Advanced Analytic Concepts: A Gambler’s Guide to the Drug Discovery, Development & Commercial Universe

John MurphyPRISM Forum SIG –Tuesday 19th October 2010

Using clinical data and analytic methods to improve our understanding of disease-state and therapeutic effect

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Have you ever thought how much drug development is similar to gambling in Vegas?

A skilled Gambler uses advancedanalytic strategies against the house.Drug developers should start doing thesame!

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The MIT blackjack team won millions by devising an analytic system to reduce the odds.

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Probabilistic analytic models have evenallowed gamblers to reduce the odds of winning Roulette

Measure the position and velocity of the roulette ball at a fixed time and you can then predict its future path, including when and where the ball will spiral into the rotor. (The rotor is the spinning circular central disc where the ball finally comes to rest in numbered pockets.) Also measure the rotor’s position and velocity at a (possibly different) fixed time and you can predict the rotor’s rotation for any future time. But then you will know what section of the rotor will be there when the ball arrives so you can know (approximately) what number will come up!

Understand the physics & probability becomes more certain

The Role of Model-Based Drug Discovery,Development and Commercialization (MBDD)

–Optimize clinical development programs to maximize therapeutic potential, R&D productivity and commercial value

• Drug treatment in a competitive environment– Dose, frequency, route, duration, comparator

• Patient population for the trial, and the market– Inclusion/exclusion, number per arm, adaptive allocation

• Endpoints– Which endpoints, when measured, surrogates, biomarkers

–Optimize clinical trial design and analysis–Support go/no-go decisions

MBDD is a rigorous, quantitative and accountable set of approaches to improve drug discovery, development and commercial strategy and decision-making.

Examples:

Each drug lead should be modeled on knownscience, disease state, therapeutic state.population, market…….through commercial value

Every development decision is ultimately a decision about value. The overriding question is always, how to improve probability and best to spend development budget and time to get regulatory approval for the most valuable label possible?

The Value Function:

To maximize value and reduce risk should we

• Proceed to the next phase of development ?• Stop development altogether ?• Continue to gather data in the current phase ?

To maximize value and reduce risk should we

• Proceed to the next phase of development ?• Stop development altogether ?• Continue to gather data in the current phase ?

Program Value =

- Development Costs

- Time to Market Costs

+ Value of Approved Label

The Decision:

PatientsRegulatorsPayers

PerformanceDemands

Therapeutic Priorities

MarketplaceGrowth

InitialMarketplace

Impact

Modeling and advanced analytics canbe used across the discovery & development continuum.

Timeline

Physicians

Priority Assets

Clinical Trial Results

Product Launch

Preliminary Therapeutic Positioning

Market Opportunity

Clinical Program Design

Brand Strategy

Brand Tactics & Execution

Asset Inventory

Performance Optimization

Lifecycle Mgmt

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Performance OptimizationGo-to-Market StrategyEvidence Plan

OptimizationAsset Strategy

MBDD Applies Across the scientificEnterprise

–MBDD methods to help teams with development decisions in all therapeutic areas at every point along the discovery & development timeline.

Preclinical Phases I/IIa Phase IIb Phases III/IV

Is there a clinical trial design that will show PoC and find the best dose?

Is it worth developing a new dosage form?

Should we continue this development program?

What is the optimal patient population for this drug?

Which indication should we go into first to maximize the value of the program?

Is this treatment likely to be as good as the competition?

Should we in-license this compound?

What candidate should we take forward to human clinical trials?

What is the probable clinical dose-response in humans?

What’s the best dose and schedule?

What dose provides the best benefit/risk profile?

What is an optimal regulatory strategy?

What are additional indications?

What are the most important attributes of a 2nd generation compound?

time

How do we demonstrate efficacy?

Have we demonstrated improved benefit/risk compared to standard of care?

Clinical Analytics Provides Systematic, Data Driven, Empirical Methods that reduce probability ofFailure Strategic Financial and Scientific Business Decisions are all too often made with incomplete and anecdotal information. Clinical Analytics employs standardized, systematic, data-driven methodologies to rationalize decision making, reduce risk and improve Return on Investment.

• Drug Models

• Disease Models

• Compliance Models

• Drop-out Models

• Risk-Value Models

Different Kinds of Analytic Decision Models

• Trial Models

• Competitor Models

• Commercial Models

• Portfolio Models

• Therapeutic Models

• Investment Models

Clinical Trial Data

Scientific Literature

Is there a clinical trial design that will show PoC and find the best dose?

Is it worth developing a new dosage form?

Should we continue this development program?

What is the optimal patient population for this drug?

What is an optimal regulatory strategy?

PatientEMR Data

Competitor

Label

Information

Marketing Study

Portfolio Data

CorporateFinancials

Clinical Procedure &

Outcome

Cost –EffectCost-BenefitComparative Performance

QuintilesData

LeveragedExternal Data

SourcesProduct Differentiation,CompetitiveConsultingAdvantageLeverage PriorRelationships

Should we invest in this lead?

What is our Portfolio Risk & Where Should We Place our financial bets?

What is the Evidence-basis for comparative effect, safety, value…?

At Quintiles we built a Data Factory as the Foundation for our Modeling

Data Presentation

Data Standards & Policies

Patient Centric Systems

Lab

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External Data Providers

Industry Standards and Regulations

Quintiles Data Council

nterprise Data Council Data Strategy

Policies Standards Quality Alliances

Enterprise Data Council Data Strategy

Policies Standards Quality Alliances

Quintiles

Client Instructions & Context

Next Gen Informatics

B2B Interfaces

Regulatory Interfaces

Standards-BasedInterfaces

Data Factory

Internal & External Ops and Clinical Data

• Data Mining• Predictive Modeling• Causal Analytics

Analytics & Business Insights

Client 1

Client 2

Client 3

Client 1

Client 2

Client 3

The Quintiles DataFactory housesover 10,000 clinicaltrials spanning 28years.

To make it usefulfor MBDD weneeded to integrateexternal datasources.

Data Services Layer

And, it required integration of internaland external data sources

Staging Area

Data Management LayerData Integration Layer

Q Operational Systems

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External Interfaces

ECG

Target System Data

Synchronization

ETL

Safety

Canonical Data Model Synchronization

DataProfiling &

Standardizing

Master Data Repository

ClinicalTrials.gov

eCRF Provider

IVRSProvider

QLIMS

Projects

Protocols

Sites

Countries

Customers

Theurapedic Areas

Regions

Staff Roles

Investigators

Golden Master Record Creation

Subject

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SF.Com

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Siebel CTMS InnTrax

TRIO P‘Soft HR

QRPM P‘Soft Fin

Subj Visit Schedule

Clinical Data

Repository

Starlims

Trial Mgmt OperationsData Store

Data Factory

Q Operational Systems

ECG

SafetyQLIMS

SF.Com

Siebel CTMS InnTrax

TRIO P‘Soft HR

QRPM P‘Soft Fin

Starlims

Shared Data Svcs

Materialized Views

Data Services Layer

Clinical Data Repository BlueprintSupport for Subject Data Review

Staging Area

Data Management LayerData Integration Layer

Q Operational Systems

Metadata Management Study Metadata Repository

Clinical Data Repository

Master Data Entities

StudySites

Protocols

Investigators

Study Subjects

Master Data Repository

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QLIMSStaging

Database

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QLIMSQLIMS

QLIMS

QLIMSQLIMS

External Interfaces

eCRFData

Lab ResultsData

eCRF Data Store

Master Data

Study Data Model (SDM)

ECG

IVRSProvider

ECG Results

Data

IVRSData

Data Presentation Layer

CDD Views

Ad-Hoc Query Views

SDRT Application

SDRT Listings & Reports

Submissions Data Feed

SDTM-compliant Feed

Cognos Framework Svcs

SDTM XML/SAS Data Set

Cognos Framework Svcs

Materialized Views

Safety

Data Provisioning is Event-based or Scheduled

Central QueryRepository

Oracle BPM Svcs

Query Reconciliation Workflow

Data forProfiling &

Standardizing

ECGResults Data

Store

IVRSData Store

Lab ResultsData Store

SafetyData

Starlims

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Support SystemsProject Centric Systems

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(Selective) Data CleansingETL (Extract, Transform, Load)

Database Administration (data model derivation, tuning, implementation)

• Common Framework• Centralizes tools• AI Neural Nets• Project & data scheduling• Query deconfliction• Refresh rate alignment• Resource optimization• Cost containment• Actionable data • Collaboration

• Drives analytic culture

A Playground for our scientists and clinicians tocollaborate with our partner drug companies

Sandbox Operations

• Players• R&D• Commercial• ETU• ATP

• EBS• Consulting

Analytical Sandbox(Owned & Managed by Shared

Expertise)

Federation ofHealth SystemRepositories

PharmaceuticalMedical Device &Medical ResearchStudy Databases

LongitudinalCo-mingled

Clinical Data

Medical &ScientificOntology

Repository Data FeedsExternal Data & Knowledge

Sources

Disease-stateModeling &

Learning System

TherapeuticModeling &

Learning System

PatientModeling &

Selection System

A clinical system and sustainable business models for successful & profitable operation of a state-wide health information network will be used to support a wide variety of evidence-based research.

The system will connecthospitals, physicians, patients and other providersof care with researchscientists and be used bymember institutions to comply with HITECH oncedeveloped.

Partnership to accelerate clinical electronic research(PACeR): Advanced Analytic Methods

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Neural Modeling begins with trainingsets that define what is known

– Neo- Menarche Pregnancy Lactation Peri Menopause Post– natal Menop Menop

– Tissues Cells Organelles

–Processes: Tissue generation; Inflammation….

– Pathways

– Enzymes Substrates Co-Factors

– Proteins

– Genes

– DNA RNA Amino Acids

–O

nto

log

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–Physiological SystemsPatient Variation

Longitudinally Model Aging for example

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A Neural Model maps acrossdimension

– Tissues Cells Organelles –Processes: Tissue generation;

Inflammation….

– Pathways

– Enzymes Substrates Co-Factors

– Proteins

– Genes

–O

nto

log

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– Physiological Development

–

Dis

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– P

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– (time)

–(t

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–Physiological Systems

– DNA RNA Amino Acids

Training Set for Analysis of Disease

Disease Definition: By ICD and subclassification Genomic definition

EmbyrologyPhysiologyPathophysiologyDifferential Diagnosis…

Diagnostic Evaluation: HistoryPhysical ExamDiagnostic Test Results

Therapeutic Regimen: DietMedications: Effects, side-effects, AERS, Contraindications…Plan of care

Neural Network analyzes Homoassociative findings (conformity totraining set), Heteroassociative findings (non-conforming patterns implying new knowledge i.e. common sub-group SNP), Chaotic finding(pattern in large population appearing random).

Actuarial Diagnosis Occurs as a result of continued monitoring ofclinical data against Diseases Defined in Training Sets.

Automated Clinical Analytics for Actuarial Confirmation of Disease States, AERS, Evidence-based Medicine…

Disease training sets provide as complete a

view of each disease as is currently

known. Links to Internal & External Data Sources

Maintains Currency& Accuracy

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Autoassociative, Heteroassociative & Mandelbrot (chaos) Pattern Association across disease state

Clinical

Values

Individual

Patients

Clinical Variables

Gaussian Mathematics Parzen WindowsProbabilistic Densities

HANYS PACeR

Partnership to AdvanceClinical electronic Research

Office of the National Coordinator (ONC)

A partnership between the clinical research community and New York-based academic research centers, hospitals & physician groups to build an advanced, on-line, clinical research capability

Establish a New York state-wide collaborative clinical electronic enterprise that connects hospitals, physicians, patients, clinical researchers, pharmaceutical and device manufacturers, and insurance carriers for the purposes of:

1. Modernizing clinical trials & clinical research

2. Improving drug & device safety

3. Improving prescription compliance

4. Improve patient outcomes & quality of life

5. Providing cost-effective & evidence-based clinical decision support

6. Provide advanced clinical modeling capabilities for a better understanding of disease, therapeutic option, quality, outcome & cost of care

PACeR Goal Statement

• R&D Incubator• Teaching• Research• Curation• Policy• Management• Research-Portal• Communication

Patient Home Physician

Allied Health

Institution

Industry

PACeR For-Profit Partner BusinessesData Network

Social Network

Business Network

PACeR-NEW YORKCenter for Clinical Analytics & The Advancement of Clinical Knowledge

PACeR Conceptual Schematic

Population Modeling

Therapeutic Modeling

Disease-State Modeling

Compliance

Quality

Safety

Device Trials

Outcomes

Clinical Trials

Economics

Education

Dat

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ata Sell/D

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Public Data

Patient Homes

Pharmacy

Nursing Homes

Physicians

Hospitals

Board of DirectorsUniversities PatientsCompaniesHospitals

Data Network

Founding Members

HANYS

Pfizer

J&J

Merck

Quintiles

Bayer

Oracle

Roche

Participating Hospitals

Albany Medical Center

Roswell Park

SUNY Stony Brook

Westchester Medical Center

University of Rochester Medical Center

Bassett Medical Center

Continuum Health Partners (St. Luke’s and Beth Israel)

SUNY Upstate

New York Hospital Queens

North Shore/LIJ

NYU/Langone Medical Center

SUNY Downstate

Advisors/Observers

FDA

NHIT

NIH

NY eHealth Collaboration

RHIOs

ONCHIT

CDISC

Hudson Highland Advisors

Hastings Institute

Current PACeR Participants

The PACeR team continues to recruit new participants, including biopharma and medical device companies, HIT vendors, insurers, advisors, and others

The PACeR Business: Year 1

Customers(Demand)

Applications

Infrastructure

Franchises (Supply)

Data / Information

Trial Modeling• Disease• Therapeutic• Population

PACeR Clinical Sciences

Patient Selection

Query Engine / Data

Facilitator Patient Portal

Physician Social

Network

Franchisees (Institutional Sellers)

Hospital / EMR Patient Physician

Pharmaceutical Purchasers Pharma customers leverage PACeR Clinical Science to

access information

PACeR Clinical Sciences facilitates access to unique data via its applications and

franchisees

Franchisees operate independently and build

data sets to answer customer requests

Money ultimately flows down through the system, and data/information up to

customers

PACeR Clinical Sciences is a for-profit corporation owned

by investors

Protocol Validation

Safety / Compliance

The PACeR Business: Long Term

Customers(Demand)

Applications

Infrastructure

Franchises (Supply)

Data / Information

Clinical Trials• Modeling• Patient Selection

PACeR Clinical Sciences

Query Engine / Data

Facilitator Patient Portal

Physician Social

Network

Franchisees (Institutional Sellers)

Hospital / EMR Patient Physician

Pharma InsurersOther (Gov’t,

Consumer Products)

Numerous customers leverage PACeR Clinical

Science to access information

PACeR Clinical Sciences facilitates access to unique data via its applications and

franchisees

Franchisees operate independently and build

data sets to answer customer requests

Money ultimately flows down through the system, and data/information up to

customers

PACeR Clinical Sciences is a for-profit corporation owned

by investors

PACeR phase I II III

Evidence Base• Benefit Design• Risk Management

Safety Sciences• …Adherence• …Phase-IV……..

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