9th Annual SAS Health Care & Life Sciences Executive Conference

Transcription

1 : An Integrated Platform to Promote Data Exchange, Collaboration, and Advanced Analytics 9th Annual SAS Health Care & Life Sciences Executive Conference 9-10 May 2012 Bernd Doetzkies, Director Informatics Daiichi Sankyo Pharma Development 1

2 Data on Demand & Collaboration Clinical Data Repository Advanced Analytics Modeling & Simulation Platform EDC & IxRS Data Next Steps & Conclusions 2

3 Data on Demand 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 3

4 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) 4

5 Data on Demand Data Capture Data Storage Centralized Repository Regulatory Compliant Secure Data Transfers Database Security Scalable CDISC Data Standards (STDM & ADaM) 5

6 Data on Demand Data Capture Storage Data Retrieval 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) 6

7 Data on Demand Data Capture Storage Retrieval Analysis Validated System for Statistical Analysis Environment to Manage Analysis Output Integrate Other Analytical Tools 7

8 Data on Demand Storage Data Capture Retrieval Analysis Reporting Validated System for Generating Tables, Listings, Figures (TLFs) CDISC Data Standards (ADaM) Environment to Manage TLF Output Provide Secure Access to TLF Output 8

9 Daiichi Sankyo - Data on Demand Patient Diary Data Lab Data PK Data ECG Data EDC Data Daiichi Sankyo Clinical Data Repository Daiichi Sankyo CRO Consultants Phase I IV Clinical Trials Data Reviews Safety Reviews Medical Coding Statistical Analysis Tables Listings Figures Advanced Analytics 9

10 Daiichi Sankyo Clinical Data Repository Storage Data Capture Retrieval Analysis Reporting SAS Drug Development d-wise Reveal ICS JReview SAS Solutions OnDemand Cerner-Galt dsnavigator 10

11 CDR Business Benefits Advanced Analytics Rapid Decision Making Global On-Demand Access to Information Accelerated, Accurate Data Flows, Reviews, & Analyses Promote Data Standards & Facilitate Data Integration Technology Transparent to Users Data Exchange with Multiple Data & Service Providers Regulatory Compliant Environment 11

12 Advanced Analytics 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. 12

13 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 13

14 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 14

15 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 15

16 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 16

17 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 17

18 Advanced Analytics Clinical Operations Monitor actual performance against recruitment plans and model adjustments and rescue strategies Dashboard capabilities to quickly identify underperforming 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 18

19 Advanced Analytics Biostatistics Clinical Trial Simulations Adaptive Trial Designs Predictive Modeling Power Analysis & Sample Size Estimation Classification & Regression Trees Multivariate Adaptive Regression Meta Analysis 19

20 Modeling & Simulation Platform 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 20

21 Modeling & Simulation Platform M&SP Architecture M&SP Portal & Dashboard M&SP Workflows Data Repository M&SP Work Bench M&SP Meta Data DB 21

22 Modeling & Simulation Platform Architecture Data Repository Federated or Distributed Data Repository Data Mart Operational & External Systems Extract / Transform / Load (ETL) Data Mart ETL Analysis Datasets Analysis Datasets 22

23 Modeling & Simulation Platform Architecture M&S Work Bench M&S Meta Data DB Data Repository Work Bench and Meta Data DB Virtual Machine (VM) Virtual Machine (VM) Virtual Machine (VM) Virtual Machine (VM) Data Repository Virtual Machine (VM) ETL M&SP Work Space M&S Workflows Meta Data DB 23

24 Modeling & Simulation Platform Architecture 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 Publish Project Results and Objects in the Repository 24

25 Modeling & Simulation Platform Portal M&SP Meta Data DB Results M&S Dashboard Data Repository Clinical Trial Data Pre-Clinical Data CMC Publication Data ETL Analysis Datasets POP/PK-PD Analysis Adaptive Trial Design Trial Simulation Data Files & Programs Publish Analyses & Final Results Inputs Modeling & Simulation Workspace Traceability Outputs 25

26 Modeling & Simulation Platform Portal & Dashboard Project & Analytical Workflows Clinical Data Repository SAS Drug Development Base SAS SAS/STAT SAS/GRAPH SAS/IML Input Data & Save Results Modeling & Simulation Platform 14 applications from 11 vendors Population PK/PD Analysis Comp. / Non-Comp. Analysis Clinical Supplies Planning Adaptive Trial Design Sample Size/Power Calculations Statistical Analysis Iterative Extract, Analysis, Review, Publish Processes 26

27 M&SP Business Benefits Increase Transparency & Traceability to Analytical Processes Efficiently Support Complex & Iterative Analytical Processes Improved Study Conduct and Planning Processes Global Access to Analytics & Results Knowledge Base Throughout the Lifecycle of Projects Ensure Current & Authoritative Sources of Data are Used Enable Users to Focus on Analytics & Not Programming Quickly Adapt to Evolving Sciences & Technologies 27

28 EDC & IxRS Data 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 28

29 EDC & IxRS Data Phase I EDC Systems CDR System A B C CDI SDD JReview 1) EDC Study Build CDI Data Mapping ) Data on Demand in SDD and JR Web Service 2) CDI Generates SAS Programs to Create CDASH and SDTM Datasets SAS 4 4) SAS Programs Create CDASH and SDTM Datasets in SDD 3 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. 29

30 EDC & IxRS Data Phase I IxRS CDR System M&SP A B SDD CT-FAST 2 Web Service 1 SAS 2) CSO users update Clinical Supply models and save results 1) SAS PROC SOAP is used to call IxRS Web Service and extract XML file which is loaded into SDD M&SP project folder 30

31 Next Steps 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 31

32 Conclusions 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 32

33 Backup Slides 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 These PowerPoint slides are the intellectual property of Daiichi Sankyo, Inc. and are protected under the copyright laws of the United States of America and other countries. Used by permission. All rights reserved. 33