Building a Culture of Model-Driven Drug DiscoveryChris 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

Now, the news…

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.

$2,870 millionCAGR = 8.5%

longer than a decade

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 – http://www.forbes.com/sites/bernardmunos/2015/01/02/the-fda-approvals-of-2014/

The productivity crisis in pharmaceutical R&DFabio Pammolli, Laura Magazzini & Massimo RiccaboniNature 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?

TransformDeliver

AggregateAccess

Drug Protein Target Response

Answers, answers, answers…

System Individuals PopulationsPathway

Research Development Commercial Medical

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 CaptureAutomate 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 TeamsEnrich Simple Drawn Compounds with Calculated Properties

Transform how chemists interact with their dataTransform how tools are delivered to the desktopTransform 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

Des

irabi

lity

Sco

re

Legend:Green = Good DoseYellow = Moderate DoseRed = 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 ServiceChecks new models for validating, complete metadata and assigns identity. At the mid-term, this service is embedded in QSAR workbench only.

Execution ServiceLaunches job requests on appropriate infrastructure. This service is provided out-of-the-box by AEP.

GEMS

Information ServiceReturns 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 PlatformA 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 DevelopmentSharepoint (one.merck.com/cheminfo)Merck Master Data and Data ArchitectureSharepoint (one.merck.com/cheminfo)Transactional Applications and Data

Repositories

LeadIdentification

Lead Optimization

Preclinical Candidate to First in Human

First in Human toPhase 2B

Phase 3 to File

Pre-LeadOptimization

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

Sharepoint (one.merck.com/cheminfo)Ligand-based Design Support

Sharepoint (one.merck.com/cheminfo)Structure-based Design Support

Hit Lead

Scientific Modeling Platforms

Drug Protein Target

Response

interacts with

and elicits a

What is the Scope of Scientific Modeling?

distributes to site of actionthrough 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

Research Development Commercial Medical

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 WinNonlinSimCyp

Trial Simulator

Life Sciences Data Hub

Foundation / PLP

Derek Nexus

…offering a wide variety of tools…

DILIsym

Parallel Transformations

Data ingestiontransformation

DATA

Data integrationWarehousing

Data stores

AuthoritativeRepositories

Client tools

PRES

ENTA

TION

LOGI

C

Data Access; Infrastructure Access (HPC); Access control

PrivateServices

DomainUsers

Data Sources

SharedServices

Scientific Information ManagementResearch Development Commercial Medical

Data Delivery Service

Data Platform

Data Mart or View

Data Mart or View

Data Mart or View

Data Mart or View

Note: Illustrative Purposes Only

D360ChemCart Scientific Information Platform API

Scientific Information Platform API

Scientific Information Common Data Model

Transactional DB

Transactional DB

Transactional DB

Transactional DBData

IngestionService

Model (Lifecycle) Management

Model ingestiontransformation

MOD

ELS

Model integrationWarehousing

Model stores

AuthoritativeRepositories

Client tools

PRES

ENTA

TION

LOGI

C

Model Access; Infrastructure Access (HPC); Access control

PrivateServices

DomainUsers

Model Sources

SharedServices

Model Repository

Note: Illustrative Purposes Only

File System

DocumentsTransactional DB

Transactional DB

Transactional DB

MLMService

MLMService

MLMService

MLMService

MLMService

Research Development Commercial Medical

D360ChemCart

ADMETWorkbench

WebModel MobileApps

Scientific Modeling Platform API

Scientific Modeling Platform API

Scientific Modeling Common Data Model

ModelExecutionService

ModelInformation

Service

ResultsPresentation

Service

Pharmaceutical Product Lifecycle Management

Product Lifecycle Management

DevelopRealize

UseConceive

Drug Protein Target Response

Pharma Product Lifecycle Management

System Individuals PopulationsPathway

Conceive Develop Realize Use

Research Development Commercial Medical

Data(Internal and

External, Structured and Unstructured)

Models and Simulations

(Data)

Workflows (Best Practices)

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…

DesignMeasure

Analyze

ImproveControl

DesignMeasure

Analyze

ImproveControl

DesignMeasure

Analyze

ImproveControl

DesignMeasure

Analyze

ImproveControl

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 Workflows…

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?

The Plan

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 solutionsProcess: Siloed workflows

People: Siloed thinkingRoot 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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Thank you!