demands in clinical database build for the trials of...•7 sdtm domains created for submission of...
TRANSCRIPT
Demands in Clinical database build for the trials of tomorrow
Moovendhan Devaraj
Associate Manager – Statistical programming
Accenture Solutions private limited
TABLE OF CONTENTS
Known Story Short
Advancements in Clinical database build
Advancements to be adapted in Clinical database build - IoT
Advancements to be adapted in Clinical database build - mHealth
Conclusion
Thanks
Cloud
Paper CRF
Computers
Internet
Data Base
Integrated database
Standardization
Secured ConnectivityEasy accessibility
Known Story Short
➢ Advancements in Clinical database build
➢ Advancements to be adapted in Clinical database build
Advancements in Clinical database build
Machine interpretable
Protocol Final eCRF design
eCRF / EC build
TA Specific eCRF Standards
?
ProtocolDraft eCRF
designFinal eCRF design eCRF / EC build
Traditional Approach
Automated Approach
Do we need eCRF Design for CDB?
WHY?
• Time and Events schedule( TE )
• Subject Population in study
• Inclusion Exclusion Criteria
• Site Information
• Time Points for Sample Collection
• Any other specific Requirements For Protocol
AI tool, enabled with Machine
learning and NLP
Machine interpretable protocol
TA Specific standard libraries
Advancements to be adapted in Clinical database build - IoT
Adaptations in SDTM
• SDTM device Sub team formed in 2006
• 7 SDTM domains created for submission of data on devices
National Evaluation System for health Technology • Generate better evidence for medical device evaluation • Regulatory decision-making• Collaborating with medical device stakeholders
In 2018, the US FDA approved 106 IoT medical devices in comparison to 25 devices in 2009
Advantages
• Faster data collection
•Higher data quality
• Shorter trial span
•Near virtual trials
Advantages•Data collection, monitoring, and project management in
real time
• Investigators were more likely to rely on transferring data by means of paper-to-computer when they captured clinical data, particularly in trials wherein data sources involved health records in Computer based trials
• Easy to access EDC and enter data
•Reduces trial conducting cost
• Increases patient retention
To be prepared for• Interface to handle data from smart devices to be
integrated in to CDB
•Optimal process and steps yet to be identified
REMOTE PATIENT MONITORING
DECENTRALIZED/ VIRTUAL
TRIAL
REAL WORLD OUTCOMES
DIGITAL THERAPY
PATIENT CENTRICITY
Advancements to be adapted in Clinical database build - mHealth
Conclusion
➢Ultimate Goal is patient safety and to bring drugs faster to patients➢By reducing cost and cycle time in clinical trials➢Adaptations to accomplish our goal
➢Prepare ourselves and the society for the better future
Trials without Animals and patients, and is currently happening.
Organ chips – with instrumentation and software to maintain the same physiological and biochemical environment of the organ system, enables monitoring and easy collection of data.
Thanks!!!
References
• IOT• https://dig.watch/updates/2018-us-fda-approved-106-iot-medical-devices-comparison-99-devices-2017
• mHealth• https://www.mhealth.org/discover-our-difference/clinical-trials
• http://mhealth.amegroups.com/
• Organ Chips• https://www.europeanpharmaceuticalreview.com/news/68630/fda-organs-chips/
• https://www.sciencedirect.com/science/article/pii/B9780124115514000131
• https://www.fda.gov/media/119946/download
Abstract
Methods and tools of collecting data and database build in clinical industry was differing across the ages, between various sponsors and trials, before the introduction of standards. CDASH played a major role in standardizing the eCRF data collection, across different sponsors, with meaningful variable names that could be related further to downstream SDTM and ADaM, helping in traceability and data integrity. Having robust standards across therapeutic areas along with standard machine interpretable protocols, will help in automating the complete Clinical data base build.
AI, Machine learning and NLP will play a major role in simplifying Clinical database build, with only minimal requirements as inputs. Clinical data bases will evolve to accommodate data entered from smart devices and validated using IOT. Use of Organ chips and its evolution in future will demand clinical databases to collect the data that’s exactly needed for analysis, that is the markers of indication and how it responds to the drug. We will discussing on these topics in this presentation.