9th annual sas health care & life sciences executive ......modeling & simulation platform 14...

Copyright 2012 Daiichi Sankyo, Inc. 1

9-10 May 2012

IxRS & EDC to CDR & MSP: An Integrated Platform to Promote Data Exchange, Collaboration, and Advanced Analytics

Bernd Doetzkies, Director Informatics Daiichi Sankyo Pharma Development

9th Annual SAS Health Care & Life Sciences Executive Conference


IxRS & EDC to CDR & MSP

• Data on Demand & Collaboration

• Clinical Data Repository

• Advanced Analytics

• Modeling & Simulation Platform

• EDC & IxRS Data

• Next Steps & Conclusions



• On-demand access to information for rapid decision making

• Accelerated, accurate data flows, reviews, analyses, and outputs in a regulatory compliant environment

• Technology that is transparent to the users

• Globally accessible

Data on Demand


IxRS & EDC to CDR & MSP Data on Demand

Data Capture

Integrate Data from Systems Used to Collect Clinical Trial Data Electronic Data Capture (EDC) Laboratory Data Pharmacokinetic Data ECG Data CRO Clinical Data Management Systems (paper-based studies)



Centralized Repository Regulatory Compliant Secure Data Transfers Database Security Scalable CDISC Data Standards (STDM & ADaM)

Data Capture Data Storage



Retrieve Data in a Structured Manner Support On-going Data Review Non-programmer Access to Data Access to Meta Data Database Locking Medical Coding Ability to pool data across studies

(STDM & ADaM)

Storage Data Capture Data Retrieval



Validated System for Statistical Analysis Environment to Manage Analysis Output Integrate Other Analytical Tools

Storage Data Capture Retrieval Analysis



Validated System for Generating Tables, Listings, Figures (TLFs) CDISC Data Standards (ADaM) Environment to Manage TLF Output Provide Secure Access to TLF Output

Storage Data Capture Retrieval Analysis Reporting


Daiichi Sankyo - Data on Demand

Phase I – IV Clinical Trials

Daiichi Sankyo Clinical Data Repository

CRO

EDC Data

Patient Diary Data Lab Data PK Data ECG Data

Consultants

Daiichi Sankyo

Data Reviews Safety Reviews Medical Coding

Statistical Analysis Tables Listings Figures

Advanced Analytics


Daiichi Sankyo Clinical Data Repository

SAS Solutions OnDemand

Cerner-Galt dsNavigator ™

d-Wise Reveal ©

ICS JReview ®

SAS Drug Development ®

Storage Data Capture Retrieval Analysis Reporting


CDR Business Benefits

Promote Data Standards &

Facilitate Data Integration

Accelerated, Accurate Data

Flows, Reviews, & Analyses

Regulatory Compliant

Environment

Technology Transparent to

Users

Data Exchange with Multiple

Data & Service Providers

Advanced Analytics

Rapid Decision Making

Global On-Demand Access to Information



Model-based drug development is a strategy advocated by the FDA as a part of the Critical Path Initiative

FDA Guidance for Industry: End-of-Phase 2A Meetings

• “FDA Recognizes trial planning may be improved by clinical trial simulations that employ quantitative models of drug exposure-response, effects in placebo group, and disease progression.”

Advanced Analytics



Translational Medicine & Clinical Pharmacology • FDA has accepted Pharmacokinetic /

Parmacodynamic (PK/PD) modeling and simulation analyses as part of NDA submissions

• Support for dose selections • Identification of at-risk patient populations • FDA has accepted labeling changes without

executing clinical trials • Most widely accepted M&S application in Pharma

Advanced Analytics



Clinical Development • Disease State Modeling (Diabetes, CV - QT) • Clinical Study Simulations to quantify the probability

of success • Sample size assumptions and distributions • Stopping rules • Non-compliance & drop outs • Inclusion / exclusion requirements • Clinical trial design comparisons

• Adaptive Trial Designs

Advanced Analytics



Clinical Supplies Operations • Applications available for developing and managing

Clinical Supply Plans

• Efficiently develop scenarios, perform sensitivity analyses, and develop contingency plans

• Incorporate Operations Research principles & goal seeking algorithms

• e.g., Optimize on drug availability and constraints on distribution costs

Advanced Analytics



Clinical Supplies Operations • Monitor actual performance against supply plans and

model adjustments and supply re-allocation plans

• Increase responsiveness to protocol changes and changes in recruitment strategies

• Key objective is to reduce the risk and cost of: • developing rescue strategies • out-of-stock situations • clinical supply overages • number of expired kits

Advanced Analytics



Clinical Operations • Applications available for developing and managing

Site Initiation and Subject Recruitment Plans

• Efficiently develop scenarios, perform sensitivity analyses, and develop contingency plans • Based on advanced algorithms & historical information

on regional performance

• Previous investigator/site performance can be added over time to refine Subject Recruitment plans

Advanced Analytics



Clinical Operations • Monitor actual performance against recruitment plans

and model adjustments and rescue strategies • Dashboard capabilities to quickly identify under-

performing and over-preforming regions and sites

• Manage recruitment plans and performance within and across studies

• Ideally the Site Initiation / Subject Recruitment and Clinical Supplies applications should be integrated

Advanced Analytics



Biostatistics • Clinical Trial Simulations

• Adaptive Trial Designs

• Predictive Modeling

• Power Analysis & Sample Size Estimation

• Classification & Regression Trees

• Multivariate Adaptive Regression

• Meta Analysis

Advanced Analytics



Requirements for Building a M&S Platform

• Provide Data on Demand

• Support a broad spectrum of users

• Technology that is transparent to the users

• Traceability & Transparency

• Balance structure with flexibility

• Support iterative analytical processes • Computationally intensive & challenging to manually track

• Regulatory Compliant

Modeling & Simulation Platform


IxRS & EDC to CDR & MSP Modeling & Simulation Platform

M&SP Portal & Dashboard

M&SP Workflows

Data Repository M&SP Work Bench

M&SP Meta Data DB

M&SP Architecture


IxRS & EDC to CDR & MSP Modeling & Simulation Platform Architecture

Operational & External Systems

Extract / Transform /

Load (ETL)

Data Mart

Analysis Datasets

Data Mart

Analysis Datasets

ETL

Federated or Distributed Data Repository

Data Repository



Meta Data DB

M&S Work Bench

M&S Meta Data DB Virtual Machine (VM)

Virtual Machine (VM)




M&SP Work Space

Data Repository

Data Repository

Work Bench and Meta Data DB

M&S Workflows

ETL



Publish Project Results and Objects in the Repository

M&S Workflows Register Known Data Sources in the Data Repository

Create Analysis Data Sets from the Repository (ETL)

Create Project and Associate Data with Applications

Update Project and Associate Data with Applications

Save Project Results and Objects in the Repository



Modeling & Simulation Workspace

Portal

Inputs

Data Repository

Clinical Trial Data Pre-Clinical Data

CMC Publication Data

Analysis Datasets

ETL

M&S Dashboard

POP/PK-PD Analysis

Adaptive Trial Design

Trial Simulation

Outputs

Files & Programs

Results

Data Publish

Analyses & Final

Results

Traceability

M&SP Meta Data DB



14 applications from 11 vendors

Modeling & Simulation Platform

SAS Drug Development ®

Base SAS ®

SAS/STAT ®

SAS/GRAPH ®

SAS/IML ®

Clinical Data Repository

Project & Analytical Workflows

Portal & Dashboard

Iterative Extract, Analysis, Review, Publish Processes

Input Data & Save Results

Population PK/PD Analysis Comp. / Non-Comp. Analysis

Clinical Supplies Planning Adaptive Trial Design

Sample Size/Power Calculations Statistical Analysis


M&SP Business Benefits

Efficiently Support

Complex & Iterative

Analytical Processes

Knowledge Base

Throughout the Lifecycle of

Projects

Quickly Adapt to Evolving Sciences &

Technologies

Ensure Current & Authoritative

Sources of Data are Used

Enable Users to Focus on

Analytics & Not Programming

Increase Transparency & Traceability to Analytical Processes

Improved Study Conduct and Planning

Processes

Global Access to Analytics & Results



• Accelerate data flow from EDC and IxRS applications • Pull data using CDISC ODM XML directly into SDD

• Facilitate data review process in the CDR • Create CDISC CDASH datasets in SDD for data review

• Streamline the process for managing IxRS data for Clinical Supplies Simulation & Forecasting

• Facilitate the process of CDISC standards compliance checking

• Create CDISC STDM and ADaM datasets within SDD • Increase quality & compliance to CDISC standards

• Reduce clinical trial outsourcing costs

EDC & IxRS Data


IxRS & EDC to CDR & MSP EDC & IxRS Data

EDC Systems

Web Service

CDR System

CDI ® SDD ®

1) EDC Study Build – CDI Data Mapping

SAS

2) CDI Generates SAS Programs to Create CDASH and SDTM Datasets

3) SAS PROC SOAP is used to call EDC Web Service and extract ODM XML file which is loaded into SDD and processed by the SAS program created with CDI.

1 2

4) SAS Programs Create CDASH and SDTM Datasets in SDD

3

4

JReview®

5

5) Data on Demand in SDD and JR

A B C

Phase I


IxRS & EDC to CDR & MSP EDC & IxRS Data

IxRS

Web Service

CDR System

SAS

1) SAS PROC SOAP is used to call IxRS Web Service and extract XML file which is loaded into SDD M&SP project folder

2) CSO users update Clinical Supply models and save results

1

2

CT-FAST ® A B

Phase I

SDD ®

M&SP



• Integrate OpenCDISC and SAS CDI® for comprehensive CDISC standards compliance checking

• Incorporate additional clinical data sources into the EDC to CDR ETL process to create all STDM datasets and ADaM datasets

• Extend M&SP with “On-Demand” Cloud computing resources

• Include additional Clinical Operations data and M&SP support

Next Steps



• Investing in & optimizing operational systems is not going to provide a competitive advantage particularly for those companies with an outsourced business model

• Competitive advantage will be to those companies that implement predictive analytics to optimize planning and project execution

• On-demand access to information for improved, rapid decision making

• Improve clinical trial design; scale back the number and/or size of clinical trials

• Minimize risks & maximize benefits to patients

• Reduce clinical trial costs & timelines

Conclusions



• SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

• JReview is a registered trademark of Integrated Clinical Systems, Inc.

• dsNavigator is a trademark of Cerner Galt, Inc.

• Reveal is copyrighted by d-Wise

• CT-Fast is a registered trademark of N-SIDE

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