeling Platform API
Scientific Modeling Common Data Model
Model
Execution
Service
Model
Information
Service
Results
Presentation
Service

Pharmaceutical Product Lifecycle Management

Product Lifecycle Management
Develop
Realize
UseConceive

Pharma Product Lifecycle Management
Conceive Develop Realize Use
Data
(Internal and
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)

Data
(Internal and
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

Data
(Internal and
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
Cross-domain Workflows…

Data
(Internal and
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?

Level 4Level 3Level 2Level 1
Current State
EDDS
Data
EDDS
Models
PCD
Data
PCD
Models
Clinical
Data
Clinical
Models
Real
World
Data
Real
World
Models
Discovery Pre-clinical Clinical Real World
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

Future State
EDDS
Data
EDDS
Models
PCD
Data
PCD
Models
Clinical
Data
Clinical
Models
Real
World
Data
Real
World
Models
Discovery Pre-clinical Clinical Real World
Barriers* between functional domains are eliminated and data, models, and
knowledge are used holistically to advance the most promising entities.
*Cultural, Behavioral, and Technical
Data Models
Integration Layer
Delivery Layer
End User Experience Layer
Merck Scientific
Modeling Platform
Merck Information
Management Platform
Nirvana

We are able to predict success.
The Vision: Failure Rates Decreasing at All Stages of R&D
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Building a Culture of Model-driven Drug Discovery at Merck

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