Emerging Trends in Clinical Data Management


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Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.

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Emerging Trends in Clinical Data Management

  1. 1. Emerging Trends in Data Management A. V. Prabhakar, PhD Senior Manager, Clinical Data Management Dr. Arshad Mohammed Director, Clinical Data Management Disclaimer: The views in this presentation are of the authors and not necessarily of Quintiles
  2. 2. Thalidomide: Revived interest Thalidomide became infamous in 1960s as one of the biggest drug disasters About 10,000 children born deformed since their mothers used Thalidomide for morning sickness during pregnancy • 1998: FDA approved for treatment and Brazilian suppression of cutaneous manifestations of erythema nodosum leprosum (ENL). physicians • 2006: Accelerated approval for thalidomide (Thalomid, Celgene Corporation) in combination with dexamethasone for the treatment of newly Drug of choice diagnosed multiple myeloma for the • STEPS* program Since 1965 treatment of severe ENL FDA Approval 2 *System for Thalidomide Education and Prescribing Safety (S.T.E.P.S.) oversight program
  3. 3. Continued Industry Challenges Time and money in R&D • Drug R&D costs have rocketed 23 folds in last 28 years, touching an all time high of up to $1.25 billion per new molecular entity (NME). • Reducing patent protected market life as drug development time up from 11.6 years in 1970s to about 14 years Returns and Profits • Even with 20 years patent protection, some companies are unable to get their drug to market before the patent’s expiration date. R&D budgets falling and patent expiries looming: Urgent Priority • Optimizing the clinical trial process • Rationalize research pipelines Industry is examining alternative ways for brining drug to market • Relying on real time technologies including CTMS, EDC, Automation of processes, shrinking timelines especially start up and close out References: 1. Drug Discovery and Biotechnology Trends: Recent Developments in Drug Discovery : Improvements in Efficiency http://www.sciencemag.org/products/ddbt_0207_Final.dtl) 2. The productivity tiger - time and cost benefits of clinical drug development in India. (http://pharmalicensing.com/public/articles/view/1153412098_44bfac02291f1) 3
  4. 4. Current Scenario in Clinical Research Generation of Clinical Operations Clinical Data End Result for Data Management Clean Data Biopharmaceutical Industry Analyzed Biostatistics Data A Safe and Effective Medical Writing Clinical Study compound that can Report be marketed Regulatory Submissions team Submission 4
  5. 5. Emerging Scenario Advanced Meaningful Data Technology enabled Information (Asset) Transformation Maximizing asset value, data Impact on Bio-Pharmaceutical turned into information and used Industry • Creates better compounds before during after • Designs better study protocols • Makes faster go or no-go decisions a clinical trial program • Alters assessments on compounds in development Protocol design Adaptive design Meta analysis 5
  6. 6. Emerging Trends in CDM Clinical & DM Cross Accelerated Key Functional Collaboration Functional adoption of Collaboration EDC DM & BIOS Data Data Analytics Standards Data Integration Lab, ECG, IVRS, Cross Trial, Safety etc Across Programs 6 EDC Standards Integration Analytics Collaboration
  7. 7. Accelerated adoption of EDC “By 2012 the expected number of Despite its slow start, the use of EDC studies would be greater than EDC is on the rise at a rapid pace 70%” - By David Handelsman Quintiles Bangalore, Sept 10 With skyrocketing costs – up to $1.25 billion to bring a new drug to market, $500 - $700 million of which EDC can help to reduce the clinical is spent on clinical trials – companies research cost by ~20 – 28% are seeking faster access to cleaner clinical data Reference: “Effective Clinical Trial Monitoring Using EDC Metrics” , Appalla Venkataprabhakar, Data Basics – Spring 2009 7 EDC Standards Integration Analytics Collaboration
  8. 8. Advantages of using EDC • 25-30% savings realized using Overall Saving Time EDC from decreasing Improvement traditional monitoring / DDE budgets • EDC provides • PWC: Shift from paper to EDC better data will bring 35-50% reductions accuracy Time to DBL time & cost • Data could be • Cost savings alone with EDC standardization reduced by vs. Paper estimated about $60 • Centralized work 43% & million per drug flow number of • Real time study queries by results 86% • Low operations cost Saving Money References 1. EDC Advantage : Shrinking LPO-DBL Timelines in EDC Study”, Appalla Venkataprabhakar, Data Basics – Spring 2010 2. Achieving cost savings using EDC effectively ” ( 3. DATATRAK International Releases Value Proposition of EDC to the Pharmaceutical Industry - Part II (http://www.thefreelibrary.com/DATATRAK+International+Releases+Value+Proposition+of+EDC+to+the...-a078554673) 8 EDC Standards Integration Analytics Collaboration
  9. 9. Process and role changes • Sponsors looking at 5, 8, • Follow the sun • Sponsor 15 weeks, etc for start methodology in • CRO ups Database builds • Industry wide? • Global EDC testing hubs • Centralized UAT Crunched EDC Global EDC start up Global Libraries Build teams timelines • Protocol • DM: partial to total • Enhanced Project • CRF outsourcing (FSP) management skills • DMP documents, Edit • Outcomes based required Checks • Partner DM staff at • Metrics driven • UAT sponsor offices • Zero tolerance: Quality • Shared Risks and and Compliance Benefits • Project Reviews Technology for Partnerships of Management of Standardization next level CDM 9 EDC Standards Integration Analytics Collaboration
  10. 10. Scenario due to Lack of Data (Standard & Integration) Example of sophisticated review process of an FDA reviewer Reference 1.http://www.globalsubmit.com/home/LinkClick.aspx?fileticket=ta1z74CpCQw=&tabid=260. 10 EDC Standards Integration Analytics Collaboration
  11. 11. Data Standards Data Standards are agreed • Standardization helps improve efficiencies in trials upon set of rules that allow by reusability of tools & ability to combine data information to be shared and across clinical studies processed in uniform & • Data standards make inter department & inter consistent manner organizational collaboration possible • Lack of globally accepted pharmaceutical data Financial Impact formats believed to cost pharmaceutical industry in excess of US $ 156 million per annum • CDISC at the forefront of partnering with industry and defining standards Leading Organizations • HL7 is accepted messaging standard for communicating clinical data & supported by most major medical informatics system vendors Reference 1. Facilitating the use of CDISC standards in clinical trials “ – http://www.iptonline.com/articles/public/Formedix1.pdf) 11 EDC Standards Integration Analytics Collaboration
  12. 12. CDISC* Standards Table & Purpose Model / Standard Purpose XML specification supporting interchange of data, metadata Operational Data Model (ODM) or updates of both between clinical systems Clinical Data Acquisition Standards Data model for a core set of global data collection fields Harmonization (CDASH) (element name, definition, metadata) Submissions Data Tabulation Model Data model supporting the submission of data to the FDA (SDTM) including standard domains, variables, and rules Data model closely related to SDTM to support the statistical Analysis Dataset Models (ADaM) reviewer XML Specification to contain the metadata associated with a Define.xml clinical study for submission Standards for the Exchange of non- Data model extending SDTM to support the submission of clinical data (SEND) animal toxicity studies Metadata model focused on the characteristics of a study Protocol Representation Model (PRM) and the definition and association of activities within the protocols, including "arms" and "epochs". * Clinical Data Interchange Standards Consortium 12 EDC Standards Integration Analytics Collaboration
  13. 13. Data Integration • Bringing data from • Out of box • Expedites data multiple sources integrations cleaning & (IVRS, Diary, Lab, • Life Sciences reconciliation Randomization, Data Hub process Coding) eliminates • Enhancing patient redundant tasks like • IVRS & EDC integration safety reconciliation, same data entry into • EDC & Safety • Strengthening multiple systems integration quality • Accelerates flow of • Quintiles Data • Reduce the risk of critical information Factory data entry errors to key stakeholders • Quintiles white paper • Accelerating that aids faster for your reading timelines decisions Examples of Advantages of Integration Data Data Integration Integration 13 EDC Standards Integration Analytics Collaboration
  14. 14. Clinical Trial Data Integration Data Analytics and Online Reports Clinical CTMS Data Review Financials Clinical Trials EDC / RDC / CDMS Progress Review Data Exports, Regulatory Compliant PDF / HTML Reports Hand Held Device Data Integration & Reporting Environment IVRS Business Process Automation with Workflow Clinical Research Central Labs Organizations / Partners Courtesy: Oracle LSH presentation 14 EDC Standards Integration Analytics Collaboration
  15. 15. White Paper for your reading Published: September 2010 15 EDC Standards Integration Analytics Collaboration
  16. 16. Data Analytics in New Health Landscape Science of examining raw data with the purpose of drawing conclusions about that information • Make better cross functional business decisions - identify risks & mitigate them in timely manner e.g. need of additional trainings for staff at a site, fraud detection, signal detection, protocol deviations. • Greater transparency into the status of a clinical trial subject • Enhanced safety and efficacy monitoring via a holistic review of individual and aggregated subject data • Increased operational efficiency and quality made possible through a transparent and holistic view of data Benefits of Clinical Data Analytics 16 EDC Standards Integration Analytics Collaboration
  17. 17. Applications of Data Analytics Data Inconsistency Trend of Temperature vs. Visit 100 98 Temperature (C) 96 94 92 90 Visit-1 Visit-2 Visit-3 Visit-4 Visit-5 Trend of Height vs. Visit 200 195 190 185 Height (Cms) 180 175 170 165 160 Visit-1 Visit-2 Visit-3 Visit-4 Visit-5 17 EDC Standards Integration Analytics Collaboration
  18. 18. Applications of Data Analytics Data Trends & Outliers Discrepancies /Site compared to number of patients. Average time from patient admission to performing ECG 20 18 4 Discrepancies / patient 16 3 Average Time (hr) 3 12 10 10 7 1.5 6 2 8 1 1.2 0.9 4 1 0 0 Site-1 Site-2 Site-3 Site-4 Site-5 Site-1 Site-2 Site-3 Site-4 Site-5 Query Rate Across Sites Lag Time Between DE & Visit Date 30 25 20 15 Average Lag Time (Days) Queries / 100 eCRF Pages 25 16 20 12 15 10 9 12 8 5 4.2 6 5.4 8 10 4 5 0 0 Site-1 Site-2 Site-3 Site-4 Site-5 Site-1 Site-2 Site-3 Site-4 Site-5 18 EDC Standards Integration Analytics Collaboration
  19. 19. Advanced Analytics 19 EDC Standards Integration Analytics Collaboration
  20. 20. DM-BIOS Collaboration Till recently Resulted in lot of rework biostatisticians were on databases (including involved at later part of locked) for the study when the data was unidentified data errors available to them for final identified by statistical analysis biostatisticians Involvement of a Data errors identified so biostatistician from start late incur additional time, of the study significantly costs and annoyed helps the DM team avoid customer (internal / a lot of potential rework. external) 20 EDC Standards Integration Analytics Collaboration
  21. 21. Best Practice of DM-BIOS Collaboration During Start up During Conduct During Close Out Data Transfer & Non-CRF Interim transfers and early Kick off Meeting Data Guidelines Preparation BIOS feedback Completes data issues log & Protocol & CRF Preparation / Review of data at subsequent provides final copy of the Annotation intervals same to the data team lead. Early review of completed Ensure BIOS feedback CRF Edit Check Document Review Key factor for early DB Locks: Effective working relationship between DM & Bios 21 EDC Standards Integration Analytics Collaboration
  22. 22. Clinical-DM Collaboration Start-Up Phase Conduct Phase Close Out Phase Inputs during designing of CDM & BIOS inputs if SDV Weekly calls* between CDM CRF per study protocol < 100% and Clinical CDM should share the Clinical share Monitoring CRF completion guidelines status updates or Visit plan with DM dashboards - live CRF’s entered, Queries in Review of edit check Triggered Monitoring Visit open status, SDV, Freezing, document Locking, PI Signature etc DM should share milestone Start about 2 months before dates with Clinical the final DB lock Monthly calls* between CDM and Clinical * Discuss issues or updates related to data points / queries / site response / site training / milestones, etc 22 EDC Standards Integration Analytics Collaboration
  23. 23. Quintiles Case Study Therapeutic Area: Anti Go Live within 6 weeks Infective Indication: Platform: Inform Typhoid Fever Last Patient Last Visit- 4.5 Vaccine Database lock in 5 days TOP 5 in terms of study Duration of All major deliverables Patients: 329 performance on Quintiles Study: 1 year achieved before time Inform Dashboard Sites: 3 (All Sites in US) Loyalty Scores Project Management Customer Audit: No Start up: 96.7% critical or major findings Clinical Operations Close out: 96.7% Data Management Overall I am very impressed with the management of the project. The whole team has Lab been extremely accessible. Of particular note was the data management team in India who seemed to work around the clock on this study. - Clinical Operations Manager, BIOS Product Development, 23 EDC Standards Integration Analytics Collaboration
  24. 24. Emerging Trends in CDM Cross Accelerated Functional adoption of Collaboration EDC Data Data Analytics Standards Data Integration 24 EDC Standards Integration Analytics Collaboration
  25. 25. Thank you appalla.venkataprabhakar@quintiles.com arshad.mohammed@quintiles.com Quintiles CDM, Bangalore Disclaimer: The views in this presentation are of the authors and not necessarily of Quintiles